DISSERTATION of the University of St. Gallen,
School of Management,
Economics, Law, Social Sciences and International Affairs to obtain the title of
Doctor of Philosophy in Management submitted by
Winfried Daun from
Germany
Approved on the application of
Prof. Dr. Torsten Tomczak and
Prof. Dr. Andreas Herrmann
Dissertation no. 3950
Hundt Druck GmbH, Köln 2011

The University of St. Gallen, School of Management, Economics, Law, Social
Sciences and International Affairs hereby consents to the printing of the present dissertation, without hereby expressing any opinion on the views herein expressed.
St. Gallen, October 26, 2011
The President:
Prof. Dr. Thomas Bieger

Meinen Eltern

__________________________________________________
Die vorliegende Arbeit ist während meiner Zeit als externer Doktorand an der
Forschungsstelle für Customer Insight, Universität Sankt Gallen, entstanden.
Parallel hierzu habe ich meine Tätigkeit in Funktionen des Corporate Brand
Management und Marketing bei der UBS AG, Zürich, fortgeführt. An dieser
Stelle möchte ich daher all jenen danken, die zum Gelingen dieser Arbeit beigetragen und mich in den letzten Jahren unterstützt haben.
Zunächst gilt mein Dank meinem Doktorvater, Prof. Dr. Torsten Tomczak, der meinem Promotionsvorhaben von Beginn an mit grossem Wohlwollen und
Interesse begegnet ist. Seine konstruktiven Hinweise und klaren Zielvorgaben waren stets hilfreich und haben ein stringentes Forschungsvorgehen erlaubt.
Ebenso bedanke ich mich bei Herrn Prof. Dr. Andreas Herrmann, der umgehend bereit war, diese Arbeit als Ko-Referent zu betreuen. Auch mein berufliches
Umfeld hat sehr zum Gelingen der Arbeit beigetragen. Insbesondere gilt mein
Dank dabei meinen Vorgesetzten Bernhard Eggli, Jestyn Thirkell-White und
Daniel Zweifel, die meine berufsbegleitende Dissertation vorbehaltlos und mit grossem Freiraum unterstützt haben.
Mein ganz besonderer Dank geht fraglos an meinen Dissertations-Betreuer,
Prof. Dr. Daniel Wentzel. Ohne sein grosses Engagement, seine fachliche und persönliche Unterstützung und seine zahlreichen konkreten Anregungen hätte die Arbeit nicht in dieser Form entstehen können. Unser freundschaftliches und humorvolles Miteinander war und ist eine grosse Bereicherung für mich.
Schliesslich möchte ich meinen Eltern von Herzen danken. Sie haben mich nicht nur in meinem ganzen Werdegang uneingeschränkt unterstützt und gefördert, sondern mir auch jenes Selbstvertrauen und jene Zuversicht mit auf den Weg gegeben, die es zur Bewältigung grosser Herausforderungen braucht.
Ihnen widme ich diese Arbeit.
Zürich, im November 2011 Winfried Daun

The past decade has seen the emergence of a particular sales and distribution model in which companies that manufacture products and sell them directly through proprietary distribution channels open these channels to third parties and often even competitor products. One of the industries to pioneer this model has been financial services, where the approach is referred to as “open architecture” offering. This term describes the fact that banks, for instance, do not only sell their own in-house investment products (such as mutual funds) to clients, but also those of other companies. Providers of an open architecture argue that their clients benefit from a wider choice of products and more objective, client-oriented advice. But even though such an extended offering may come at a price (e.g., loss of market share or the dilution of one’s own product brand image), there is surprisingly little if any research available on how customers actually perceive and react to an open-architecture offering.
In order to address this issue, this dissertation investigates if and how customer reactions are affected when a company sells third-party products next to its inhouse ones. Specifically, the present work examines how customer reactions to an open architecture are influenced by salespeople behavior and attributional thinking. To establish a sound understanding of the specific factors and processes at play, a conceptual model is developed and tested that draws on research into behavioral cues and attribution theories. A qualitative prestudy and two experiments confirm the model’s proposition that a salesperson’s persuasiveness of reasoning, the proactiveness in offering third-party products and the “mix” of in-house and external products have a substantial influence on customer reactions. Moreover, all three cues are substantially mediated by customer-oriented attributions. The present research has important implications for the services and sales literature and it expands our understanding of the interaction among behavioral cues and customer attributions. Moreover, the dissertation contributes a number of managerially relevant propositions on how to ensure that an open-architecture offering is successfully delivered to customers.

Im vergangenen Jahrzehnt liess sich die Entwicklung eines spezifischen
Vertriebsmodells beobachten, in welchem Unternehmen, die ihre eigenen
Produkte über proprietäre Distributionskanäle vertreiben, diese Kanäle für
Dritte, oft sogar für Konkurrenzprodukte öffnen. Eine der Branchen, die ein solches Modell zuerst eingeführt haben, ist die Finanzindustrie, in welcher der
Ansatz als "offene (Produkt-) Architektur" bezeichnet wird. Dieser Begriff beschreibt den Umstand, dass etwa Banken nicht nur ihre hauseigenen
Anlageprodukte (wie z.B. Investmentfonds) an Kunden verkaufen, sondern auch solche von anderen Firmen. Anbieter einer offenen Architektur unterstreichen, dass ihre Kunden von einer breiteren Auswahl an Produkten und einer objektiveren, kundenorientierten Beratung profitieren. Gleichzeitig birgt ein derartig erweitertes Angebot aber auch erhebliche Risiken, wie etwa den
Verlust von Marktanteilen oder die Beschädigung der eigenen
Produktmarke(n). Vor diesem Hintergrund erstaunt es, dass nahezu keine
Forschung zu der Frage vorliegt, wie Kunden eine offene Produktarchitektur wahrnehmen und auf sie reagieren.
Um diese Thematik aufzugreifen, untersucht die vorliegende Dissertation, ob und in welchem Masse Kundenreaktionen davon beeinflusst werden, dass ein
Unternehmen Fremdprodukte parallel zu den Eigenen verkauft. Insbesondere wird die Fragestellung behandelt, ob das Verhalten von Verkäufern und attributionales Denken von Kunden sich darauf auswirken, wie Letztere auf eine offene Architektur reagieren. Ziel der Arbeit ist es, zu einem fundierten
Verständnis der spezifischen Einflussfaktoren und relevanten Prozesse beizutragen. Aus diesem Grund wird ein konzeptioneller Modellansatz entwickelt und getestet, der auf Forschungserkenntnissen in den Bereichen der
Attributionstheorie und der "behavioral cues" (i.e., Verhaltenssignale) aufsetzt.
Eine qualitative Vorstudie und zwei quantitative Experimente bestätigen die
Hypothese, dass die Überzeugungskraft einer Verkaufsperson, ihre
Eigeninitiative im Anbieten von Fremdprodukten und die "Mischung" von

Eigen- und Fremdprodukten einen wesentlichen Einfluss auf Kundenreaktionen haben. Darüber hinaus wird die Wirkung aller drei dieser Verhaltenssignale durch Attributionen von Kundenorientierung mediiert. Die vorliegenden
Forschungsergebnisse haben wichtige Implikationen für die Verkaufs- und
Serviceliteratur, und sie erweitern unser Verständnis der Interaktion zwischen
Verhaltenssignalen und Kundenattributionen. Darüber hinaus gibt die
Dissertation eine Reihe von Management-Empfehlungen, die Unternehmen dabei helfen können, ihren Kunden eine offene Architektur erfolgreich anzubieten.

__________________________________________________
1 INTRODUCTION .......................................................................................... 1
1.1
Problem Orientation ................................................................................. 1
1.2
The Case of "Open Architecture" in Financial Services.......................... 2
1.3
Research Questions and Structure of the Dissertation............................. 7
2 CONCEPTUAL DEVELOPMENT ............................................................. 11
2.1
The Appeal of Variety............................................................................ 11
2.2
Customer Attributions ............................................................................ 13
2.2.1
Theoretical Foundations .................................................................. 13
2.2.2
Antecedents of Attributional Thinking............................................ 19
2.2.2.1
Disconfirmed Expectations ......................................................... 19
2.2.2.2
Other Triggers of Attributional Thinking ................................... 21
2.2.3
Outcomes of Attributional Thinking ............................................... 22
2.3
Customer Reactions to Salesperson Behavior ....................................... 24
2.3.1.1
Influence of Salesperson Cues on Customer Reactions .............. 24
2.3.1.2
Influence of Cues on Customer Attributions ............................... 28
2.3.1.3
Risks of "Priming" Attributions .................................................. 29
2.4
Qualitative Study.................................................................................... 31
2.4.1
Objectives ........................................................................................ 31
2.4.2
Design, Participants and Procedure ................................................. 32
2.4.3
Results.............................................................................................. 32
2.4.3.1
Disconfirmation of Expectations ................................................. 32
2.4.3.2
Attributional Thinking ................................................................. 33
2.4.3.3
Salesperson Behavior .................................................................. 39
2.4.3.4
Summary ...................................................................................... 43
3 HYPOTHESES DEVELOPMENT .............................................................. 45
3.1.1
Impact of Cues on Customer Reactions .......................................... 45
3.1.1.1
Persuasiveness of Reasoning ...................................................... 45
3.1.1.2
Proactiveness .............................................................................. 47
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3.1.1.3
Product Mix ................................................................................. 49
3.1.2
Causal Attributions as Mediator ...................................................... 51
3.1.2.1
Mediation of Persuasiveness of Reasoning ................................ 52
3.1.2.2
Mediation of Proactiveness ......................................................... 52
3.1.2.3
Mediation of Product Mix ........................................................... 53
4 EXPERIMENTAL ANALYSES.................................................................. 54
4.1
Overview of Analyses ............................................................................ 54
4.2
Experiment 1 .......................................................................................... 56
4.2.1
Design, Participants and Procedure ................................................. 56
4.2.2
Manipulation of Independent Variables .......................................... 58
4.2.2.1
Manipulation of Proactiveness ................................................... 59
4.2.2.2
Manipulation of Product Mix ...................................................... 60
4.2.2.3
Manipulation of Persuasiveness ................................................. 61
4.2.3
Selection of Measures...................................................................... 61
4.2.3.1
Dependent Measures ................................................................... 61
4.2.3.2
Manipulation Checks .................................................................. 63
4.2.4
Results.............................................................................................. 64
4.2.4.1
Manipulation Checks and Item Reliability.
................................ 64
4.2.4.2
Hypothesis Testing ...................................................................... 65
4.2.5
Discussion........................................................................................ 80
4.3
Experiment 2 .......................................................................................... 81
4.3.1
Design, Participants and Procedure ................................................. 81
4.3.2
Manipulation of Independent Variables .......................................... 82
4.3.3
Selection of Measures...................................................................... 85
4.3.3.1
Dependent Measures ................................................................... 85
4.3.3.2
Manipulation Checks .................................................................. 85
4.3.4
Results.............................................................................................. 87
4.3.4.1
Manipulation Checks and Item Reliability.
................................ 87
4.3.4.2
Hypotheses Testing ..................................................................... 88
4.3.5
Discussion...................................................................................... 103
4.4
Influence of Customer Expertise.......................................................... 104
II

5 DISCUSSION............................................................................................. 107
5.1
Summary of Results ............................................................................. 107
5.2
Theoretical contribution ....................................................................... 110
5.2.1
Contribution to Literature on Open Product Architectures ........... 110
5.2.2
Contribution to Literature on Cue Influence in Selling................. 111
5.2.3
Contribution to Literature on Customers' Attributional Thinking. 113
5.3
Managerial Implications....................................................................... 114
5.3.1
Delivering an Open-Architecture Offering to Clients ................... 115
5.3.1.1
Sales Force Competence ........................................................... 116
5.3.1.2
Sales Force Incentivization ....................................................... 117
5.3.2
Promoting an Open-Architecture Prior to the Sales Encounter..... 118
5.3.2.1
Advertising Communication ...................................................... 118
5.3.2.2
Brand Positioning ..................................................................... 121
5.3.2.3
Impact of Third-Party Brands ................................................... 122
5.4
Limitations............................................................................................ 123
5.5
Future Research.................................................................................... 126
5.5.1
High versus low Customer Expertise ............................................ 126
5.5.2
Manufacturer Role versus Retailer Role ....................................... 128
5.5.3
Perceived Product and Range Fit................................................... 130
6 REFERENCES ........................................................................................... 132
7 APPENDICES ............................................................................................ 151
7.1
Stimulus Materials Used in Experiment 1 ........................................... 151
7.2
Scripts of the Video Treatments in Experiment 2................................ 158
III

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Figure 1-1: Open-architecture print advertisement (2001) .................................. 4
Figure 1-2: Print Advertisement (2011) encouraging Banking Clients to ask for
Third-Party Funds ............................................................................ 5
Figure 1-3: Structure of the dissertation .......................................................... 10
Figure 2-1: Examples of attributional antecedents and outcomes ..................... 23
Figure 2-2: Relationship of cues, customers’ attributions and reactions .......... 31
Figure 2-3: Conceptual model of the relationship between salesperson cues, customers’ attributions and reactions ............................................ 44
Figure 3-1: Effects of salesperson behavior on customer reactions .................. 50
Figure 3-2: Mediation through customer-oriented attributions ......................... 53
Figure 4-1: Overview of experimental analyses ................................................. 56
Figure 4-2: Structure of online experiment 1 ..................................................... 57
Figure 4-3: Interaction of persuasiveness and proactiveness in experiment 1; customer reactions as dependent variables .................................... 68
Figure 4-3 (cont.): Interaction of persuasiveness and proactiveness in experiment 1; customer reactions as dependent variables ............ 69
Figure 4-4: Interaction of persuasiveness and product mix in experiment 1; customer reactions as dependent variables .................................... 71
Figure 4-4 (cont.): Interaction of persuasiveness and product mix in experiment
1; customer reactions as dependent variables ............................... 72
Figure 4-5: Interaction of persuasiveness and proactiveness / product mix; customer-oriented attributions as dependent variable ................... 75
Figure 4-6: Mediation of persuasiveness through customer-oriented attributions in experiment 1 ............................................................................... 76
Figure 4-7: Mediation of proactiveness through customer-oriented attribution
( under conditions of high persuasiveness) in experiment 1 .......... 78
Figure 4-8: Mediation of product mix through customer-oriented attributions
(under conditions of high persuasiveness) in experiment 1 ........... 80
IV

Figure 4-9: Screenshot of the experimental video clip ....................................... 83
Figure 4-10: Structure of online experiment 2 ................................................... 85
Figure 4-11: Interaction of persuasiveness and proactiveness in experiment 2; customer reactions as dependent variables .................................... 91
Figure 4-11(cont.): Interaction of persuasiveness and proactiveness in experiment 2; customer reactions as dependent variables ............ 92
Figure 4-12: Interaction of persuasiveness and product mix in experiment 2; customer reactions as dependent variables .................................... 94
Figure 4-12 (cont.): Interaction of persuasiveness and product mix in experiment 2; customer reactions as dependent variables ............ 95
Figure 4-13: Interaction of persuasiveness and proactiveness / product mix in experiment 2; customer-oriented attributions as dependent variable
........................................................................................................ 98
Figure 4-14: Mediation of persuasiveness through customer-oriented attributions in experiment 2 ............................................................ 99
Figure 4-15: Mediation of proactiveness through customer-oriented attributions
(under conditions of high persuasiveness) in experiment 2 ......... 101
Figure 4-16: Mediation of product mix through customer-oriented attributions
(under conditions of high persuasiveness) in experiment 2 ......... 103
V

__________________________________________________
Table 2-1: Typology of customer-oriented attributions ....................................... 35
Table 2-2: Typology of suspicion-oriented attributions ...................................... 38
Table 2-3: Typology of salesperson cues ............................................................. 41
Table 2-3: Typology of salesperson cues (cont.) ................................................. 42
Table 4-1: Attribution measurement items .......................................................... 62
Table 4-2: Overview of measures used in experiment 1 ..................................... 63
Table 4-3: Results of multivariate analyses in experiment 1; customer reactions as dependent variable, main effects .................................................. 66
Table 4-4: Mean values for customer reactions as dependent variables in experiment 1, main effects ................................................................. 66
Table 4-5: Results of multivariate analyses in experiment 1; customer reactions as dependent variables, interaction effects ....................................... 67
Table 4-6: Mean values for customer reactions as dependent variables in experiment 1, interaction persuasiveness x proactiveness .............. 68
Table 4-7: Mean values for customer reactions as dependent variables in experiment 1, interaction persuasiveness x product mix ................. 71
Table 4-8: Results of a univariate analysis in experiment 1; customer-oriented attributions as dependent variables .................................................. 73
Table 4-9: Mean values for customer-oriented attributions as dependent variable in experiment 1 .................................................................... 74
Table 4-10: Overview of measures used in experiment 2 ................................... 86
Table 4-11: Results of multivariate analyses in experiment 2; customer reactions as dependent variable, main effects .................................................. 89
Table 4-12: Mean values for customer reactions as dependent variables in experiment 2, main effects ................................................................. 89
Table 4-13: Results of multivariate analyses in experiment 2; customer reactions as dependent variables, interaction effects ....................................... 90
VI

Table 4-14: Mean values for customer reactions as dependent variables in experiment 2, interaction persuasiveness x proactiveness ............... 91
Table 4-15: Mean values for customer reactions as dependent variables in experiment 2, interaction persuasiveness x product mix .................. 94
Table 4-16: Results of a univariate analysis in experiment 2; customer-oriented attributions as dependent variable .................................................... 96
Table 4-17: Mean values for customer-oriented attributions as dependent variables in experiment 2 .................................................................. 97
Table 4-18: Influence of customer expertise on customers’ attributional thinking and reactions ................................................................................... 105
Table 4-19: Influence of customer expertise on customers’ attributional thinking and reactions (mean values) ........................................................... 106
VII

Imagine yourself in the following situation. With the intention to afford yourself a new pair of running shoes, you enter the “Nike” flagship store in your hometown. After a bit of looking around the heavily Nike-branded interiors, you’re addressed by Mark, a friendly and sporty Nike shop assistant. He asks whether he can help you in any way. With a telling glance, you point at the first unmistakable signs of the potbelly you’ve been cultivating over the last years.
You say “Well you know, I used to run quite a lot, but that feels as if it was back in the Middle Ages.” You go on telling him about your firm resolution to revive your old running aspirations. Mark smiles, nods approvingly and you have a conversation about how often and where you’re usually running. He then leads you to a gym-like section of the Nike store and lets you do a quick run on the treadmill. He makes notes on how you set your feet and where you put on the most pressure. After that, Mark leaves you for a couple of minutes to fetch a number of different shoes that might suit your needs. When he returns, he puts four different pairs of running shoes in front of you: Two from Nike, one from
Asics and one from Adidas. He smiles knowingly and says: “The Nike ones here are great allrounders which I'm sure you'll find very comfortable. But you said that you're mostly running on tarmac. That’s why I’ve brought the Asics. Their shock absorption is simply unmatched; you might want to try these out. If they feel a little too heavy, take a look at the Adidas. They're light-weight.” The guy seems to know what he’s talking about. But why, you start wondering, would a
Nike shop assistant try to sell you a competitor product?
This scenario seems odd at first, but it is more common than one would think.
The past two decades have seen the emergence of a particular sales and distribution model in which companies that manufacture products and sell them directly through proprietary distribution channels open these channels to third parties and often even competitor products. The form and extent of a
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collaboration among competitors can vary and its application reaches across industries as diverse as pharmaceuticals (Dussauge and Garrette 1999), groceries
(Garella and Peitz 2007) and automobiles (Dussauge, Garrette, and Mitchell
2004). And while cooperation in research and development would seem to represent a typical and (under certain conditions) intuitively sensible case of competitor alliances (Amaldoss et al. 2000; Hagedoorn, Link, and Vonortas
2000; Hamel, Doz, and Prahalad 1989; Luo, Rindfleisch, and Tse 2007), the joint distribution of products which are in direct competition with one another appears slightly unorthodox. One example are traditional own-label retailers that add well-known manufacturer brands to their assortment (Barr 2009; Garella and Peitz 2007; Sandler 2009): the British retailer “Marks & Spencer”, a UK apparel and food retailer renowned for the upmarket quality and positioning of its products (Sandler 2009) had for decades only been selling food products under its own Marks & Spencer brand label. On November 5, 2009, that changed – when the company announced that, for the first time in 50 years, it would extend its product offering by about 200 “external” product brands, such as Kellogg’s Cornflakes or Coca Cola (Finch 2009). For British consumers, this represented a “radical change” (Finch 2009, p. 16) in their grocery shopping landscape – consequently, the announcement was broadly featured in the news
(e.g., Barr 2009; Felsted 2009; Finch 2009; Sandler 2009). But Marks &
Spencer are far from being the first ones to open their proprietary distribution network to external products. Few industries in fact have seen a more widespread adoption of such a model than financial services, where the approach is referred to as “open architecture” offering.
In financial services, the term “open architecture” describes the fact that banks, for instance, do not only sell their own “in-house” investment products (such as mutual funds) to clients, but also those of other companies (Kelleher 2007;
Skinner 2006). Over the last few years, the up- and downsides of open product
2

architectures in the financial services industry have been the subject of considerable controversy. Advocates of this sales model promote several advantages it is supposed to have. Their “best-of-breed” argument claims that an extended choice of options improves customers’ chances to get the absolutely best product for their needs (Narat 2002; Schulz 2002). In line with this point, banks are said to acknowledge that, however extensive their own product range is, they cannot always offer the best product in every category and lack the required specialized expertise (Kelleher 2007). Articles published in the financial press claim that many financial customers have come to a similar conclusion and therefore expect their banks to offer also third-party products
(Baum 2005; Severin 2002; Skinner 2006). This seems plausible, given media headlines such as “Bank-run funds are poor performers”, as proclaimed by the
Financial Times (Johnson 2011). A second client benefit of an open architecture lies in the promise of greater objectiveness. Pfanner (2002) summarizes this point by saying that “advisers offer funds and other products from their own firms as well as competitors, rather than simply pushing in-house offerings." It is argued that an open-architecture offering de-couples banks’ advisory services from their product ‘factory’ and thus lends more credibility and perceived objectiveness to the investment advice that they offer to clients (Kelleher 2007)
The idea is, in other words, that by offering third-party products, a bank’s client advisor will be perceived as more of a neutral “consultant” rather than a salesperson. Some banks have quite explicitly played on this argument in their advertising: German “Commerzbank”, e.g., ran a poster campaign in their branches that featured the claim “no paternalism, please – third-party funds at
Commerzbank” (Weber 2002). Another German retail bank, Hypovereinsbank, asked in an advertisement “what else is advice about, if it's not independent”
(HypoVereinsbank 2001, see Fig. 1-1). It does also not surprise that arguments in favor of an open architecture are strongly supported by individual fund management companies that are interested in winning banks as distribution channel for their products – the press contributions of Baum (2005) or
Shaugnessy (2009) would seem typical examples.
3

Figure 1-1: Open-architecture print advertisement (2001)
"My bank offers me independent advice. If another bank has a better investment fund, they will tell me. They even sell it to me. Makes you wonder what else advice is about, if it's not independent."
"If another bank has a better investment fund, then my bank will recommend it to me. That means I don't have to waste my time on running to each and every bank in order to find the right investment products. That's convenient. I know much better ways to waste my time."
Source: Hypovereinsbank (2011)
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Figure 1-2 shows a recent print advertising in which Goldman Sachs encourages banking clients to explicitly ask for Goldman Sachs products (GoldmanSachs
2011).
Figure 1-2: Print Advertisement (2011) encouraging Banking Clients to ask for Third-Party Funds
“Talk to your advisor about mutual funds from Goldman Sachs” (Goldman Sachs 2011)
The opposite side of the open-architecture controversy is represented by critics who doubt that the promised advantages of this sales model are genuinely delivered to customers. They argue that the opening of proprietary distribution channels has not resolved a major conflict of interest that is posed by the fact that banks’ in-house products often have the higher margin. “As well intentioned as open architecture is, it is in-house products that produce revenues", claims
Euromoney magazine (Anonymous 2010). Banks are accused of incentivizing the sales of their own products (Rasch 2003) and allocating their clients' money to in-house funds because they do not have to share their margin with a third
5

party (Ross 2010). Bank representatives admit that such a temptation exists
(Speck 2010). If this accusation was justified, the whole value proposition of an open architecture would seem at stake. As Shaugnessy (2009, p. 6) puts it:
"Worse than having too much or even too little choice is for consumers to be presented with the illusion of choice where none really exists."
Opponents of an open architecture also point out that an unlimited offering of similar products can easily overstrain a financial advisor's capabilities and, consequently, be counterproductive to the goal of recommending the right solution (Schulz 2002). The latter problem may be resolved by limiting the amount of third-party providers to a manageable number, an approach often referred to as “guided architecture” (Baum 2005; Schulz 2002; Wiecking 2003).
However, this approach could undermine the original promise of higher objectiveness, as it raises the question of how those ‘preferred suppliers’ are selected and on what basis. Common criticism of guided architectures has pointed out that fund companies "buy" their way into the distribution channel of banks through retrocession agreements, contributions to marketing expenses or revenue sharing (Kelleher 2007; Narat 2002; Schulz 2002; Wiecking 2003).
Consequently, it has been suggested that banks promote those third-party funds that offer the best ‘kickback’ payments.
A number of points can be concluded from the above excurse. First, open architecture is a topic of considerable media interest and a subject of ongoing controversy in the financial services industry. Secondly, as a result of this discussion, both an open architecture’s potential advantages and banks’ persuasion motives may be salient to at least those clients who have a certain investment expertise and interest in these matters.
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The previous sections have argued that there is considerable research available on collaboration among competitors and that open product architectures in industries such as financial services are the subject of controversial discussions.
With this in mind, it astounds that information is very scarce on how customers themselves perceive an open-architecture offering. This is even more surprising if the obvious risks of such a sales model are considered: If a company opens its proprietary distribution channel to competitor products, the negative consequences can be numerous – such as loss of product market share, dilution of own products’ value proposition and erosion of product brand image. Luo et al. (2007) warn that too close alliances with a competitor may put a firm's profitability at risk and lead to the exploitation of its proprietary technologies and marketing capabilities. With regard to open product architectures in financial services, banks’ own fund management companies make a good example of such a negative effect. It seems likely that these in-house product
'factories' will suffer when their exclusive distribution channel is opened to rivals (Gimbel and Major 2002). Specifically, the fund management units of banks are anxious that clients will perceive a third-party offering as an indication that their own products are of inferior quality or variety (Weber
2002). Such potential downsides would need to be outweighed by a strong and positive overall customer reaction, reflected in parameters such as improved client satisfaction or purchasing intention. Consequently, retailers highlight their claim that extending their assortment with top-selling brands offers more convenience to shoppers (Barr 2009; Sandler 2009), and banks promise access to 'best in class' products and more objective investment advice (Kelleher 2007;
Narat 2002; Schulz 2002). But is a favorable customer perception of an open architecture easily achieved, let alone a given? After all, many customers are wary of sales persons’ ulterior motives and suspect that the advice they receive is often biased towards the company’s or its salespeople’s own benefit (Bolton,
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Freixas, and Shapiro 2007; Friestad and Wright 1994; Jonas and Frey 2003;
Krausz and Paroush 2002; Stafford, Leigh, and Martin 1995).
The purpose of this dissertation is to investigate if and how customer reactions are affected when a company that has up to some point in time only sold its inhouse products starts to sell third-party, even competitor products. It would appear that the present research is the first to empirically investigate such effects of an open-architecture sales model. Specifically, the dissertation focuses on the influence that salespeople behavior has on customer reactions to an open architecture. To establish a sound understanding of the relevant effects, a conceptual model is developed and tested that draws on marketing and services research into behavioral cues and attributional thinking. In order to enhance the theoretical and practical scope, this model will also incorporate both moderating and mediating effects, specifying interactions among the independent variables and describing the circumstances under which the resulting customer reactions are stronger or weaker. In short, this dissertation aims to answer the following research questions:
1.
How do customers react towards an open-architecture offering? Are there specific behavioral cues that salespeople provide during the sales episode that will influence these customer reactions?
2.
To what extent, if any, is such an influence subject to interaction effects among the different cues?
3.
Lastly, is the relationship between salesperson cues and customer reactions mediated by attributions that customers generate in order to explain the salesperson's behavior?
This dissertation is structured into the following chapters: Chapter 1 presented an introduction into the topic in general and provided a detailed insight into the specific context of open product architectures in financial services, as this industry will provide the background for empirical research. Chapter 2 develops
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a conceptual model based on existing research into expectancy disconfirmation, attributional thinking and the effects of behavioral cues. In addition, this chapter covers the design, procedure and results of a qualitative pre-study undertaken to substantiate the model's basic assumptions. Chapter 3 then brings forward a number of research hypotheses based on the conceptual model. The validity of these hypotheses has been empirically examined in form of two experimental studies that share the same independent and dependent variables but which employ different experimental stimuli and sample populations in order to enhance their practical and theoretical scope. The design, procedure and results of these quantitative studies are thoroughly documented in Chapter 4. Finally,
Chapter 5 presents a summary of the findings, addresses their implications and limitations, and identifies potential areas of future research. The structure of the dissertation is outlined in Figure 1-3.
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Figure 1-3: Structure of the dissertation
CHAPTER 1: INTRODUCTION
Problem orientation of the dissertation, context information on open product architecture in financial services and formulation of research questions pp. 1 - 10
CHAPTER 2: CONCEPTUAL DEVELOPMENT
Review of theoretical foundations, development of a conceptual model and presentation of supportive findings from a qualitative pre-study pp. 11 - 44
CHAPTER 3: HYPOTHESES DEVELOPMENT
Formulation of research hypotheses to be empirically tested in two experimental studies pp. 45 - 53
CHAPTER 4: EXPERIMENTAL ANALYSES
Documentation of the design and the results of two experimental studies pp. 54 - 106
CHAPTER 5: DISCUSSION
Detailed discussion of the results, implications, and limitations of the dissertation and identification of future research pp. 107 - 131
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This chapter will build the conceptual foundations that ultimately serve as a basis for the hypotheses formulated in Chapter 3. It comprises four distinct sections. In the first one, a short review on the appeal of variety aims to explain why, prior to all other considerations, many customers may welcome the idea of an open product architecture. In the next two sections, factors that may influence their actual reactions towards an open architecture are explored by reviewing extant literature into expectancy disconfirmation, attributional thinking and the effects of salesperson behavior. Hereafter, key findings are summarized and reflected in form of a high-level conceptual model. The fourth and final part presents results from a qualitative pre-study that substantiate the model’s assumptions and provide relevant details for the design of subsequent experimental studies.
There are a number of reasons why the fact that a company opens its proprietary distribution channel to competitor products may have a positive influence on customer reactions. First of all, the introduction of an open product architecture offers customers a greater variety of options to choose from. Over the last few decades, a substantial amount of research has discussed the advantages and disadvantages of assortment size and variety from a customer’s perspective.
Many of these studies suggest that greater variety of products and services positively affects customer satisfaction (Broniarczyk, Hoyer, and McAlister
1998; Chernev 2003; Hoch, Bradlow, and Wansink 1999; Kahn and Lehmann
1991) and leads to more positive evaluations of the assortment (Oppewal and
Koelemeijer 2005). One of the main reasons brought forward on this argument is that a wider range of options provides a higher chance of finding a product perfectly fitting one's own requirements. Consequently, a greater assortment
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variety would promise a reduction of perceived risk (Simonson 1990), reduce the need for alternative-seeking and save time and effort. A second line of research follows the premise that many customers display a variety-seeking behavior, actively appreciating the multitude of consumption choices that an extensive assortment can offer (Chernev and McAllister 2005; Huffman and
Kahn 1998). Building on Kahn and Lehmann’s findings (1998), Hoch,
Brandlow and Wansink (1999) observe that the desire for variety may be even greater when customers are uncertain what product they need and, consequently, would prefer. They argue that in such a context, a wide variety promises a greater flexibility of choice and the chance to form an educated opinion about the scope of possible solutions. This seems plausible for many product categories that present the customer with a virtually unmanageable amount of different, sometimes very complex options, such as investment products, insurance policies or personalized travel packages. Against this background, it is hardly surprising that many of the above arguments find themselves in the reasoning of companies advocating an open product architecture: while retailers allude to the convenience of ‘one-stop-shopping’ (Barr 2009), financial services institutions highlight customers’ access to ‘best-in-class’ products (Baum 2005;
Narat 2002).
On the other hand, it has been shown that the positive effect of greater variety does apply far from everywhere, as too much variety often leads to confusion on the customer's side and inefficient decision processes (Boatwright and Nunes
2001; Chernev 2006; Greenleaf and Lehmann 1995; Iyengar and Lepper 2000).
Consumers quite plainly 'do not see the woods for the trees'. Such effects, however, are likely to be ameliorated wherever some form of sales or service agent acts as intermediate between the client and an overwhelmingly large choice of products. As long as a bank client, for example, trusts his or her financial advisor to pre-select the "right" products and thereby significantly reduce the complexity of choice, a greater choice of products is likely to keep its appeal. Chernev (2006) argues that customers whose decision process focuses
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on selecting the right overall assortment rather than an individual product
(because that, in the banking case, would be pre-selected by the advisor) will prefer larger assortments. In addition, Kahn (1995, p. 139) notes: "Variety may also be an important consideration when a consumer chooses a portfolio of options at one time. For example, when choosing financial services or investments, consumers may choose a diverse portfolio." The latter arguments are partially based on the premise that intermediates, such as financial advisors or specialized travel agents, truly use the benefits of their firm’s wider assortment to their clients’ best interest. How salespeople and advisors would need to behave in order to give their customers just that confidence is one of the fundamental questions at the core of this dissertation thesis.
It can be concluded that many customers are likely to appreciate the greater assortment variety that an open-architecture offers to them – at least in those circumstances where their product or service selection is assisted by an advisor or salesperson and as long as they are confident that the salesperson’s preselection or recommendation makes use of the additional variety. Overall, the promise of an open architecture may therefore tend to instigate positive rather than negative or no customer reactions. However, clients will rarely be able to form an opinion about a product or service offering without the influence of any contextual factors. Instead, it can be assumed that existing beliefs and expectations that the individual holds, as well as the specific circumstances of a sales encounter, will strongly affect a customer’s attitude formation. These factors will be discussed in more detail in the following two sections.
2.2.1
Theoretical Foundations
Imagine a scenario similar to the one used in the introduction chapter: The customer of a high street bank wants to invest money in form of mutual funds.
His or her bank has up to now been selling only products from its own
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investment fund label. But this time, the bank’s client advisor recommends also funds from other parties, even competitors. What reaction will the customer show: surprise, delight or suspicion? First of all, it seems highly plausible that such a behavior would seem counter-intuitive to many clients. Why, after all, would someone offer a competitor’s product instead of his or her own? What is the reason driving such a behavior? In case that no immediate or obvious explanation is at hand, customers may start looking for a cause.
For many decades, behavioral theory has investigated individuals’ attempts to understand the events and behaviors that they observe – especially those that have no obvious explanation. One of the most prominent and enduring theoretical frameworks is provided by attribution theory, whose foundations have been contributed by Heider (1958), Jones and Davis (1965), Kelley (1967,
1973) and Weiner (1985a, b). In line with many works of attribution research, this dissertation will use the term 'attribution theory' (cf. Folkes 1988; Wimer and Kelley 1982), even though some researchers have pointed out that
'attribution theories' would be more accurate, since the concept is a framework of different related theories (Kelley and Michela 1980; Mizerski, Golden, and
Kernan 1979).
The term attribution refers to the cognitive process that individuals undertake to arrive at causal explanations of perceived events (Kelley 1973). Following
Trommsdorff (2009), attribution theory attempts to explain how individuals infer potential reasons and motives behind observed events and behaviors, but it also argues how different conclusions subsequently affect the attributing person's behavior. In line with this definition, Niemeyer (1993) states that attribution theory deals with effects that are consciously observed by someone and influence that person's perceptions and actions. The fundamental assumption of this branch of social theories is that humans strive to understand the world they live in and the events they witness – as such, attribution theory attempts to explain the circumstances in which people ask 'why' questions
(Kelley 1973).
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Going back to the banking example, attribution theory postulates that a client may perceive the offering of competitor products by a bank's advisor as caused by very different reasons, e.g., a) I am a very demanding and knowledgeable client – the advisor knows I’d never buy only his own bank’s products b) The advisor wants to provide me with the best solution for my needs c) The advisor is a neutral source of information, and his advice is not impaired by the fact that he is working for a bank d) The bank has little competence in this specific investment category and therefore it resorts to products from third parties e) The bank gains a high profit margin on external products and therefore "pushes" them f) The advisor makes a better commission on the expensive thirdparty products g) A newspaper on the advisor’s desk features a large advertisement for a competitor’s award-winning investment funds h) Open architecture is in discussion all over the financial press and therefore banks consider it a “must” i) The bank’s own products are of poor quality
This short list of potential attributions helps to illustrate several fundamental predications of attribution theory. Firstly, when investigating the potential cause of an event, consumers can search in different places, commonly termed locus of causality (Weiner 1985a): They can perceive themselves (a) or others (b, c, d, e, f) as being the cause. They may attribute the event to the circumstances of a specific situation (g) or to the subject of the event itself (h, i) (Kelley 1973;
Kroeber-Riel 2003; Niemeyer 1993). In addition to locus of causality, different works of research have identified a number of dimensions that apply to the concept of attribution, including those that Weiner (1985a) has summarized as the controllability and stability of a perceived event (also, Folkes 1984).
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Controllability refers to the degree to which the outcome of an event is perceived as being within the control of the entity that caused it (Weiner 1985a).
A customer who waits in a line at the checkout may perceive the checker as the locus of causality, but acknowledge that the control of the event lies within the hands of the store manager who refuses to open more checkouts. Similarly, the offering of third-party funds to a banking client may ultimately not be decided by the financial advisor, but be influenced by the bank or even different external fund companies. A more sophisticated judgment like this, however, would require the customer to be aware of the multiple agents involved in the
“background” of an event: In this context, Folkes (1988) points out that, in spite of a wide range of responsible entities along the supply chain, a customer may just distinguish between two causal agents – him or herself on the one side and the salesperson on the other side. In light of these examples, it does not surprise that Folkes (1984) has demonstrated that "locus of causality" and
"controllability" are quite similar, reporting a correlation coefficient of .94.
Consequently, Tsiros, Mittal and Ross (2004) merge the two dimensions and simply talk of "responsibility".
Finally, individuals form an opinion about the stability of the cause that they have attributed to a certain event, i.e., they assume that the causality behind a certain event is a common, frequent one or, rather, a one-time, coincidental occurrence (Folkes 1984; Weiner 1985a). In addition to Weiner's (1985a) main dimensions of locus of causality, controllability and stability, empirical research has yielded other attribution dimensions, such as those forwarded by Wimer and
Kelley (1982), which included simple vs. complex or good vs. bad. With regard to this “valence” of attributions, the present dissertation follows DeCarlo’s broad distinction between “customer-oriented” and “suspicion-oriented” attributions (DeCarlo 2005, p. 239). Customer-oriented attributions presume the influence of altruistic motives, as in the above examples b) or c). "Suspicionoriented" attributions, such as e) or f), reflect different self-seeking motives a customer might suspect behind a salesperson’s message or behavior (DeCarlo
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2005). As the term would suggest, such attributions are frequently triggered by suspicion, a “dynamic state in which the individual actively entertains multiple, plausibly rival hypotheses about the motives or genuineness of a person's behavior.” (Fein 1996, p. 1165)
The examples a) to i) help to illustrate more than just the different dimensions of attributional thinking. They also highlight the fact that in search of possible causes, customers are likely to develop more than one explanation. Individuals can attribute a certain event to several causes at a time, depending on its perceived significance – the more crucial an event appears to the observer, the more he or she will be inclined to presume multiple reasons (Cunningham and
Kelley 1975). Not all of these reasons may have the same impact on the attitudeformation process (Kelley 1973), but may undergo weighting instead. Coming back to the banking examples, it seems highly plausible that a customer who is offered competitor products will entertain multiple assumptions about the motives behind such an offering. He or she may perceive an advisor’s recommendation of competitor products as caused by a combination of ulterior motives on the bank’s and the advisor’s side (e + f), or an interplay of benevolent advisor behavior and the customer’s own skill (a + b). Moreover, the different pieces of information that a customer relies on in forming an explanation may not always be conclusive. Burnkrant (1975) has argued that consumers in the search for the cause of an observed event are likely to evaluate several cues that are available rather than only one. However, these cues may deliver an inconsistent message. A financial advisor, for instance, may 1) listen carefully to the client, 2) ask thoughtful questions, 3) communicate in a friendly and open manner, but 4) still seem very reluctant to offer any other investment funds than his own firm's. To which causes will a client attribute the advisor's motives in this scenario – is the advisor acting in the client's best interest or does he want to maximise his own profit? As attribution theory dictates, customers will favour the inference that seems to be supported by a majority of cues
(Burnkrant 1975).
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The cognitive processes and dilemmas that attribution theory suggests for a scenario like the one in which a client is faced with an open product architecture seem quite plausible. The fact that it describes intuitively understandable human behavior and reactions may be one of the reasons why attribution theory has proven to be one of the most resilient and often-used concepts in research on consumer behavior. As Folkes (1988, p. 548) stated in one of the most comprehensive reviews of marketing-related attribution research: "Attribution theory is a rich and well developed approach that has a great deal to say about a wide range of consumer behavior issues." Consequently, the framework affects many different disciplines of marketing, including pricing, distribution and communications (Trommsdorff 2009). While many of the predications of attribution theory are intuitively plausible and have been corroborated by an extensive body of research, the concept has also been criticized. Fletcher (1984), for instance, has argued that the framework represented little but common sense and therefore contributed few insights of scientific relevance. Weiner’s (1985a) response was that while individual dimensions and relationships forwarded by attribution theory may be seen as commonly shared knowledge, it is their connection and integration within one framework that represents the value of the theory: “What is not shared knowledge, however, is the conceptual analysis – the linking of various "understood" empirical relations and the use of similar principles to explain a vast array of phenotypic observations [...] It is this systemization, that is, the higher order relations between associations realized in everyday life, that represents much of this attributional contribution” (Weiner
1985a, p. 570). In addition, some researchers have criticized that attribution theory overestimated the cognitive activities of consumers (Herkner 1981; cited in Kroeber-Riel 2003): They argue that many consumer perceptions and predispositions are not the result of logical and differentiating reflection, as implied by the concept. In response to this accusation, Wong and Weiner (1981) have pointed out that attributional research does not claim that individuals are constantly engaged in attributional thinking or that they consciously strive to find an explanation for everything they observe. Rather, attributions are
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instigated in case that existing schemata or convictions fail to explain the observation in a satisfying manner (Wong and Weiner 1981). In order to shed further light on this matter, the following chapter will review the antecedents of attributional thinking.
2.2.2
Antecedents of Attributional Thinking
2.2.2.1
Disconfirmed Expectations
At the beginning of the previous chapter, it was argued that a salesperson’s offering of competitor products will lead a customer to wonder about the reasons for such a behavior. However, such a reaction is likely to depend on the expectations that the customer brings into the sales situation. If the customer is used to being recommended both own-label and external products, he may feel little urge to question the motives behind the offering. But if he has rarely (if ever) been offered third-party products or is generally unaware that such products are available through the bank’s advisor, an open-architecture offering will not be in line with what the customer has come to expect. On the contrary, the client´s original conviction may have been that the salesperson was keen on selling his or her own firm’s products – a common allegation, for instance, in financial services (Bender 2009; Bolton et al. 2007). Hence, the offer of competitor products would seem counter-intuitive to many clients in a similar situation, and instigate them to ‘wonder why’.
Over the last four decades, a considerable body of research has forwarded the close relationship between the disconfirmation of individuals’ expectations and their likelihood to engage in attributional thinking (Hastie 1984; Hunt, Domzal, and Kernan 1982; Lau and Russell 1980; McPeek and Edwards 1975; Smith and
Hunt 1987; Sujan, Bettman, and Sujan 1986; Wong and Weiner 1981). Some of these works have investigated the role of an individual’s behavior as the cause of the disconfirmation, another line of research has investigated the specific
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impact of communication that violates a recipient’s expectations. The first group would include the contribution of Pyszczynski and Greenberg (1981) who showed that unexpected behavior leads to more thorough attributional processing, a result which is also supported by Hastie (1984). Similarly, Lau and
Russel (1980) found that unexpected results of sports events triggered a greater number of causal attributions than expected ones. Niemeyer (1993) argues that a salesperson may actively trigger attributions by surprising a customer with a positive behavior that is incongruent with what the customer had been expecting. With regard to communication, Hunt, Domzal, Kernan (1982) argue that if a sales message contains negative information about a product, this presumably contradicts customers’ expectations. In response, recipients are more likely to attribute actual product attributes as the message’s cause and hence perceive it as more credible. Smith and Hunt (1987) arrived at similar conclusions. McPeek (1975) also confirmed that unexpected messages increase the perceived honesty and sincerity of sources, but only suggested that attributions may play a role. Consequently, marketing researchers have investigated how the relationship of incongruent messages and favorable attributions could be exploited in consumer goods advertising (cf. Golden 1977;
Golden and Alpert 1987; Hunt et al. 1982; Pechmann 1992).
Whether expectancy disconfirmation triggers “good” or “bad” attributions is likely to depend on whether the disconfirmation itself is a pleasant or unpleasant surprise. It seems only natural that, for example, consumers generate positive attributions if the waiting time at a supermarket checkout is shorter than expected, and negative ones if they have to wait longer than they had thought they would (Tom and Lucey 1995). In summary, a considerable amount of different studies has shown that the disconfirmation of expectations is one of the strongest instigators of customers’ attributional thinking.
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2.2.2.2
Other Triggers of Attributional Thinking
Beyond expectancy disconfirmation, attribution research has identified a number of other antecedents, some of which could bear a relevance in the context of an open-architecture product offering. Kelley and Michela (1980) have summarized attributional antecedents under the broad categories of information, beliefs and motivation. On a more concrete level, Wong and Weiner (1981) for instance argue that novel and unknown or stressful events may trigger attributional thinking, as could a sentiment of frustration. Internal factors like the last one – antecedents that lie within the attributing person – include motivations or predispositions of the observer, such as suspicion (Fein 1996), the desire for control of one's environment (Burger and Hemans 1988) or high involvement
(Niemeyer 1993). Put simply, involvement can be defined as the feeling of personal engagement or interest a consumer has in a stimulus (Greenwald and
Leavitt 1984; Zaichkowsky 1985). For the present context of financial services,
Aldlaigan and Buttle (2001) have demonstrated that financial customers' usage of investment services is a high involvement activity – unlike, for instance, the usage of a cheque book – , even though involvement may vary across different types of customers (Howcroft, Hamilton, and Hewer 2007). Other findings from attribution research would seem to support the basic notion that financial investments provide a context for attributional thinking: in case that the investment is of considerable size and potential losses would substantially affect the investor's financial situation, making the right investment choice is likely to be an activity of great personal importance. It is such stressful or simply highimportance events and conditions of "cognitive unrest" (Lichtenstein and
Bearden 1986, p. 295) that have also been claimed to be antecedents of attributional thinking (Lichtenstein and Bearden 1986; Wong and Weiner 1981).
To conclude, the confrontation with an open-architecture offering may not only instigate customer attributions because it disconfirms expectancies. Other factors such as stress or frustration, suspicion, desire for control or high involvement may also increase a customer’s perceived need to search for causes.
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2.2.3
Outcomes of Attributional Thinking
As the previous sections outline, individuals use attributions not only to identify
“who or what is responsible” for a certain event (locus of causality, controllability) and whether it is likely to reoccur (stability). Attributions also affect the attributing person’s behavior, triggering a response (Niemeyer 1993;
Trommsdorff 2009). Such consequences seem obvious: after all, the valence of attributions can differ significantly, ranging from positive conclusions about the benevolence, sincerity or honesty of a communicator or the quality of a product to negative assumptions about incompetence, ulterior motives or flawed design.
A vast body of research has demonstrated the impact of attributional thinking on customer behavior, with service failure and recovery being one of the most frequent subjects (cf. Casado Diaz and Más Ruíz 2002; Chebat et al. 1995; Curren and Folkes 1987; Folkes, Koletsky, and Graham 1987; Hess Jr, Ganesan, and
Klein 2003; Iglesias 2009; Swanson and Kelley 2001). Folkes, Koletsky and
Graham (1987) have demonstrated that consumers’ attributions affect their propensity to complain and their likelihood of repurchasing a product. Chebat et al (1995) show a similar relationship between attributions and customer perceptions of service quality. The importance of “who is responsible” is underlined by findings of Curren and Folkes (1987) who found evidence that locus of causality and controllability (i.e., responsibility) affect consumer’s willingness to communicate – independent of attribution valence. For negative as much as positive attributions, customers were more likely to talk about a product when they perceived the seller as responsible for the observed event
(product failure vs. product success). The effect of control attributions on repurchasing and complaining intention is also supported by Casado and Mas
(2002). Swanson and Kelley (2001) have shown that buyer’s likelihood to provide word-of-mouth increases when the attributed cause is perceived as stable . Iglesias (2009) argues that attributions have both a direct and indirect impact on customer satisfaction. For the case of service failure, the author showed that customers who perceived the service provider as responsible
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displayed lower levels of satisfaction and lower evaluations of the service's overall quality and less perceptions of the service's individual attributes. Similar results were found by Tom and Lucey (1995) who demonstrated that in case of a waiting time that is shorter than expected, consumers are more satisfied when the cause of the shorter waiting is perceived as under the store's control or when the cause was perceived as stable. The effect was mirrored in case of negative confirmation, where perceived control and stability led to greater dissatisfaction.
In summary, there is ample evidence that attributional thinking affects customer reactions such as satisfaction, willingness to communicate, propensity to complain and intention to (re-)purchase. Moreover, they determine expectations towards further action – if a firm is seen as responsible for a product failure, it is expected to apologize (Folkes 1984) or to provide a certain reparation (Hess Jr et al. 2003).
More than three decades of scientific contributions underline how relevant learnings from attribution research are for marketing and sales, and especially for personal selling encounters (Johnson 2006). They bear fundamental implications for salesperson-consumer encounters (Niemeyer 1993;
Trommsdorff 2009) as much as for negotiations between professional sellers and buyers (Folkes 1988). Figure 2-1 summarizes the relationships outlined in chapter 2.2.
Figure 2-1: Examples of attributional antecedents and outcomes 4
Antecedents Attributions Consequences
• Benevolence
• Credibility
• Competence
• Ulterior motives
• Satisfaction
• Intention to purchase
• Intention to recommend
• Disconfirmed expectations
• Suspicion
• High involvement
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Up to this point, it has been argued that the very existence of an open product architecture may trigger customer attributions and, subsequently, reactions:
Firstly, because it carries the appeal of variety. Secondly, because it disconfirms customers’ expectations of how firms advocate their own products, which leads to favorable attributions. These in turn influence customer behavior. However, how customers react to a certain product or service offering is also likely to depend on the agent that “connects” a customer with an offering. The following chapter will therefore review to what extent salesperson behavior can influence customer reactions to an open product architecture.
2.3.1.1
Influence of Salesperson Cues on Customer Reactions
In many sales scenarios in which a customer seeks a salesperson's help in picking from a large, often intransparent range of options (e.g., in financial investment planning, travel booking or insurance policies) the salesperson can act as a gatekeeper between a company's overall product offering and the customer: the salesperson has an overview of the available options that the customer often doesn't have. In the case of a bank's open architecture, it may be entirely up to the bank's financial advisors to offer third-party products to their clients – or not.
And even if they did, there would be many ways of positioning the competitor products in a way that the bank's own products seemed preferable. Many customers are aware of this dilemma and the different tactics that salespeople apply to persuade them of a certain choice (Campbell and Kirmani 2000; Friestad and
Wright 1994; Niemeyer 1993; Trommsdorff 2009). One of their response strategies is to look for certain cues in a salesperson's behavior that will reveal his or her “true intentions”, or at least indicate whether the salesperson strives to act in the customer’s best interest or is merely focused on selling.
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Researchers have investigated an extensive range of different salesperson behaviors in order to measure salespeople's customer- and selling-oriented behavior, both from a seller's perspective (Saxe and Weitz 1982) and a customer perspective (Michaels and Day 1985; Wood et al. 2008b). Salesperson behaviors such as accurately describing the features and benefits of a product and providing all the information a customer has asked for reflects customerorientation (Michaels and Day 1985). An insurance agent, for instance, who gave a very clear overview of several life insurance products, highlighting their different fee structures and payout conditions and explaining their investment approaches, would be perceived as customer-oriented. Overselling a product or stretching the truth indicates a strong seller-orientation (Michaels and Day
1985). In the life insurance example, the agent could display such behavior by describing a mediocre, expensive insurance policy as "great" or downplaying the financial risks inherent in a life insurance whose capital was mainly invested in stocks. In a similar vein, Wood, Boles and Babin (2008a) have explored “good” and “bad” cues that are perceived by customers. Beyond those related to a salesperson’s behavior , they list other aspects of a firm’s or a salesperson’s appearance that affect a customer’s opinion of a business – such as the cleanliness and professional “look” of the business, and the gender, age, race and outfit of the sales agent.
The cues that customers perceive often have a consequence. Salesperson behavior that reflects customer-orientation or selling-orientation has been shown to affect customer satisfaction with the salesperson, but also with the product and its manufacturer (Goff et al. 1997). Such a ‘blanket judgment’ would seem in line with findings from Crosby, Evans and Cowles (1990), who claim that consumers often do not distinguish between salesperson and selling firm. Over the last 30 years, many studies have highlighted how strongly cues affect critical aspects of a selling encounter. They demonstrate a substantial effect of cues on, e.g., the formation of trust (Doney and Cannon 1997; Hawes, Mast, and Swan
1989; Kennedy, Ferrell, and LeClair 2001; Sirdeshmukh, Singh, and Sabol
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2002; Swan, Bowers, and Richardson 1999; Wood et al. 2008a; Wood et al.
2008b), the attribution of credibility (Pornpitakpan 2004), the persuasiveness of sales messages (Fein 1996; Sparks and Areni 2002) and selling effectiveness in general (Weitz 1981; Weitz, Sujan, and Sujan 1986). Among these contributions, a rather common finding has been that the trustworthiness of a salesperson is, to some extent, inferred from cues that indicate likeability and competence (cf. Hawes et al. 1989; Swan et al. 1999; Wood et al. 2008a).
According to Buda and Zhang (2000), cues that convey credibility will lead consumers to take a sales message rather at face value than counter argue it: A shop assistant in a golf equipment store, for instance, is more likely to persuade his customer of a specific club’s superiority if he turns out to be a passionate, experienced golfer himself. Importantly, a considerable body of research suggests that buyers assess certain characteristics of salespeople and make inferences about the ulterior motives driving their behavior (e.g. Campbell and
Kirmani 2000; Fein 1996; Wood et al. 2008b). In other words, salesperson cues may represent a critical input for attributional thinking; an implication that will bear further relevance in the development of the conceptual model.
Many of the above studies have categorized cues in slightly or substantially different ways. Based on an extensive review of the variables that drive credibility and message persuasiveness, Pornpitakpan (2004) makes a distinction between "source variables" such as physical attractiveness or gender and
"message variables" such as argument quality or message discrepancy. In their analysis of the different communication variables that drive message persuasiveness, Areni and Cox (1995) distinguish between arguments and cues. According to their definition, an argument is "a communication element that constitutes part of the fundamental message, and cues […] refer to a communication element that creates a setting for the message" (Areni and Cox 1995, p. 198).
The two authors admit that the clear separation of these concepts is difficult, since arguments and cues can take on multiple roles in the persuasion process.
Swan, Bowders and Richardson (1999) argue that determinants of trust can be
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separated into “direct” and “indirect” cues, the former representing trustinducing behaviors (e.g., revealing one’s own commission fees), the others sales person features (e.g., likability, outer appearance) that customers use to attribute trustworthiness. Their findings suggest that direct cues tend to have a stronger effect, especially over time. While some of the above cited studies may differ in their categorization of cues, they all support the idea that cues have a substantial impact on customer reactions.
In addition to the different qualities or categories of cues that a client may perceive, his or her judgment is likely to be formed not only on impressions gained during a specific sales encounter. It will often be influenced by existing beliefs or other factors (such as word-of-mouth) accessible to the client in advance of his actual encounter with the sales agent. The present research, however, focuses on direct, behavioral, and “in situ” cues, e.g., the ways in which a salesperson acts when presenting and arguing during a sales episode. This is done for several reasons. Firstly, it has been argued at the beginning of this section that a customer’s choice of products often depends on the pre-selection done by the salesperson – especially, where the complexity of an open product architecture is given. Being aware of this dependency, it seems highly plausible that a customer will carefully monitor the salesperson’s behavior, draw inferences on the agent’s intentions and determine an appropriate reaction. Secondly, it seems fair to assume that “in situ” cues (though not all) are more easily manageable by the company and its sales force than some of those observed outside of the sales encounter – the advice given by a family member, for instance. Hence, the focus on “in situ” cues promises results of greater managerial relevance. Lastly, a focus on certain explicit behaviors allows for the investigation of cues that are relatively specific to the scenario of an open-architecture offering. As such, they may deliver more novel and theoretically relevant insights than indirect cues such as outer appearance or likeability of a salesperson.
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2.3.1.2
Influence of Cues on Customer Attributions
The above chapter forwards the idea that salesperson cues have an influence on customer reactions. This effect may not (always) be of a direct nature, though.
Rather, salesperson cues will often provide an input on which customers draw causal inferences about the motives behind a certain behavior (Campbell and
Kirmani 2000; Fein 1996; Wood et al. 2008b) which then affect their reactions.
Given the earlier insights into the function of customer attributions, their causes and effects, it seems very plausible that attributional thinking forms part of the cognitive processes that take place between the observation of a cue and the instigation of a reaction. In other words, salesperson behavior may trigger attributions or at least influence which attributional causes an individual perceives as likely.
Consumers have been found to evaluate a multitude of available cues when searching for the cause of an observed event (Burnkrant 1975). Those will frequently include aspects of a salesperson’s behavior, but are not limited to it.
Su and Tippins (1998) argue for the case of product failure, that customers' attributions on "who is to blame" within the supply chain (manufacturer or retailer) are significantly affected by brand visibility. They conclude from their findings that well-known brands are less likely to be attributed as the original cause behind a product failure. In other words, the brand acts as a powerful cue that refutes the notion that the product failure was the manufacturer’s fault.
Wood, Boles and Babin (2008a) demonstrate that, in order to establish a salesperson's credibility, a client will draw on a number of cues gained from both the salesperson and the selling environment. They suggest that attributions mediate the relationship between cues and customer attitude: "Given that cues influence a respondent's attributions about trust-building characteristics, study results indicate that these assessments initiate a process leading to trust of the salesperson and the firm" (Wood et al. 2008a, p. 34). DeCarlo (2005) has explicitly shown that such a mediating effect exists. In addition, Rose and
Dickson (1988, p. 106) argue for negotiations between professional sellers and
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buyers that their research findings “are certainly supportive of the proposition that attributions mediate the effect of one bargainer's behavior on the behavior of his bargaining partner”.
The idea that certain cues instigate attributions which in turn affect customers’ behavior has led researchers to suggest that specific cues should be provided deliberately in order to elicit a desired consumer behavior. Trommsdorff (2009, p. 291) argues that “it is obvious for personal selling to use attributional aids in order to shape the perception of a salesperson as competent, altruistic, solutionoriented and credible.” In light of the negative effects that attributional thinking can potentially have on customers' evaluation of a service encounter, Hui and
Toffoli (2002, p. 1841) conclude that "service managers should try to encourage customers to form attributions in positive service encounters and to inhibit […] or manipulate consumer attributions (against the natural attribution biases) in negative service encounters." Rose and Dickson (1987) have shown how cues affect impression formation and attribution in a bargaining context. They argue that “an understanding of the factors capable of stimulating inference-making in bargaining might improve bargaining skills by suggesting tactics that would tend to elicit certain types of attributions and impressions favorable to the tactician”
(Rose and Dickson 1987, p. 382).
2.3.1.3
Risks of "Priming" Attributions
Promising as they seem, the above described manipulations may neither be easy to bring about nor free of risk. Deliberately providing a certain cue in order to activate a consumer's use of specific (and favorable) decision heuristics is commonly termed as “priming”. Priming, in the definition of Stafford (1996, p.
37), is based on the idea that sellers can "surreptitiously induce favorable decision rules in buyers in order to further the sale". The effectiveness of manipulating consumer through priming social information cues is likely to be limited, though. Stafford (1996) argues that any sort of ‘subtle’ priming must be
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unreliable since its effect will strongly depend on the seller's communication and presentation skills as well as on the buyer's wariness and persuasion knowledge.
Obtrusive priming, on the other hand, carries the substantial risk that consumers detect the deliberate persuasion attempt behind it and their perception is affected negatively. This thesis is backed by persuasion research from Friestad and
Wright (1994), who reason that many customers are well aware of persuasion attempts and will counteract against them. Typical reactions will include active counterarguing, bolstering one’s own initial attitudes, derogating the message source, avoiding incongruent information or blaming the experienced anger or irritation on the message or its source (Tormala and Petty 2004). In addition to the risks of obtrusive priming, Sharma (1996) has questioned the transferability of priming concepts to sales situations in general, arguing that situational parameters in a sales context are critically different from the setup of sociopsychological experiments.
Up to this point, chapter 2 has reviewed several streams of research that contribute to building a conceptual model for this dissertation. A first conclusion is that an open-architecture offering is likely to be perceived favorably by clients. It promises greater choice and variety, and with that, a higher likelihood of finding the right product. Moreover, a firm that offers competitor products side by side to its own products seems to act against the obvious selling motives that many customers are wary of. Their expectations are, consequently, disconfirmed in a positive way, which should lead to customer attributions in favor of the firm. Attributions in turn have a substantial influence on customer reactions such as purchasing intention or willingness to recommend. While these arguments support the idea that an open architecture per se triggers attributional thinking, a customer is likely to draw on several sources of information when forming an opinion about a product offering. The context of a sales episode, and especially the behavior and appearance of the salesperson, should provide cues that constitute such information. Not only is there abundant evidence of a strong relationship between cues and customer reactions, cues have also been found to
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instigate and affect attributional thinking. Hence, in their search for the reasons behind an open-architecture offering, customers will evaluate different cues that are available to them, infer likely causes and react accordingly. In short, different cues should affect a customer’s reaction to an open product architectture, and this influence is likely to be mediated by attributions. Figure 2-2 summarizes the arguments that have been developed until this point in form of a high-level conceptual model.
Figure 2-2: Relationship of cues, customers’ attributions and reactions 5
Customer attributions
Cues obtained from salesperson behavior
Customer reactions
2.4.1
Objectives
While the relationships outlined in the previous sections are based on substantial evidence generated by marketing and services researchers, little is known on how customers specifically perceive an open-architecture offering and how they are likely to react to it. A qualitative study was therefore undertaken to further substantiate and concretise the assumptions of the proposed model and provide relevant details for the design of subsequent experimental studies.
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2.4.2
Design, Participants and Procedure
A series of semi-structured in-depth interviews was conducted with Swiss and
German banking clients in the time between March and May 2009. A sample of
10 male and 10 female interviewees was recruited, their ages ranging from 30 to
78, from a wide range of socioeconomic backgrounds (‘standard’ retail banking clients with financial assets below CHF 25k to “high net worth” individuals with assets exceeding CHF 1m). The interviewees were told that they participated in a research study on their general experiences with investment advice and products offered from their bank. In order to appropriately incentivize the interviewees for talking about sensitive matters such as personal finance, the introductory email informed them that 50 CHF would be donated to a charity project for each interview. In a first step, they were encouraged to talk about the experiences they had made with investment advice in general, whether and how they had received advice from a bank’s client advisor and what kind of products had been discussed. In almost all cases, the respondents also talked very explicitly about the expectations and general convictions they had brought and would bring into such an advisory meeting. The discussion then went on to address whether their bank had ever offered them third-party products, how they had perceived this or, if not, how they would react to such an offering. In the final stage, the respondents were asked to indicate conditions under which they would appreciate or resent being offered third-party products. Each interview was recorded and lasted from 40 to 70 minutes. After transcription and categorization of the interviews, a content analysis was conducted.
2.4.3
Results
2.4.3.1
Disconfirmation of Expectations
Results from the interview series demonstrate that many respondents held the expectation that a bank would only offer them its own investment products to
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choose from; for many of them, this expectation was rooted in personal experience. A few exemplary quotes are listed below:
ƒ "I would not have expected of an advisor of [bank name] that he would present other products than his own." (female, 39)
ƒ "I would not go to a bank in order to get advice, because, well, I would always have the feeling they offer me only their own products." (female, 37)
ƒ "Well, it's clear, they prefer their own products...I would assume they have the biggest margin on their own products." (male, 59)
ƒ "With funds from banks, I tend to be suspicious, because I always presume that they will foist their own funds on me." (female, 38)
ƒ "I simply had this idea that a bank's advisor is not objective." (male, 59)
2.4.3.2
Attributional Thinking
The interview results suggest that the above described beliefs and convictions were disconfirmed by the idea an open-architecture offering. They also indicate that attributional thinking was triggered. The vast majority of respondents
(n=19) articulated one or several motives they would suspect behind a recommendation of third-party products. Several interviewees explicitly mentioned that such an offering would make them ‘wonder why’ (n=4):
ƒ "I would ask him why.” (male, 53)
ƒ "I would ask 'Why?' Why does he offer me third-party products? If he's offering only his own products, well, that makes sense - he's an employee of the bank, after all. Questions would come up...it cannot be that he's just a dogooder!" (female, 37)
ƒ "Then I would ask him why!" (male, 64)
ƒ "...that would raise suspicion in me. I would need to ask: Why? Why does he offer this to me? What is his stake in this?" (female, 37)
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The study revealed a number of different attributions, which, as presumed, can be separated into customer-oriented and suspicion-oriented ones. The most frequently cited favorable attributions were that the bank was trying to offer a more complete and sophisticated range of products (n=7 respondents), that the advisor had the client’s best interest at heart (n=5) or gave neutral and objective advice (n=4). The majority of respondents would perceive an open-architecture model as a sign of (more) neutral and objective investment advice which in turn indicates that the bank has its clients’ best interest at heart. They tend to take the advantages of a wider choice of products at face value. Some interviewees underlined this perception with statements like "this shows me that [the advisor] has an intention that I'm doing well" (male, 59) or "it would have felt like there is somebody […] committed to get the best out of it for me" (male, 43) . A fairly similar attribution is that of "neutrality" or "independence" as a cause behind the third-party offering. In the interviews, such a reasoning was suggested by respondents claiming they would "like to have this kind of neutral advisor, and this would support the impression" (male, 37) or that they would "probably perceive it as relatively independent advice" (female, 36) . By recommending external products, the advisor also indicates "that he knows the market and has an overview of what's going on" (male, 37) and "that he's familiar with other products" (female, 37) . Such statements highlight that some clients will attribute an open-architecture offering to high competence and a superior market overview on the advisor's side. One of the most frequently mentioned causes is the assumption that no bank can cover all possible investment solutions with its own product portfolio and therefore needs to seek partnerships. It is important to note that all respondents who arrived at this specific attribution saw it as a logical consequence of the market’s diversity and not as a weakness of the bank’s in-house offering. The different customer-oriented attributions are listed in Table 2-1.
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Table 2-1: Typology of customer-oriented attributions 1
Attributions Definitions Examples
Advisor is independent/ objective
Client assumes that the advisor‘s behavior is caused by a
„neutral“ position, i.e., one that is not biased by any ulterior motive
Advisor has client's best interest at heart
Client attributes the advisor‘s behavior to a strong focus on client needs and a commitment to place clients’ interest ahead of self interest
Advisor has strong product expertise
Bank extends its offering bandwidth
Client perceives the advisor‘s behavior as a sign of product expertise
Client attributes the product selection offered to him to
„gaps“ in the bank‘s own product range and to the bank‘s intention to close these gaps (by including third-party products)
ƒ I'd like to have this kind of neutral advisor, and this would support my impression.
ƒ In that case, I would probably perceive it as relatively independent advice.
ƒ It would surely reinforce his independence.
ƒ This shows me [...] that he […] has an intention that I'm doing well.
ƒ It shows me that in this moment, my client advisor is busy covering my requirements...and not those of his sales track record.
ƒ To me, it would have felt like there is somebody […] committed to get the best out of it for me, not only [selling] his own funds
"by default".
ƒ With this, he would demonstrate that he knows the market and has an overview of what's going on.
ƒ It would also tell me that he's familiar with other products, not only the ones from his own bank.
ƒ I think, even a bank X might not be able to cover the whole market of products that exist in the financial world.
ƒ You can't be the best in every area. Therefore, you need to cooperate with people in places where you have some weak spots...I don't think there's anything infamous to it.
ƒ I think, in some parts they probably have to do this, because they are not able to provide the full bandwidth of products […] on the specialized side, they probably have to add something from external.
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Negative attributions were much less frequent and included suspicions that the advisor’s product recommendation was driven by obscure inter-company
‘alliances’ (n=4) or ulterior motives such as profit-making (n=1). Overall, few interviewees established a direct link between the offering of external products and unfavorable causes. Some respondents who had expressed a strong suspicion towards banks in general – supported by their own bad experiences – attributed the open architecture to "hidden alliances" of obscure intention. As one respondent put it, "a small doubt would remain...who's dealing here with whom, who's pushing products across to whom?" (female, 59). Another expressed his anxiousness that "there's some form of alliance or partnership in the back-ground, of which I do not know" (female, 36). For these clients, the open architecture did not come across as a sign of neutrality. This is in line with what Fein (1996, p. 1196) assumes: “Once they have become suspicious, perceivers are more likely to perceive a wide range of behavior as attributionally ambiguous. This mind-set may stem in part from the desire not to be duped by another individual.” Other statements indicate that clients suspect ulterior motives like the maximization of sales commissions or the achievement of sales targets: "Maybe there's also an interesting incentive on the financial side, for external products." (female, 36) .
Very few interviewees perceived the offering of third-party products as a sign that the advisor does not believe in his/her own products or that the bank's inhouse products are of inferior quality: "Does your fund underperform that much that you're not offering it to me?"(female, 38 ). However, this attribution was only triggered by a significant alteration of the question that respondents were confronted with – namely, when they were asked how they would perceive it if the advisor offered them only third-party products and none from his or her own bank. This limitation is critically important. As it has been pointed out at the beginning, banks’ in-house product factories tend to argue against an openarchitecture model: They claim that clients will perceive such an offering as a
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confession that the bank’s own products are of inferior quality or variety. The interviews did not yield any evidence of such an overall, undifferentiating effect.
Finally, the data also supports the idea that clients might perceive an open architecture as a sophisticated form of persuasion tactic. As one respondent put it: "[My suspicion would be] that in times of need, banks cooperate. And they go: 'People appreciate it if you also offer products from other companies.
Hence, if you offer our products, we'll offer yours'" (female, 37). This would indicate that persuasion knowledge, as Friestad and Wright (1994) have described it, is being applied. Such a conclusion is also backed by the fact that a majority of respondents suspected that their bank’s actions were generally driven by ulterior persuasion motives such as “achieving sales targets” or
“maximizing profits”. At this point, it is important to note that unfavorable attributions do not always entail an overall negative evaluation (Campbell and
Kirmani 2000). The qualitative data would seem to confirm this impression.
Even those clients who said they were aware of the bank’s sales motives claimed they would appreciate the market overview and competence displayed by offering third-party products. Those few clients who were either highly familiar with the idea of an open architecture and/or had a long-lasting, trusted and friendly relationship with their advisor were less prone to see any unfavorable ulterior motives behind their bank’s offering. Again, this is in line with Friestad and Wright (1994) who claim that persuasion knowledge is not always and necessarily accessed, but that occasionally, existing knowledge about a certain topic or about the sales agent himself might dominate the attitude forming. The different suspicion-oriented attributions are listed in Table 2-2.
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Table 2-2: Typology of suspicion-oriented attributions 2
Attributions Definitions Examples
Advisor has ulterior motives
Bank has ulterior motives
Bank's own products are inferior
Client assumes that the advisor‘s behavior is caused by ulterior motives such as maximising his own return from commissions or achieving a certain sales target
Client perceives the product selection offered to him as a sign of ulterior motives on the bank‘s side, such as the maximization of fees
Client attributes the product selection offered to him to the low quality of the bank‘s own product range
ƒ He probably makes more money on these, right? Even more!
ƒ Maybe there's also an interesting incentive on the financial side, for external products.
ƒ Of course, there's also the question of how they have been incentivized by the other banks and what the commissions are like.
ƒ I believe it's the internal quota that's driving him. Yes, I think the advisor also gets his orders from above, like "you have to place so many millions of these per year."
ƒ I don't know...a small doubt would remain...who's dealing here with whom, who's pushing products across to whom?
ƒ I would think there's some form of alliance or partnership in the background, of which I don't know.
ƒ I guess my first reaction would be: "Well, have you struck an alliance? How many sales orders need to get signed?"
ƒ I'd have my own thoughts, whether the bank itself is very effective then.
ƒ [I would ask] does your fund underperfom that much that you're not offering it to me?
ƒ Then I would have some questions about the bank's own products.
ƒ [I'd ask myself]…are their products so terrible?
ƒ Maybe the [third-party] products are better.
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2.4.3.3
Salesperson Behavior
A second objective behind the qualitative study was to provide a first answer to the question whether customers’ perceptions of an open-architecture offering are influenced by certain behavioral cues. It seems likely that clients will pay specific attention to cues that seem to support their initial inferences – research has shown that people often selectively absorb information that supports their initial beliefs and opinions (Jonas et al. 2001). Consequently, they will interpret available cues as reflecting a salesperson's expertise, ulterior motives, benevolence or other traits and motives. In the present research, such cues would mainly include the appearance, behavior and sales messages provided by a financial advisor. Indeed, the interviews revealed a number of cues provided during the sales episode that seem to have an influence on whether unfavorable or favorable attributions prevail in the client's attitude-forming process. This would include behaviors and statements that very directly suggest honesty, benevolence or expertise. This would be the case if, for instance, the bank advisor admitted that he or she earned a commission on selling a specific product or advised against buying a specific in-house product because its performance was unsatisfactory. Similar cues have been researched quite extensively (cf. Crosby et al. 1990; Doney and Cannon 1997; Sirdeshmukh et al.
2002; Swan et al. 1999; White 2005; Wood et al. 2008a; Wood et al. 2008b).
Therefore, this dissertation focuses on cues that are more specific to the scenario of an open-architecture offering, as they promise both relevant and novel insights.
Three distinct cues present in an open-architecture context have emerged from the qualitative data and are listed in Table 2-3. Firstly, clients appreciate an advisor’s proactiveness in offering third-party products (n=3): statements like
“the advisor should put them (the third-party products) on the table at the same time as the in-house products” (female, 38) suggest that clients want their advisor to treat the bank’s own products and external ones equally and to provide a transparent overview. If customers have to explicitly ask for third-
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party alternatives, they are prone to suspect that the advisor intended to withhold these options from them. As one interviewee put it: “Well, that would seem like a sales-force approach to me: It would tell me that this guy (the advisor) wants to see first whether I’ll take what he’s got in-house.” (female, 38)
A second cue that emerged from the interview data is the perceived ‘balance’ of the product mix , as it is reflected in the share of in-house vs. third-party products within the advisor’s recommended selection (n=9). Many interviewees demonstrated a clear expectation towards their bank: They want to see both own and third-party products getting offered to them. For some clients, it is acceptable (and somewhat foreseeable) that the share of in-house products may be more than 50% ( “I’d expect 2/3 in-house, 1/3 third-party; male, 53 ), as long as external options do not become a mere side note. With such an offering, the bank proves its competence as both product manufacturer and – relatively – neutral advisor.
Finally, the most frequently mentioned cue is the persuasiveness of reasoning provided by the advisor (n=13). Clients expect that their advisor explains his or her choice of products to them. And while they demand to understand why any specific product is being recommended (e.g., because of superior performance or low management fees), some request an even more elaborate reasoning behind any external product that is offered to them. This would seem in line with the finding that an open-architecture offering, while commonly appreciated, disconfirms many clients’ expectations and makes them ‘wonder why’. One respondent made this very clear: „Well, I would always expect him
[the advisor] to explain to me what this product does, what its features are. And if it was an external product, then indeed, I’d like to know ‘why this one?’ […]
With one of the bank’s own products, this idea would not occur to me” (male,
30). Note that the order in which the three cues have been presented here is no indication of their relative importance. Rather, they reflect the natural
“sequence” of the steps that a meeting with a financial advisor would often go through, as remembered (or imagined) by the interviewee. An advisor would
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demonstrate “proactiveness” during a first, broader discussion of the options at hand, the concrete, recommended “product mix” would then indicate whether competitor products are truly and genuinely a part of the actual offering. Finally, the advisor’s reasoning behind each product would be “persuasive” or not.
Table 2-3: Typology of salesperson cues 3
Cues Definitions Examples
Proactiveness in offering open architecture
Biased product mix
The fact that an advisor either offers external products on his or her own initiative (high proactiveness) or only if directly asked to do so by the client (low proactiveness)
The client's perception that the advisor's recommended choice either strikes a good balance between inhouse and external products or is strongly biased towards inhouse products
ƒ From my point of view, it would be better if he
(the advisor) brought them (the products) on right away.
ƒ (The advisor should) put them (the third-party products) on the table at the same time as the in-house products.
ƒ Certainly, a lot is achieved if they (the thirdparty products) are put on the table proactively...that's a step forward.
ƒ I believe that (the proactive offering of thirdparty products) would lower the risk of me going to see another advisor.
ƒ If he tried to foist only bank X products on me, I‘d be very skeptical.
ƒ Well, I‘d question the advice if somebody presented only his in-house products to me.
ƒ And when he said ‘I have only this one product for you, I can highly recommend it to you’, I‘d be very skeptical and I‘d think ‘OK, he‘ll probably get himself a Porsche from the
Kickbacks.
ƒ Only offering external products, that wouldn‘t be a smart solution. That would be merely a very ostentatious attempt at saying ‚Hey, I‘m
Mister Neutral‘.
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Table 2-3: Typology of salesperson cues (cont.) 4
Cues Definitions Examples
Persuasiveness of reasoning
The extent to which an advisor's reasoning behind his or her product recommenddation is perceived as credible, convincing and transparent
ƒ Well, I would always expect him [the advisor] to explain to me what this product does, what its features are. And if it was an external product, then indeed, I’d like to know ‘why this one?’
ƒ For me, this transparency matters enormously...that I have the feeling these things are presented to me in a transparent, forthcoming way.
ƒ It‘s obviously much more credible if I (in the role of the advisor) say ‘Look, there‘s different options and for each one, these are the pros and cons‘.
ƒ He would have to explain to me why he has selected exactly those.
ƒ He has to tell me why this is good; I want to know what his reasoning is.
ƒ If he says ‘that's what you do today’ or
‘everyone does this’, that's not an argument.
He has to justify his selection and in a way that I can understand it and say 'that makes sense.
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2.4.3.4
Summary
Purpose of the qualitative study was to further explore the relationships postulated by the high-level conceptual model and to generate more specific insights into customers’ perception of an open-architecture offering. To summarize, the results of the qualitative study would seem in line with the basic relationships forwarded in the previous chapter. Firstly, the findings shed some initial light on several cues that clients may observe during the advisory episode and that could subsequently influence their reaction to an offering of third-party products. Three of these cues seem to bear specific relevance in an openarchitecture context and will therefore receive further attention in the course of this dissertation: The persuasiveness with which an advisor argues for a specific choice of products, the proactiveness that an advisor displays in offering thirdparty products and the balance of in-house vs. third-party products in the advisor’s selection, i.e., the product mix. It should be noted that these cues occur in different phases of the sales encounter, which suggests that customers observe and evaluate the advisor’s behavior from start to end. Lastly, the data suggests that customers respond to an open product architecture with a variety of attributions. The interview statements include both favorable attributions regarding the benevolence, expertise or objectiveness of the advisor and unfavorable ones which suspect ulterior motives on the advisor’s or bank’s side.
While the limited sample size of the qualitative study allows no quantitative analysis, it should still be noted that customer-oriented attributions were prevalent. This would support the idea that – due to the general appeal of variety and/or the disconfirmation of certain long-held beliefs – an open-architecture offering is perceived favorably by many customers. The fundamental conclusions of Chapter 2 form a concretized conceptual model that is summarized in
Figure 2-3.
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Figure 2-3: Conceptual model of the relationship between salesperson cues, customers’ attributions and reactions 6
Salesperson cues Attributions Customer reactions
Customeroriented attributions
Persuasiveness of reasoning
Proactiveness in offering third- party products
• Satisfaction
• Purchasing
Intention
• Willingness to provide WOM
Balance of product mix
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In this chapter, a number of hypotheses are developed. They postulate that the three cues which have been identified – persuasiveness of reasoning, proactiveness and balanced product mix – will affect customer reactions to an openarchitecture offering. These reactions may take form, for instance, in higher satisfaction, intention to purchase or willingness to recommend. The hypotheses also point out that the effects of proactiveness and balanced mix on customer reactions are likely to be moderated by persuasiveness of reasoning. A second set of hypotheses argues that under certain conditions, these effects are strongly mediated by customer-oriented attributions.
3.1.1
Impact of Cues on Customer Reactions
3.1.1.1
Persuasiveness of Reasoning
Results of the qualitative study suggest that persuasiveness of reasoning is a powerful cue. This seems highly plausible since the importance of strong, convincing messages in sales and service encounters has been highlighted repeatedly. Research into influencing factors of customer persuasion has demonstrated a positive correlation between argument quality and customers' purchasing intentions (Berger, Cunningham, and Kozinets 1999; Hunt, Smith, and
Kernan 1985; Sanbonmatsu and Kardes 1988). Of specific relevance for the present study is the contribution of Hunt, Smith and Kernan (1985). The authors argue that under conditions of expectancy disconfirmation, strong arguments should have a substantial positive influence on customers’ purchasing intention and causal attributions, as a more thorough message processing is triggered.
More recent investigations into selling effectiveness have provided further support by underscoring that meaningful, argument-based communication is a key skill of successful salespeople (Ahearne, Jelinek, and Jones 2007; Anselmi
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and Zemanek Jr 1997; Dion and Notarantonio 1992; Plouffe, Hulland, and
Wachner 2009; Rentz et al. 2002) and that a critical element of an effective sales presentation is for salespeople to "discover relevant needs and, in persuasive terms, explain corresponding product benefits" (Boorom, Goolsby, and Ramsey
1998, p. 19). For the specific context of personal financial planning services,
Sharma and Patterson (1999) argue that communication effectiveness – determined by how well a financial advisor communicates to his clients, what and how much information he shares and how strongly he or she fosters clients' own understanding – is “the single most powerful determinant of relationship commitment” (Sharma and Patterson 1999, p. 151). This does not surprise – financial products are intangible, often complex and require a considerable explanation effort. Strong sales arguments are often associated with explaining product features and presenting the advantages and disadvantages of alternative options (Ahearne et al. 2007; Boorom et al. 1998; Hunt et al. 1985), which requires salespeople to have considerable expertise on the subject matter. In case of an open product architecture, clients are very likely to appreciate such expertise: Faced with a seemingly infinite choice of, e.g., investment products, insurance policies or vacation offers, they may feel the need to rely substantially on a salesperson’s or advisor’s expert knowledge. Expertise plays a significant role in building trust (Doney and Cannon 1997; Wood et al. 2008a; Wood et al.
2008b) and hence, it is very likely that a convincing reasoning will lead to favorable client reactions: It has been shown that a salesperson’s demonstration of expertise improves customer satisfaction and the overall buyer-seller relationship (Boles, Johnson, and Barksdale Jr 2000; Crosby et al. 1990).
Satisfaction in turn has often been associated with customers’ loyalty (Anderson and Sullivan 1993; Bolton and Lemon 1999; Rust and Zahorik 1993) and their willingness to refer and recommend (Brown et al. 2005; de Matos and Rossi
2008; Rajaobelina and Bergeron 2009; Swan and Oliver 1989).
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In general, intentions to purchase products or services have been shown to correlate with the willingness to provide positive word of mouth (Bloemer, de
Ruyter, and Wetzels 1999; Zeithaml 2000). On the basis of these arguments, the following hypothesis is suggested.
Hypothesis 1: Persuasiveness of reasoning has a significant effect on
Satisfaction, Purchasing intention and WOM Intention
3.1.1.2
Proactiveness
The interview data also seems to indicate that many clients appreciate if the advisor proactively includes third-party products in his or her product recommendation. Imagine you wanted to fly to New York and you knew that the service center agent at SWISS could in theory sell you any airline ticket available: After you had provided your timing requirements, one of the agent’s first suggestions would be to book an Emirates flight because it was the option that best matched your needs. It seems quite likely that such an initiative-taking would be perceived positively. And even though proactiveness has rarely been investigated as a behavioral cue that customers distinctly recognize, there is substantial research that links the proactiveness of salespeople or companies to their sales and service effectiveness (e.g., Beverland, Farrelly, and Woodhatch
2007; Challagalla, Venkatesh, and Kohli 2009; Worsfold, Worsfold, and
Bradley 2007). Proactive post sales service, for instance, has been shown to lead to higher customer satisfaction – by preventing misuse and failure of a product or by soliciting customer feedback (Challagalla et al. 2009). It seems plausible that in the case of an open-architecture offering, proactiveness my bear an even greater relevance: First of all, many interviewees in the qualitative study were unaware that banks offered third-party products in the first place. This seems in line with reports that at many banks, an open-architecture offering – though officially promoted – is something that only few clients have yet experienced in day-to-day reality (Bender 2009; Bolton et al. 2007). Hence, only if the advisor
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recommends third-party products on his or her own initiative, many clients will actually learn about such an offering. Secondly, the qualitative study confirmed that many clients believe a bank will get the highest return on selling its own inhouse products. Thus, it seems to oppose the advisor's own interest if he or she proactively promotes competitor products. Different lines of marketing and sales research have shown that consumers often perceive seemingly counter-intuitive behaviors and messages from salespersons or advertisers as signs of benevolence, i.e., "an underlying motivation to place the consumer's interest ahead of self-interest" (Sirdeshmukh et al. 2002, p. 18). The advocacy of an unexpected position, incongruous or two-sided messaging can improve a communicator’s credibility, as this behavior suggests sincerity and trustworthiness to a client (Crowley and Hoyer 1994; Etgar and Goodwin 1982;
Golden and Alpert 1987; Koeske and Crano 1968; Kohn and Snook 1976;
Pechmann 1992). But can any positive effect of proactiveness prevail even when the salesperson has given little or poor explanation of why a certain product is recommended? That seems unlikely, given the need for explanation articulated by interviewees in the qualitative study. A salesperson’s convincing reasoning behind his or her product recommendation would seem a ‘table stake’ in most sales encounters - especially in an advisory context like investment solutions, in which the explanation of complex, intangible products and their individual advantages is a key element of the service offered to customers (Sharma and
Patterson 1999). Therefore, it is conceivable that the influence of proactiveness is moderated by the first cue, persuasiveness of reasoning. In summary, clients are likely to perceive it as counter-intuitive, benevolent behavior if their advisor offers them products from other companies, but they might only do so as long as the advisor acted on his or her own initiative. At the same time, it seems very likely that a sound reasoning given by the advisor is a “must-have” and without it, proactiveness may have little positive effect on customer perceptions and behavioral intentions.
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These arguments lead to the following hypothesis:
Hypothesis 2: Under high (low) persuasiveness of reasoning, proactiveness in recommending third-party products has a (has no) significant effect on
Satisfaction, Purchasing intention and WOM Intention
3.1.1.3
Product Mix
Finally, data from the qualitative study suggest that the right balance between in-house and external products may be another critical cue. The interview data allow the conclusion that many clients expect the advisor to provide a
‘reasonable’ balance of in-house and third-party products. Consequently, many clients will find it suspicious if a salesperson is in a position to offer both inhouse and third-party products, but in reality only (or predominately) recommends in-house ones. And in many cases, such a suspicion would seem justified: There is well-known risk, for instance, that financial advisors make a biased product recommendation to their clients because they pursue own interests (Bolton et al. 2007; Jonas and Frey 2003; Krausz and Paroush 2002). In fact, most customers assume that a salesperson is biased in one way or another
(e.g. Friestad and Wright 1994; Stafford et al. 1995). Bias has been argued to take form, for instance, in the withholding of relevant information (Eagly,
Wood, and Chaiken 1978; Jonas and Frey 2003). The availability and adequacy of other relevant options can certainly be seen as critical information – and this information is kept from the client if the advisor’s selection is clearly dominated by products of one provenance. Hence, the very mix of products recommended by the advisor may indicate a salesperson’s bias and is therefore likely to serve as a cue to clients. Again, however, it should be asked whether the positive effect of a well-balanced product mix can prevail when the reasoning behind the advisor’s selection is unsatisfactory and unconvincing. For reasons similar to those provided in the previous section, this would seem unlikely. If a salesperson presented a “fair” mix of different products but ultimately failed to
49

explain in a convincing way why these qualified for recommendation, this would surely leave most customers unsatisfied. On the other hand, customers who perceive the salesperson’s individual reasoning as persuasive are likely to show even more positive reactions when they consider the very mix of recommended products as unbiased. Hence, the following hypothesis is forwarded.
Hypothesis 3: Under high (low) persuasiveness of reasoning, the perceived
“balance” of the mix of recommended products has a (has no) significant effect on Satisfaction, Purchasing intention and WOM Intention
Hypotheses 1,2,3 are illustrated in Figure 3-1 .
Figure 3-1: Effects of salesperson behavior on customer reactions 7
Advisor Cues
Persuasiveness of reasoning
Proactiveness in offering thirdparty products
Balance of product mix
H
H
H
2
3
1
Customer Reactions
• Satisfaction
• Purchasing
Intention
• Willigness to provide WOM
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3.1.2
Causal Attributions as Mediator
The three previous hypotheses have outlined the direct influence that different cues gained from a salesperson’s behavior may have on customer reactions. The conceptual model has also forwarded the idea that these effects are likely to be mediated by attributional thinking. The qualitative pre-study sheds some initial light on such cognitive processes. The data suggest that customers attribute an open-architecture offering to a number of different customer-oriented, and less frequently, suspicion-oriented causes. Given the appeal of variety, and the positive effect of disconfirmed expectancies, is does not surprise that favorable attributions prevail when customers form their opinion about an open product architecture. In the interview sessions, such attributions included assumptions that the advisor or salesperson was acting in the client's best interest or offered neutral and objective advice.
Attributions have been shown to substantially determine consumers’ affective and behavioral responses to an event. Previous research has highlighted a considerable influence of customers' attributions on their desire to provide wordof-mouth (Bitner 1990; Curren and Folkes 1987; Swanson and Kelley 2001) and
(re-) purchasing intentions (Casado Diaz and Más Ruíz 2002; Folkes et al.
1987). The role that attributions play on customers' affective and behavioral responses, such as willingness to recommend and purchasing intentions, has also been confirmed by Hui and Toffoli (2002) and DeCarlo (2005). Recently,
Rajaobelina and Bergeron (2009) have demonstrated for the specific context of personal financial advisory that perceived customer-orientation has a positive influence on both purchase intention and word-of-mouth. At the same time, different studies have argued that attributions are often triggered or influenced by behavioral cues provided, for instance, by a salesperson (Burnkrant 1975;
DeCarlo 2005; Rose and Dickson 1988; Wood et al. 2008a). In light of both the antecedents and consequences of attributional thinking, a mediation role seems to emerge. This notion is supported by DeCarlo (2005), who has explicitly
51

shown how attributions can mediate the effect of a cue – such as salesperson communication – on consumer attitudes.
3.1.2.1
Mediation of Persuasiveness of Reasoning
Summarizing the above arguments and those that have been forwarded in
Chapter 2, it seems likely that customer reactions instigated by different behavioral cues will be significantly mediated by customer-oriented attributions. If a salesperson, for instance, delivered a very convincing reasoning for picking a specific product, a customer may attribute this to the fact that the salesperson was both understanding and very knowledgeable and consequently would recommend him or her to others. For the first cue, persuasiveness of reasoning, this argument is summarized in the following hypothesis:
Hypothesis 4: In an open-architecture sales context, customer-oriented attributions mediate the influence of reasoning persuasiveness on Satisfaction,
Purchasing intention and WOM Intention
3.1.2.2
Mediation of Proactiveness
The argument that the relationship of cues and customer reactions will be mediated by attributional thinking also applies to the second cue, proactiveness. At the same time, Hypothesis 2 postulates that the positive effect of proactiveness on customer reactions is contingent upon the level of persuasiveness. Without a sound reasoning, proactive behavior has little impact. Hence, any possible mediation of proactiveness can only happen under high persuasiveness – otherwise, there is no effect to mediate. This argument leads to the following hypothesis:
Hypothesis 5: Under high persuasiveness, customer-oriented attributions mediate the influence of Proactiveness on Satisfaction, Purchasing intention and
WOM Intention
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3.1.2.3
Mediation of Product Mix
Finally, a similar reasoning as the above one can be applied to the third cue, product mix. If a) a balanced product mix leads to favorable customer reactions only in conjunction with a highly persuasive reasoning and b) such an effect is likely to be mediated by customer-oriented attributions, then these two arguments can be summarized in the following hypothesis:
Hypothesis 6: Under high persuasiveness, customer-oriented attributions mediate the influence of Product Mix on Satisfaction, Purchasing intention and
WOM Intention
Hypotheses 4, 5, 6 are illustrated in Figure 3-2.
Figure 3-2: Mediation through customer-oriented attributions 8
Advisor Cues
Persuasiveness of reasoning
Proactiveness in offering third- party products
Balance of product mix
H
4
H
5
Customeroriented attributions
H
6
H
1
H
2
H
3
Customer
Reactions
• Satisfaction
• Purchasing
Intention
• Willigness to provide WOM
53

This chapter presents the design and results of the two experimental studies that were conducted in order to test the hypothesized effects. Experiments were chosen as the preferred method for hypothesis testing, as the validation of theories through the observation of actual service encounters poses a methodlogical challenge. Interactions between individuals can vary substantially and in combination with different environments, there is an abundance of confounding variables, or ‘noise’ (Bateson and Hui 1992). Laboratory experiments avoid this dilemma by offering the researcher high levels of control and by providing a setting in which individual variables can be manipulated individually and effects can be isolated to a certain extent. Due to these advantages, experiments are a common, if not a dominant method in services research.
The independent variables in the focus of this dissertation are different behavioral cues provided by a salesperson. Therefore, a role-playing scenario featuring a salesperson and a client promised to be a highly suitable form of experimental treatment for the hypothesis testing. Many experiments in psychological and marketing research have employed comparable role-playing scenarios since they offer control and the experimental stimuli can be manipulated precisely. In addition, previous research indicates that test subjects find salesperson scenarios to be believable and understandable (e.g. Bitner 1990).
It was feasible and – in order to measure interaction effects – desirable to cover all cues within one and the same experiment. However, in order to improve the generalizability of results, it seemed advisable to test the hypotheses in two different experimental studies. Firstly, it has been argued that the validity of experimental results is contingent upon the level of realism and authenticity achieved by the experimental setting (Bateson and Hui 1992). Clearly, one of the decisive factors in this context must be the type of medium through which
54

the service environment and interaction are created and presented to the test subjects. One of the key differences between Experiment 1 and 2 was therefore the type of simulation used to depict the salesperson-customer encounter. The first experiment featured a written scenario that test subjects were confronted with at the beginning of the session, an approach commonly used in service studies (e.g., DeCarlo 2005; Wentzel 2009; White 2005; Wood et al. 2008a).
Stimuli presented through the use of written vignettes offer a very effective control of the independent variables, but they have been accused of lacking realism (Bateson and Hui 1992). Therefore, while testing the same hypotheses,
Experiment 2 used a video clip that showed a customer-salesperson interaction very similar to the written one of Experiment 1. As early as 30 years ago, researchers have proposed to overcome the disadvantages of text stimuli by using videotaped interactions as experimental cues: "videotape technology may offer a particularly appropriate method for isolating the effect of individual difference variables on attributions of organizational behavior" (Niebuhr, Manz, and Davis Jr 1981, p. 46). Specifically, it has been argued that video vignettes as experimental stimuli both offer a high degree of control over the manipulated variables and confounding effects, and provide greater realism (Grandey et al.
2005). Consequently, several studies report laboratory experiments in which video stimuli led to results of satisfactory external and internal validity (Baker et al. 2002; Bateson and Hui 1992; Grandey et al. 2005). Such results would also seem in line with Bateson and Hui's (1992) finding that videotapes induced psychological and behavioral effects similar to those in a real-life setting.
A second variation of the setup of Experiment 2 versus Experiment 1 was achieved by introducing a different line of products and services – the life insurance business – and by using a sample with different properties. Figure 4-1 provides an overview of the experimental analyses. The design and the results of the experiments are presented in the following sections.
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Figure 4-1: Overview of experimental analyses 9
Proactiveness, Product mix, Persuasiveness
Written scenario
238 Swiss and German banking clients
1-6
Proactiveness, Product mix, Persuasiveness
Video scenario
260 German adults from an online panel
1-6
4.2.1
Design, Participants and Procedure
Study 1 employed an online experiment using a 2 (persuasiveness high/low) x 2
(proactiveness high/low) x 2 (product mix balanced/biased) between-subjects design. A sample of 238 responses was captured from test subjects recruited through the online community ‘XING’, a business network similar to ‘LinkedIn’ or other online platforms. This approach promised a higher suitability of subjects for the experiment: The average participant in the sample – German or
Swiss, well into his or her business career and mostly aged between 30 and 50 – would be more likely to have built own experiences with financial investment advice than, for instance, students. 53.8% were male, 46.2% were female. In terms of age, 20.2% were less than 30 years, 72.2% were between the ages of 31
56

and 50 and 7.6% were over the age of 50. The sample distribution across the eight experimental cells ranged from a low of 25 (10.5%) to a high of 32
(13.4%) participants. All participants received an email including a link to the online survey homepage. The email informed them also that for each completed questionnaire, CHF 5 would be donated to a charity project.
Following an introduction on how to proceed in the survey environment, each participant was randomly assigned to one of 8 written treatments. Similar to previous research (e.g. Campbell and Kirmani 2000; DeCarlo 2005; Folkes
1984; Swanson and Kelley 2001), the test subjects were instructed to carefully read the scenario and imagine themselves as the client in the interaction. The text described a meeting of the participant with a financial advisor at his or her bank. As stimuli, the scenario comprised a) high/low proactiveness in offering third-party products, b) biased/balanced mix of selected products and c) advisor’s persuasive/unconvincing reasoning. The content of all eight scenarios was identical exclusive of the cue manipulation. After reading the treatment, participants were asked to respond to several measures of customer-oriented attributions (e.g., advisor has my best interest at heart) and reactions (e.g., intention to purchase). The structure of the experiment is depicted in Figure 4-2.
Figure 4-2: Structure of online experiment 1 10
General briefing and introduction to the questionnaire
Exposure to written customerclient interaction scenario
Measurement of attributions
Measurement of customer reactions and other variables
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The subjects were requested to rate a number of statements, using a Likert scale with anchors of strongly disagree (1) and strongly agree (7). The different attributions offered in the questionnaire were based on the results of my qualitative study. Different methods of measuring attributions have been discussed by Elig and Frieze (1979): According to their comparisons, intertest validity and reliability of results seem to be better for structured response methods than for open-end questions. Consequently, the authors recommend the use of scale measures, as employed in the present research. A disadvantage of structured responses may lie in the fact that subjects are prompted with attributions they might not have formed otherwise (Enzle and Schopflocher
1978). However, such a risk should be reduced by using the specific typology of attributions which were generated in the preliminary qualitative study. This way, subjects were confronted only with a selection of attributions that had been proven to occur in the specific context of an open architecture. Overall, the rating of predefined attributions would seem common practice in the field of marketing and sales research (e.g. Dixon, Spiro, and Jamil 2001; Dubinsky,
Skinner, and Whittler 1989; Lichtenstein and Burton 1988).
4.2.2
Manipulation of Independent Variables
Each of the experimental scenarios featured all three cues. In order for the test subjects to get sufficiently immersed in the experimental scenario, all participants were exposed to one and the same short introduction text. Its purpose was to explain the overall situation, introduce the idea of an openarchitecture offering, improve the salience of participants' expectations towards a bank and its advisor and make them receptive to the specific stimuli tested in the experiment. The text read as follows:
58

Recently, you've decided to benefit from soaring equity prices on the
German and Swiss stock markets. In order to do this, you've put aside a certain amount of money that you'd like to invest into suitable mutual funds. It's important to you that you take a well thought-through, sound investment decision. Therefore, you have made an appointment with the bank that you also use for your other financial transactions.
A while ago, you've read that some banks offer a so-called "open product architecture". Clients of these firms can not only buy in-house funds managed by the bank itself, but they can choose from a wider offering of funds that includes external products from third parties. You don't know whether your bank offers such third-party products, but you intend to find out.
You meet with Thomas Breiter, your client advisor, in the lobby of the bank's local branch. After a short welcome, Mr. Breiter accompanies you to his office where you take seat at a small conference table. After his assistant has brought you an espresso, you and Mr. Breiter discuss your financial requirements and expectations for a while. Then, the client advisor suggests to present you with a selection of products that are suitable for your goals.
4.2.2.1
Manipulation of Proactiveness
‘Advisor’s proactiveness in offering third-party products’ was manipulated by how actively the advisor brought up the open-architecture offering of his firm and proposed to include third-party products in his recommendation. The scenario depicted the advisor as either acting on his own initiative or only on the client’s explicit demand. The exact wording of the manipulations was as follows:
High proactiveness: Mr. Breiter rises from his chair, walks over to his PC and begins to call up a variety of fund profiles. He turns around to you and says: "Maybe you've heard that our firm has an 'open product architecture'?
As you nod, he continues: "That means you don't get only in-house funds from us, but also those of other providers. I'd suggest that I'll also include those third-party products in my selection."
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Low proactiveness: Mr. Breiter rises from his chair, walks over to his PC and begins to call up a variety of fund profiles. He turns around to you and says: "Our in-house funds are really excellent, I'll pick a few for you." You respond: "Yeah, sure,…but, would it be possible that you also show me a few funds from other companies?" Mr. Breiter hesitates briefly and answers: "Ok...sure…if you like…then I can do that."
4.2.2.2
Manipulation of Product Mix
With regard to the manipulation of “product mix”, results from the qualitative study suggested that in-house products must not strongly dominate the advisor’s selection if the overall mix is supposed to be perceived as well-balanced. Hence, the advisor in the scenario proposed six different investment funds out of which either three or five were from his own bank. The exact manipulations were as follows:
Balanced product mix: With a focused expression, the client advisor's glance sweeps across the wide range of different funds that his PC is displaying. Finally, he prints 1-page profiles for some of the funds and takes these to the conference table, where he spreads the pages out in front of you. You leaf through the six documents. Three of the selected funds are in-house products of the advisor's bank, the other three are from different external fund managers.
Biased product mix: With a focused expression, the client advisor's glance sweeps across the wide range of different funds that his PC is displaying.
Finally, he prints 1-page profiles for some of the funds and takes these to the conference table, where he spreads the pages out in front of you. You leaf through the six documents. Five of the selected funds are in-house products of the advisor's bank, only one is from an external fund manager.
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4.2.2.3
Manipulation of Persuasiveness
‘Persuasiveness of the reasoning behind the advisor’s recommendation’ was manipulated by how well the advisor managed to explain his reasons for making a specific recommendation. In one half of the scenarios, he was described as outlining his reasons in an insightful and transparent way, highlighting pros and cons. In the other half, he was evasive and used financial jargon instead of sound arguments. A full description of the treatments can be found in the appendix.
The exact manipulations read as follows:
High persuasiveness: You take a sip of your espresso and give the different fund profiles another glance. "Ok", you go and lean back in your chair,
"why do you recommend these specific funds?" "Of course", Mr. Breiter smiles, "let me explain that to you." He then gives you a very accurate and transparent explanation on why he's chosen each product and what their individual pros and cons are. He also illustrates very clearly, how these different funds will offer you a good diversification of your risk.
Low persuasiveness: You take a sip of your espresso and give the different fund profiles another glance. "Ok", you go and lean back in your chair,
"why do you recommend these specific funds?" "Oh, well" says Mr. Breiter and shrugs. "It's pretty difficult to explain this in detail". He goes in to longwinded elaboration, using a lot of jargon. Mr. Breiter does not explain the different pros and cons of each product, but assures that he's got a "good feeling" about the selection he has recommended.
4.2.3
Selection of Measures
4.2.3.1
Dependent Measures
In Experiment 1, four dependent variables were measured: customer-oriented attributions, satisfaction, purchasing intention and willingness to provide wordof-mouth (WOM). All variables were measured as multi-item constructs,
61

leveraging existing scales whenever possible. All items had been pretested for face validity, asking 20 clients of different banks for a check on clarity and unambiguousness. Participants rated their customer-oriented attributions on five seven-point scales (applies very much / does not apply at all) which had been created based on the results of the qualitative pre-study. The reliability level of the scale ( Į = .83) was satisfactory. Table 4-1 provides a list of the items that comprised the customer-oriented attributions measure.
Table 4-1: Attribution measurement items 5
Please indicate your agreement with the following statements on a scale from "do not agree at all" to "completely agree"
ƒ Mr. Breiter is interested in my welfare and long-term benefit
ƒ Mr. Breiter is very motivated to achieve my goals
ƒ Mr. Breiter is a neutral advisor
ƒ The advice that Mr. Breiter gives is objective
ƒ Mr. Breiter's recommendations are unaffected by him being employed by a bank
With regard to the different customer reactions, satisfaction was measured using three items (very satisfied / not at all satisfied with the service, like very much/do not like at all what has been done, has highly fulfilled / not at all fulfilled my requirements) that Hui et al (2004, referring to Westbrook 1980) have applied. In order to measure purchasing intention, a 3-item scale was taken from De Carlo (2005) and slightly adapted to fit a service encounter in which investment funds are discussed (very likely/very unlikely to use Mr. Breiter’s support, very likely/unlikely to invest at Mr. Breiter’s bank, very likely/very unlikely to purchase products from Mr. Breiter) . Finally, the intention to provide
Word-of-Mouth was measured with a 3-item scale (very likely / very unlikely to provide WOM, very likely / very unlikely to recommend, very likely / very unlikely to suggest to friends) taken from Maxham III and Netemeyer (2002).
All items employed seven-point Likert scales.
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4.2.3.2
Manipulation Checks
In order to ensure that the three different salesperson cues were manipulated successfully, participants of Experiment 1 rated each stimulus on two sevenpoint scales. In each case, the second measurement scale was reversed to counter response-set effects. Participants rated two statements, assessing the persuasiveness of the advisor (“Mr. Breiter made an effort to explain his recommendations well”, “The reasoning Mr. Breiter gave for his fund selection was hardly convincing”) . The two items yielded a satisfactory reliability level of r = .70.
Table 4-2: Overview of measures used in experiment 1 6
Measure
Dependent Variables
Customer-oriented attrib.
Satisfaction
5
3
Intention to purchase 3
Willingness to provide WOM 3
Number of Items Reliability Source
α = .83
α = .96
α = .96
α = .98
Hui et al (2004)
DeCarlo (2005)
Maxham III and
Netemeyer (2002)
Manipulation Check
Persuasiveness
Proactiveness
Product mix
2
2
2 r = .70 r = .87 r = .82
For proactiveness, the statements to rate were “Mr. Breiter has offered me thirdparty funds on his own initiative” and “only at my request, Mr. Breiter has offered me funds that were not from his own bank” . The level of reliability (r =
.87) was good. Finally, in order to measure the manipulation of product mix, the participants assessed the statements “the advisor has offered me a balanced mix
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of in-house and external products ” and “products of his own bank dominated the advisor’s product selection” (r = .82). All items were anchored by applies very much / does not apply at all . The different dependent variables and manipulation checks and their reliability measures are listed in Table 4-2.
4.2.4
Results
4.2.4.1
Manipulation Checks and Item Reliability.
Firstly, a series of manipulation checks was applied to ensure that the different manipulations had been effective. The salesperson’s reasoning was considered more persuasive under conditions of high persuasiveness than under low persuasiveness (M
HighPers
= 4.56, M
LowPers
= 1.83, F(1,238) = 275.104, p < .001). While this intended effect was by far the strongest, marginally significant effects were also measured for the independent variable proactiveness (F(1,238) = 2.87, p <
.093) and for the interactive effects of persuasiveness/proactiveness (F(1,238) =
3.17, p < .077), persuasiveness/product mix (F(1,238) = 2.90, p < .091) and proactiveness/product mix (F(1,238) = 3.12, p < .08). Product mix by itself
(F(1,238) = 1.64, p > .20) and the interaction of all three independent variables
(F(1,238) = .93, p > .33) had no significant effect on the perception of persuasiveness. In conclusion, the manipulation of the independent variable persuasiveness was effective, but not as precise as could be desired.
Respondents who had received a scenario treatment with high proactiveness reported a greater value for the advisor’s proactiveness than in those in the low proactiveness condition (M
HighProact
= 5.17, M
LowProact
, = 1.51, F(1,238) = 416.73, p < .001). Furthermore, it was tested whether the other independent variables, persuasiveness or product mix, or any interactions among the independent variables had an effect on the manipulation check. The main effects of neither persuasiveness (F(1,238) = 1.09, p > .29) nor product mix (F(1,238) = 1.36, p > .24) were significant. The same applies to the interaction effects of persuasive-
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ness/proactiveness (F(1,238) = .22, p > .64), persuasiveness/product mix
(F(1,238) = .01, p > .91), proactiveness/product mix (F(1,238) = 2.49, p >.11) and the interaction of all three independent variables (F(1,238) = .54, p > .46).
Hence, it is concluded that the manipulation of proactiveness was successful.
A third manipulation check revealed that respondents in the balanced-mix condition reported a considerably higher value for balanced product mix than those in the biased product mix condition (M
BalancedMix
= 4.08, M
BiasedMix
= 1.62,
F(1,238) = 209.22, p < .001). However, this manipulation would seem to have been the least accurate one: significant effects were also measured for the independent variables persuasiveness (F(1,238) = 3.15, p < .078) and proactiveness
(F(1,238) = 27.18, p < .001), as well as for the interactive effects of product mix and proactiveness (F(1,238) = 11.63, p < .002). The data suggest no significant effect of the interactions of persuasiveness/product mix (F(1,238) = .12, p >
.73), persuasiveness/proactiveness (F(1,238) = .02, p > .89) and the interaction of all three independent variables (F(1,238) = .67, p > .41).
Reliability was assessed using the Cronbach’s alpha coefficient, which yielded satisfactory values for the construct of 'customer-oriented attributions' (0.83), for satisfaction (0.96), purchasing intention (0.96) and willingness to provide WOM
(0.98). These values can also be found in Table 4-2.
4.2.4.2
Hypothesis Testing
Effect of persuasiveness on customer reactions.
A three-factor multivariate analysis of variance (MANOVA) was used to test for the main and interactive effects of cues on customer reactions. Hypothesis 1 predicted that persuasiveness would have a significant effect on customer reactions. The results of the first experiment (Table 4-3) show that persuasiveness of reasoning indeed had substantial influence on customers’ satisfaction (F satis
(1,238) = 80.96, p < .001). their intention to purchase (F purchase
(1,238) = 44.99, p < .001), and their willingness to provide word-of-mouth (F
WOM
(1,238) = 55.52, p < .001).
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Table 4-3: Results of multivariate analyses in experiment 1; customer reactions as dependent variable, main effects 7
Dependent Variable F(1,238) p
Persuasiveness Satisfaction of Reasoning Intention to purchase
(high/low)
80.96
44.99
Willingness to provide WOM 55.52 p <. 001 p <. 001 p <. 001
Proactiveness
(high/low)
Satisfaction
Intention to purchase
20.46
13.63
Willingness to provide WOM 16.64 p <. 001 p <. 001 p <. 001
Product Mix
(balanced /
biased)
Satisfaction 13.97
Intention to purchase 11.37
Willingness to provide WOM 8.35 p <. 001 p <. 002 p <. 005
Table 4-4: Mean values for customer reactions as dependent variables in experiment 1, main effects 8
Low Persuasiveness of Reasoning
High Persuasiveness of Reasoning
Mean Mean
Dependent Variables
Satisfaction
Intention to purchase
Willingness to provide WOM
1,83
0,96
2,09
1,19
1,47
Manipulation Check
0,87
1,83
1,11
Note: Numbers in italic letters are standard deviations
3,25
1,57
3,29
1,58
2,64
1,55
4,56
1,42
Respondents were more satisfied when persuasiveness was high (M satis
= 3.25) than when it was low (M satis
= 1.83). As Table 4-4 shows, they were also more intent to purchase (high persuasiveness: M purchase
= 3.29, low persuasiveness:
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M purchase
= 2.09) and more willing to provide word-of-mouth (high persuasiveness: M
WOM
= 2.64, low persuasiveness: M
WOM
= 1.47). This supports H1.
Moderation of proactiveness.
In Hypothesis 2, it was postulated that proactiveness would only have a positive effect on customer reactions under conditions of high persuasiveness of reasoning. The experimental results show such a significant interaction for all three variables (F satis
(1,238) = 21.44, p < .001;
F purchase
(1,238) = 14.37, p < .001; F
WOM
(1,238) = 23.58, p < .001). The corresponding values are also listed in Table 4-5. As Figure 4-3 shows, customer reactions were only improved through high proactiveness when persuasiveness was high (high proactiveness: M satis
=3.88; M purchase
=3.86; M
WOM
=3.25; low proactiveness: M satis
=2.55; M purchase
=2.65; M
WOM
=1.96). Under conditions of low persuasiveness, proactiveness had no significant effect (high proactiveness:
M satis
=1.82; M purchase
=2.08; M
WOM
=1.42; low proactiveness: M satis
=1.84; M purchase
=2.10; M
WOM
=1.53). All values are listed in Table 4-6.
Table 4-5: Results of multivariate analyses in experiment 1; customer reactions as dependent variables, interaction effects 9
Dependent Variable
Persuasiveness x Satisfaction
Proactiveness Intention to purchase
Willingness to provide WOM
Persuasiveness x Satisfaction
Product Mix Intention to purchase
Willingness to provide WOM
F(1, 238)
21.44
14.37
23.58
9.03
0.88
4.06 p p < .001 p < .001 p < .001 p < .004 p > .34 p < .05
For high persuasiveness, a t-test of contrast effects yielded two-tailed p-values <
.001 for all three customer reaction variables. For low persuasiveness, no significant effects were measured, with p-values > 0.9 (satisfaction), > 0.9
(intention to purchase) and > 0.45 (willingness to provide WOM). These findings are fully consistent with H
2
.
67

Table 4-6: Mean values for customer reactions as dependent variables in experiment 1, interaction persuasiveness x proactiveness 10
Low Persuasiveness of Reasoning
High Persuasiveness of
Reasoning
Low Proactiveness
High Proactiveness
Low Proactiveness
High Proactiveness
Dependent Variables
Satisfaction 1,84
Intention to purchase
0,94
2,10
1,21
Willingness to provide WOM 1,53
0,87
Manipulation Check 1,46
1,05
5,02
1,78
Note: Numbers in italic letters are standard deviations
1,82
1,00
2,08
1,18
1,42
0,87
2,55
1,25
2,65
1,25
1,96
1,14
1,56
0,88
3,88
1,58
3,86
1,63
3,25
1,63
5,31
1,58
Figure 4-3: Interaction of persuasiveness and proactiveness in experiment 1; customer reactions as dependent variables 11
3,0
2,5
2,0
1,5
1,0
0,5
0,0
4,5
4,0
3,5
Satisfaction
Proactiveness
LOW
Proactiveness
HIGH
Persuasiveness
LOW
Persuasiveness
HIGH
68

Figure 4-3 (cont.): Interaction of persuasiveness and proactiveness in experiment 1; customer reactions as dependent variables 12
2,5
2,0
1,5
1,0
0,5
0,0
4,5
4,0
3,5
3,0
Purchasing
Intention
Proactiveness
LOW
Proactiveness
HIGH
Persuasiveness
LOW
Persuasiveness
HIGH
2,5
2,0
1,5
1,0
0,5
0,0
4,5
4,0
3,5
3,0
Persuasiveness
LOW
Persuasiveness
HIGH
Willingness to provide WOM
Proactiveness
LOW
Proactiveness
HIGH
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Moderation of product mix.
Hypothesis 3 postulated that product mix would have a positive effect on customer reactions only when persuasiveness of reasoning was high. The data provide evidence of such an effect for the dependent variables satisfaction and willingness to provide WOM (F satis
(1,238)
= 9.03, p < .004; F
WOM
(1,238) = 4.06, p < .05). For purchasing intention, no such moderating influence was detected (F purchase
(1,238) = 0.88, p > .34).
Instead, it would seem that product mix always has a significant effect on purchasing intention, independent of the persuasiveness of arguments. The results are listed in Table 4-5. As the mean values in Table 4-7 demonstrate, satisfaction , purchasing intention and willingness to provide WOM were significantly higher under conditions of high persuasiveness and balanced product mix (M satis
=3.70; M purchase
=3.59; M
WOM
=2.95) than under high persuasiveness and biased product mix (M satis
=2.76; M purchase
=2.96; M
WOM
=2.31). No such effect could be found when persuasiveness was low (Balanced product mix: M satis
=1.89; M
WOM
=1.54; biased product mix: M satis
=1.77; M
WOM
=1.41), with the exception of purchasing intention (balanced product mix:
M purchase
=2.30; biased product mix: M purchase
=1.89). The interaction effects are illustrated in Figure 4-4.
For high persuasiveness, a t-test of contrast effects yielded two-tailed p-values <
0.05 for all three customer reaction variables. For low persuasiveness, p-values were > 0.5 (satisfaction), and > 0.4 (willingness to provide WOM). In line with the above MANOVA results, the p-value for intention to purchase was significant at < .06. With the exception of the variable purchasing intention, the results support Hypothesis 3.
70

Table 4-7: Mean values for customer reactions as dependent variables in experiment 1, interaction persuasiveness x product mix 11
Low Persuasiveness of Reasoning
Biased
Prod.Mix
Balanced
Prod.Mix
Dependent Variables
Satisfaction 1,77
Intention to purchase
1,01
1,89
1,14
Willingness to provide WOM 1,41
0,86
Manipulation Check 1,49
0,77
3,90
1,77
Note: Numbers in italic letters are standard deviations
1,89
0,91
2,30
1,22
1,54
0,89
High Persuasiveness of Reasoning
Biased
Prod.Mix
Balanced
Prod.Mix
2,76
1,41
2,96
1,54
2,31
1,42
1,76
1,09
3,70
1,60
3,59
1,57
2,95
1,62
4,25
1,74
Figure 4-4: Interaction of persuasiveness and product mix in experiment 1; customer reactions as dependent variables 13
4,0
3,5
3,0
2,5
2,0
1,5
1,0
0,5
0,0
Satisfaction
Product Mix
BIASED
Product Mix
BALANCED
Persuasiveness
LOW
Persuasiveness
HIGH
71

Figure 4-4 (cont.): Interaction of persuasiveness and product mix in experiment 1; customer reactions as dependent variables 14
4,0
3,5
3,0
2,5
2,0
1,5
1,0
0,5
0,0
Purchasing
Intention
Product Mix
BIASED
Product Mix
BALANCED
Persuasiveness
LOW
Persuasiveness
HIGH
3,5
3,0
2,5
2,0
1,5
1,0
0,5
0,0
Willingness to provide WOM
Product Mix
BIASED
Product Mix
BALANCED
Persuasiveness
LOW
Persuasiveness
HIGH
After the first section of the analysis had investigated a) the direct relationships between cues and customer reactions and b) the interaction among cues, the second part focused on the mediating role of customer-oriented attributions. In order to validate the corresponding hypotheses 4, 5 and 6, initial tests addressed
72

the direct influence of the three cues on customer-oriented attributions and, again, potential interaction effects. Subsequent testing then incorporated a stepwise analysis of all three sets of variables – cues, attributions and reactions.
Effect of persuasiveness on customer-oriented attributions.
The conceptual model outlined in Chapter 2 postulates that the three behavioral cues not only will influence customer reactions, but are also likely to trigger attributional thinking. A three-factor unvariate analysis of variance (ANOVA) was therefore used to investigate whether main and interaction effects comparable to those found with customer reactions also occurred with ‘customer-oriented attributions’ as the dependent variable. The results of the first experiment (Table 4-8) show that persuasiveness of reasoning indeed had substantial influence on customeroriented attributions (F attr
(1,238) = 20.93, p < .001): Respondents were more likely to attribute favorable motives behind the advisor’s actions when persuasiveness was high (M
HI Persuas
= 2.78) than when it was low (M
LO Persuas
= 2.25).
Table 4-8: Results of a univariate analysis in experiment 1; customeroriented attributions as dependent variables 12
F(1, 238) p
Persuasiveness (High / Low)
Proactiveness (High / Low)
Product mix (Balanced / Biased)
Persuasiveness x Proactiveness
Persuasiveness x Product Mix
20.93
13.06
27.04
8.79
0.03 p < .001 p < .001 p < .001 p < .004 p > .85
Moderation of proactiveness and product mix.
Yet again, the data suggests a significant interaction between persuasiveness of reasoning and proactiveness
(F attr
(1,238) = 8.79, p < .004). Under high persuasiveness, more favorable attributions were triggered when proactiveness was high than when it was low (M
HI
73

Proact
= 3.16; M
LO Proact
= 2.44). When persuasiveness was low, no such effect occurred (M
HI Proact
= 2.27; M
LO Proact
= 2.20). Planned contrasts yielded values of t(118) = -3.75, p < .001) for high persuasiveness, and t(120) = -0.43, p > .66) for low persuasiveness.
In the case of product mix, no such moderation could be demonstrated: Product mix always had a significant influence on customer oriented attributions, independent of the level of persuasiveness (F attr
(1,238) = 0.03, p > .85). Mean values were: high persuasiveness, M
Balanced Mix
= 3.07; M
Biased Mix
= 2.54; low persuasiveness, M
Balanced Mix
= 2.56; M
Biased Mix
= 1.93. Mean values for all three effects can also be found in table 4-9.
A t-test delivered values of t(118) = -2.66, p < .01 for high persuasiveness and t(120) = -4.48, p < .001 for low persuasiveness. The interactions are illustrated in Figure 4-5.
Table 4-9: Mean values for customer-oriented attributions as dependent variable in experiment 1 13
Dependent Variables
Customer-oriented attrib.
Low Persuasiveness High Persuasiveness
2,24
0,83
2,82
1,11
Low Persuasiveness High Persuasiveness
Low Proactiveness
2,20
0,84
High Proactiveness
2,27
0,83
Low Proactiveness
2,44
1,00
High Proactiveness
3,16
1,09
Low Persuasiveness High Persuasiveness
Biased
Prod.Mix
Balanced
Prod.Mix
1,93
0,68
2,56
0,85
Note: Numbers in italic letters are standard deviations
Biased
Prod.Mix
2,54
1,02
Balanced
Prod.Mix
3,07
1,13
74

Figure 4-5: Interaction of persuasiveness and proactiveness / product mix; customer-oriented attributions as dependent variable 15
3,5
3,0
2,5
2,0
1,5
1,0
0,5
0,0
Proactiveness
Proactiveness
LOW
Proactiveness
HIGH
Persuasiveness
LOW
Persuasiveness
HIGH
2,0
1,5
1,0
0,5
0,0
3,5
3,0
2,5
Product Mix
Product Mix
BIASED
Product Mix
BALANCED
Persuasiveness
LOW
Persuasiveness
HIGH
Mediation of persuasiveness through customer-oriented attributions.
The results of Experiment 1 suggest a significant main effect of persuasiveness on both customer reactions and customer-oriented attributions. At the same time, it has been
75

hypothesized that customer-oriented attributions are likely to mediate the influence of cues on customer reactions. In order to examine if customer-oriented attributions mediate the impact of persuasiveness, a further analysis was undertaken. The procedure followed the recommendations from Baron and Kenny
(1986) who apply a sequence of regression analyses to test for such effects.
Firstly, persuasiveness (dummy variable: low=0, high=1) had an impact on the dependent variables ( ȕ satisf
= .48, p < .001; ȕ purchase
= .40, p < .001; ȕ
WOM
= .42, p
<.001). Secondly, persuasiveness was also related to customer-oriented attributions ( ȕ attr
= .29, p < .001). Thirdly, customer-oriented attributions were a significant predictor of the dependent variables ( ȕ satisf
= .66, p < .001; ȕ purchase
=
.60, p < .001; ȕ
WOM
= .68, p <.001). Lastly, when both persuasiveness and customer-oriented attributions were included in the regression model, the mediator remained a significant predictor ( ȕ satisf
= .57, p < .001; ȕ purchase
= .53, p
< .001; ȕ
WOM
= .61, p <.001), whereas the impact of the independent variable decreased ( ȕ satisf
= .32, p < .001; Sobel: z = 4.27; ȕ purchase
= .24, p < .001; Sobel: z
= 4.18; ȕ
WOM
= .25, p <.001; Sobel: z = 4.31). Thus, H4 is confirmed. The results are summarized in Figure 4-6.
Figure 4-6: Mediation of persuasiveness through customer-oriented attributions in experiment 1 16
0.29
Customeroriented attributions
Satisfaction: 0.57 (0.66)
Puchasing Int.: 0.53 (0.60)
WOM: 0.61 (0.68)
Persuasiveness of reasoning
Customer reactions
Satisfaction: 0.32 (0.48)
Puchasing Int.: 0.24 (0.40)
WOM: 0.25 (0.42)
Note: The total effect between the predictor and the criterion (i.e., before controlling for the mediator) is given in parentheses; the direct effect (i.e., after controlling for the mediator) is given outside the parentheses. p was significant at < .001 level
76

Mediation of proactiveness through customer-oriented attributions.
The above results suggest that the influence of persuasiveness on customer reactions is substantially mediated by customer-oriented attributions. The following section investigates whether a similar effect occurs for the influence of proactiveness and product mix. Previously, it has been demonstrated that proactiveness and product mix only lead to more favorable customer reactions when persuasiveness is high. Hence, if the influence of proactiveness and product mix should also be mediated by customer-oriented attributions, this can only be the case under conditions of high persuasiveness – otherwise, there is no significant effect to be mediated. In order to validate Hypotheses 5 and 6, two separate analyses were performed, splitting the sample into high (n=117) and low persuasiveness (n=119) groups. Then, the same approach that had been used to test for a mediation of persuasiveness was used on proactiveness. Firstly, proactiveness (dummy variable: low=0, high=1) had an impact on the dependent variables when persuasiveness was high ( ȕ satisf
= .42, p < .001; ȕ purchase
= .39, p <
.001; ȕ
WOM
= .42, p <.001), but had no such effect under low persuasiveness
( ȕ satisf
= -0.01, p > 0.9; ȕ purchase
= -0.01, p > 0.9; ȕ
WOM
= -0.07, p > 0.4). It should be noted that only under conditions of high persuasiveness, proactiveness was also related to customer-oriented attributions, a prerequisite for any mediation through this variable (low persuasiveness: ȕ attr
= 0.04, p > .60; high persuasiveness: ȕ attr
=.33, p < .001). Thirdly, customer-oriented attributions were always a significant predictor of the dependent variables (low persuasiveness: ȕ satisf
=.54, p
< .001; ȕ purchase
=.51, p < .001; ȕ
WOM
=.49, p <.001; high persuasiveness: ȕ satisf
=.66, p < .001; ȕ purchase
=.59, p < .001; ȕ
WOM
=.72, p <.001).
Lastly, when both proactiveness and customer-oriented attributions were included in the regression model, the mediator remained a significant predictor
(low persuasiveness: ȕ satisf
= .54, p < .001; ȕ purchase
= .51, p < .001; ȕ
WOM
= .50, p
< .001; high persuasiveness: ȕ satisf
= .58, p < .001; ȕ purchase
= .52, p < .001; ȕ
WOM
=
.65, p < .001), whereas the impact of the independent variable was diminished
(low persuasiveness: ȕ satisf
= -0.03, p > .7; Sobel: z = .43; ȕ purchase
= -0.03, p > .70;
77

Sobel: z = .43; ȕ
WOM
=. -0.09, p > .25; Sobel: z = .43; high persuasiveness: ȕ satisf
= .23, p < .002; Sobel: z = 3.42; ȕ purchase
= .22, p < .007; Sobel: z = 3.28; ȕ
WOM
=
.20, p <.005; Sobel: z = 3.52). The results are illustrated in Figure 4-7.
Consistent with H
5
, it can be concluded that under high persuasiveness, the effect of proactiveness on customer reactions is mediated by customer-oriented attributions.
Figure 4-7: Mediation of proactiveness through customer-oriented attribution
( under conditions of high persuasiveness) in experiment 1 17
0.33
Customeroriented attributions
Satisfaction: 0.58 (0.66)
Puchasing Int.: 0.52 (0.59)
WOM: 0.65 (0.72)
Proactiveness
Customer reactions
Satisfaction: 0.23* (0.42)
Puchasing Int.: 0.22** (0.39)
WOM: 0.20* (0.42)
Note: The total effect between the predictor and the criterion (i.e., before controlling for the mediator) is given in parentheses; the direct effect (i.e., after controlling for the mediator) is given outside the parentheses. p was significant at < .001 level., < .005 (*) or .01 (**)
Mediation of product mix through customer-oriented attributions.
A final analysis examined if customer-oriented attributions also mediated the impact of product mix under high persuasiveness conditions. Again, it was necessary to perform separate analyses for the two persuasiveness conditions since the relationship between product mix and customer reactions should be positive in the high persuasiveness and insignificant in the low persuasiveness conditions.
Firstly, product mix (dummy variable: low=0, high=1) had an impact on the dependent variables (low persuasiveness: ȕ satisf
= .06, p > .50; ȕ purchase
= .17, p <
78

.06; ȕ
WOM
= .08, p > .40; high persuasiveness: ȕ satisf
= .30, p < .002; ȕ purchase
= .20, p < .03; ȕ
WOM
= .21, p <.03). This influence only occurred under high persuasiveness, with the exception of purchasing intention, on which product mix always has an effect. Both findings are in line with previous evidence discussed in the context of Hypothesis 3. Secondly, product mix was also related to customer-oriented attributions (low persuasiveness: ȕ attr
=.38, p <.001; high persuasiveness: ȕ attr
=.24, p < .01). In congruence with earlier ANOVA results, the significance of this relationship was not contingent upon the level of persuasiveness. Thirdly, customer-oriented attributions were always a significant predictor of the dependent variables (low persuasiveness: ȕ satisf
= .54, p < .001; ȕ purchase
= .51, p < .001; ȕ
WOM
= .49, p <.001; high persuasiveness: ȕ satisf
= .66, p < .001; ȕ purchase
= .59, p < .001; ȕ
WOM
= .72, p <.001). Lastly, when both product mix and customer-oriented attributions were included in the regression model, the mediator remained a significant predictor (low persuasiveness: ȕ satisf
=.61, p < .001; ȕ purchase
=.52, p < .001; ȕ
WOM
=.54, p <.001; high persuasiveness: ȕ satisf
= .62, p < .001; ȕ purchase
= .57, p < .001; ȕ
WOM
= .71, p <.001), whereas the impact of the independent variable decreased (low persuasiveness: ȕ satisf
= -
0.17, p < .04; Sobel: z = 3.81; high persuasiveness: ȕ satisf
=.15, p < .04; Sobel: z
= 2.54) or was eliminated (low persuasiveness: ȕ purchase
= -.02, p > .70; Sobel: z =
3.59; ȕ
WOM
= -.13, p > .12; Sobel: z = 3.66; High persuasiveness: ȕ purchase
= .06, p
> .40; Sobel: z = 2.49; ȕ
WOM
= .04, p >.50; Sobel: z = 2.58). The results show that under conditions of high persuasiveness, the effect of product mix on customer reactions is partially (satisfaction) or fully (purchasing intention and
WOM willingness) mediated by customer-oriented attributions. Thus, H
6
is confirmed.
79

Figure 4-8: Mediation of product mix through customer-oriented attributions
(under conditions of high persuasiveness) in experiment 1 18
0.24**
Customeroriented attributions
Satisfaction: 0.62 (0.66)
Puchasing Int.: 0.57 (0.59)
WOM: 0.71 (0.72)
Product mix
Customer reactions
Satisfaction: 0.15* (0.30)
Puchasing Int.: n.s.*** (0.20)*
WOM: n.s. *** (0.21)*
Note: The total effect between the predictor and the criterion (i.e., before controlling for the mediator) is given in parentheses; the direct effect (i.e., after controlling for the mediator) is given outside the parentheses. p was significant at < .002 level., < .04 (*) or .01 (**).
Insignificant p was > .40 (***)
4.2.5
Discussion
Study 1 indicated that in an open-architecture sales context, the persuasiveness of reasoning, the proactiveness in offering third-party products and the mix of recommended products are powerful cues that are perceived by clients and affect their reactions. While persuasiveness of reasoning has a significant main effect on customer reactions, it also moderates the influence of the other two cues. Proactiveness and product mix only had a positive effect on customer reactions when at the same time persuasiveness was high. One exception must be noted for the dependent variable purchasing intention, for which no significant interaction between persuasiveness and product mix could be found.
Of the three customer reaction variables, the intention to purchase would seem to have the most immediate link to the investment decisions and risks that a client ultimately takes. Hence, it seems plausible that with regard to the actual purchasing intention, a “balanced” product mix – and the risk diversification it
80

may promise – is of substantial relevance to a client, independent of other factors, such as a convincing reasoning of the advisor.
The results from Study 1 also provide first empirical evidence of the mediating function that attributions seem to exert. The results suggest a substantial influence of all three cues on customer-oriented attributions, which in turn have a strong effect on the different customer reactions. The effect of persuasiveness on customer reactions shrinks significantly upon the addition of customeroriented attributions as mediator. For proactiveness and product mix, this effect is contingent on the level of persuasiveness and can achieve full mediation in some cases. Taken together, Experiment 1 provides satisfactory evidence to support all six hypotheses.
4.3.1
Design, Participants and Procedure
As in the previous research, Experiment 2 employed an online, scenario-based setup using a 2 (persuasiveness high/low) x 2 (proactiveness high/low) x 2
(product mix balanced/biased) factorial design. With the help of a professional market research agency, a sample of 260 responses was gathered from participants recruited through an online panel representative of the German population.
49.2% of the test subjects were male, 50.8% were female. 32% were less than 30 years old, 46.6% were between the ages of 30 and 50 and 21.4% were over the age of 50. The relative size of the age groups illustrates that the sample was remarkably different from the one used in Study 1. The sample distribution across the eight experimental cells ranged from a low of 29 (11.2%) to a high of
39 (15%) participants. All participants received an email including a link to the online survey homepage. They were also informed that the completion of the questionnaire would be rewarded with an amount of 2.50 EUR.
81

4.3.2
Manipulation of Independent Variables
Manipulation of the variables was similar to Experiment 1. This time, however, the treatment was presented in form of a video clip embedded into the online questionnaire. As stimulus material, video clips in 8 different combinations of high/low persuasiveness, high/low proactiveness and balanced/biased product mix were produced. Filming of the sales encounters took place in the client meeting room of a local highstreet bank, an environment sufficiently similar to the branch of an insurance company. Adding to the realism, the insurance agent wore a business suit and tie; different insurance brochures were spread out on a nearby sideboard. The premise of the video was that a customer had visited an insurance agent and they had talked about the customer’s intention to take out a life insurance policy. He was now sitting in the agent’s office waiting for the agent to recommend a number of options. Similar to the approach taken in
Experiment 1, all participants were exposed to a short introduction text in order to prepare the test subjects for the experimental scenario. The text explained the situation and introduced the idea of an open-architecture offering to the participants. A main objective was to make participants' expectations salient and to improve their receptiveness to the specific stimuli of the experiment. The text read as follows:
Mr. Staiger has decided to put some money aside by taking out a life insurance. It's important to him to choose the insurance product most appropriate for his needs. Therefore, he's made an appointment with the local agency of a large insurance company. A while ago, Mr. Staiger has read that some insurance firms offer a so-called "open product architecture". Their customers can buy the firm's own-label insurance policies, but also those of third-party providers. He intends to find out whether his insurance company will also offer such third-party products.
After Mr. Müller, the insurance agent, has welcomed him at his office, the two of them discuss Mr. Staiger's financial requirements and expectations for a while. Then, Mr. Müller suggests to present his customer with a selection of insurance products that will meet his requirements.
82

Both the customer and agent were male and approximately thirty-five years of age. The customer did not vary his reactions across the video clips but the agent did in order to simulate the eight experimental conditions. The video mainly showed the insurance agent from the waist up, talking to the customer, or standing in front of a sideboard, sifting through different insurance brochures. In order to minimize the influence of the on-screen customer on the participants, his script was limited to few vocal cues, and his tone of voice was kept sufficiently neutral. The video scenarios were enacted and re-filmed a number of times until both actors’ behavior was consistent and realistic within each scene.
The content of the eight clips was identical exclusive of the cue manipulation.
Each of the eight movies consists of three scenes. The first scene starts out identically for both conditions: Mr. Müller, the insurance agent, concludes a conversation he has had with his client before. He suggests selecting a few product alternatives that will suit the client's requirements. After a cut, the clip shows the agent standing in front of a sideboard, browsing through different product brochures.
Figure 4-9: Screenshot of the experimental video clip 19
83

In condition one, high proactiveness, Mr. Müller turns around, facing the customer, and mentions that his firm has an open-architecture offering and explains that this means the customer can also purchase third-party products.
The agent suggests including some of these in his recommendation. In condition two – low proactiveness – Mr. Müller turns around, pointing out that his firm has "great" in-house products and that he's picking a few of those. On that remark, the customer, Mr. Staiger, nods his approval, but asks explicitly whether the agent can also include some third-party products in his selection. Mr. Müller seems surprised, but after a short hesitation agrees to satisfy this request. In scene two, the sales agent sits down at the table again and spreads out six brochures in front of his client. Under the condition of balanced product mix, he explains that three of the leaflets are about in-house products, while the other three promote policies from other insurance companies. Under the condition of biased product mix, five out of six brochures are in-house material and the advisor just points his finger at a sixth one which is for a competitor product.
Finally, in scene three, the customer asks the insurance agent to explain in more detail why he has picked the specific products which are on the table. In condition one, high persuasiveness of reasoning, the agent provides an elaborate and clear explanation of some of the key differences among the products. In condition two, the agent seems evasive, argues that the strategy behind his pick would be "difficult to explain" and uses a lot of jargon. He does not explain the different pros and cons of each product, but assures that he's got a "good feeling" about the selection he has recommended.
Following an introduction on how to proceed in the online survey environment, each participant was randomly assigned to one of the eight video treatments.
The survey questionnaire was identical to the one used in Experiment 1, with only few adaptations to the different industry (i.e., “insurance policies” instead of “investment funds”, “insurance agent” instead of “advisor”). After watching the video clip, participants were asked to respond to several measures of attributions, perceptions and behavioral intentions (see Figure 4-10).
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Figure 4-10: Structure of online experiment 2 20
General information and handling of the questionnaire
Exposure to video clip of a customerclient interaction
Measurement of attributions
Measurement of customer reactions and other variables
4.3.3
Selection of Measures
4.3.3.1
Dependent Measures
In order to ensure comparability of results, Experiment 2 employed the same measures for all dependent variables as Experiment 1, listed in Table 4-10.
Customer-oriented attributions were measured with five items, satisfaction, purchasing intention and willingness to provide WOM were measured with three items each. Their wording had to be slightly adapted to the different experimental setup: While in the written scenario of Experiment 1, the participant had been addressed as the client, he or she observed an interaction between a client and a salesperson in Experiment 2. Consequently, the participants were asked to evaluate the scenario as if this episode had happened to them. The individual measurement items were therefore phrased in subjunctive form (e.g., “I would be satisfied with this service” rather than “I am satisfied”).
4.3.3.2
Manipulation Checks
As previously, the manipulation checks each featured two seven-point scales rated by participants. The two items used for assessing the persuasiveness of the insurance agent ( “Mr. Müller made an effort to explain his recommendations well”, “The reasoning Mr. Müller gave for his policy selection was hardly
85

convincing.”) yielded a satisfactory reliability level of r = .72. For proactiveness, the statements to rate were “Mr. Müller has offered me third-party policies on his own initiative” and “only at my request, Mr. Müller has offered me policies that were not from his own insurance firm” . The level of reliability (r =
.89) was good. Finally, in order to measure the manipulation of product mix, the participants assessed the statements “the agent has offered me a balanced mix of in-house and external products ” and “products of his own insurance company dominated the agent’s product selection” (r = .75). All items were anchored by applies very much / does not apply at all .
The different dependent variables and manipulation checks and their reliability measures are listed in Table 4-9.
Table 4-10: Overview of measures used in experiment 2 14
Measure
Covariates
Age
Manipulation Check
Persuasiveness
Proactiveness
Product mix
Number of Items Reliability Source
Dependent Variables
Customer-oriented attrib.
Satisfaction
Intention to purchase
5
3
3
Willingness to provide WOM 3
2
2
2
α
α
α
α
= .90
= .96
= .98
= .99
r = .72
r = .89
r = .75
Hui et al (2004)
DeCarlo (2005)
Maxham III and
Netemeyer (2002)
86

4.3.4
Results
4.3.4.1
Manipulation Checks and Item Reliability.
Manipulation check results showed that the experimental manipulation had worked for all three different cues. The insurance agent’s reasoning was considered more persuasive under conditions of high persuasiveness than under low persuasiveness (M
HighPers
= 3.95, M
LowPers
= 2.31, F(1,260) = 78.79, p <
.001). Other than expected, a lower, but still significant effect was also revealed for the other two independent variables, proactiveness (F(1,260) = 8.46, p <
.005) and product mix (F(1,260) = 5.84, p < .017). Similarly, the manipulation was influenced by interaction effects of proactiveness/ persuasiveness (F(1,260)
= 3.92, p < .05) and product mix/persuasiveness (F(1,260) = 5.85, p < .017). No such effects were measured for the interaction of proactiveness/product mix
(F(1,260) = .10, p > .75) and the interaction among all three variables (F(1,260)
= 2.09, p > .14).
A second manipulation check revealed that respondents in the high proactiveness condition reported a considerably higher value for proactiveness than those in the low proactiveness condition (M
HighProact
= 5.50, M
LowProact
=
1.91, F(1,260) = 421.52, p < .001). Again, some significant effects were also measured for the independent variable product mix (F(1,260) = 4.67, p < .04) and for the interactive effect of product mix and proactiveness (F(1,260) = 3.25, p < .08). The data suggest no significant influence of persuasiveness (F(1,260) =
.50, p > .48), or of the interactions of persuasiveness/product mix (F(1,260) =
2.63, p > .10), persuasiveness/proactiveness (F(1,260) = 2.45, p > .11) and the interaction of all three independent variables (F(1,260) = 1.21, p > .27).
Finally, respondents who had received a scenario treatment with balanced product mix reported a greater value for balanced mix than those exposed to the biased mix condition (M
BalancedMix
= 4.29, M
BiasedMix
= 2.12, F(1,260) = 182.28, p
< .001). As before, it was tested whether the other independent variables, persuasiveness or proactiveness, or any interactions among the independent
87

variables had an effect on the manipulation check. The main effect of proactiveness (F(1,260) = 48.95, p < .001) was significant, as were the interaction effect of proactiveness/product mix (F(1,260) = 6.10, p < .014) and the interaction of all three independent variables (F(1,260) = 3.52, p < .07). No significance was measured for the the main effect of persuasiveness (F(1,260) = .34, p > .56), or the interaction effects of persuasiveness/proactiveness (F(1,260) = 2.27, p > .13) and persuasiveness/product mix (F(1,260) = .11, p > .73). Overall, the manipulations in Experiment 2 must be deemed sufficiently effective, but not as precise as desired.
Item reliability was assessed using the Cronbach’s alpha coefficient, which yielded satisfactory values for the construct “customer-oriented attributions”
(0.90), for satisfaction (0.96), purchasing intention (0.98) and willingness to provide WOM (0.99).
Due to the multiple dependent variables, a multivariate ANOVA was used to test for the main and interactive effects of cues on customer perceptions and intentions. Age was included as a covariate, since persuasion knowledge may affect customers’ attributional thinking and the extent and sophistication of customers’ persuasion knowledge has been argued to grow over their lifetime
(Friestad and Wright 1994). Correspondingly, younger customers have been found to employ different and fewer response strategies to salesperson persuasion than older customers (Kirmani and Campbell 2004).
4.3.4.2
Hypotheses Testing
Effect of persuasiveness on customer reactions.
The results of my second quantitative study confirmed that persuasiveness of reasoning had a positive influence on customers’ satisfaction (F satis
(1,260) = 57.20, p < .001), their intention to purchase (F purchase
(1,260) = 52.93, p < .001), and their willingness to provide word-of-mouth (F
WOM
(1,260) = 53.95, p < .001). These values are also displayed in Table 4-11.
88

Respondents were more satisfied when persuasiveness was high (M satis
= 3.48) than when it was low (M satis
= 2.10). They were also more intent on purchasing
(M purchase
= 3.18) and more willing to provide word-of-mouth (M
WOM
= 2.95) under high persuasiveness than under low persuasiveness (M purchase
= 1.88,
M
WOM
= 1.71). These results provide further support for H
1
and are summarized in Table 4-12.
Table 4-11: Results of multivariate analyses in experiment 2; customer reactions as dependent variable, main effects
15
Dependent Variable F(1, 260)
Persuasiveness of Satisfaction
Reasoning (high/low) Intention to purchase
57.20
52.93
Willingness to provide WOM 53.95
Proactiveness
(high/low)
Satisfaction
Intention to purchase
25.03
15.64
Product Mix
Willingness to provide WOM 23.63
Satisfaction 6.24
(balanced / biased) Intention to purchase
Willingness to provide WOM
1.43
2.51 p p < .001 p < .001 p < .001 p < .001 p < .001 p < .001 p < .014 p > .23 p > .11
Table 4-12: Mean values for customer reactions as dependent variables in experiment 2, main effects 16
Low Persuasiveness High Persuasiveness
Dependent Variables
Satisfaction
Intention to purchase
Mean
2,10
1,39
1,88
Manipulation Check
1,36
Willingness to provide WOM 1,71
1,17
2,31
1,42
Note: Numbers in italic letters are standard deviations
Mean
3,48
1,53
3,18
1,49
2,95
1,56
3,95
1,59
89

Moderation of proactiveness.
Similar to experiment 1, the data of the second experiment highlight a significant interaction between persuasiveness of reasoning and proactiveness for all three variables (F satis
(1,260) = 6.63, p < .012;
F purchase
(1,260) = 8.11, p < .006; F
WOM
(1,260) = 9.37, p < .003). The values are also listed in Table 4-13.
Table 4-13: Results of multivariate analyses in experiment 2; customer reactions as dependent variables, interaction effects 17
Dependent Variable F(1, 260) p
Persuasiveness x Satisfaction
Proactiveness Intention to purchase
Willingness to provide WOM
Persuasiveness x Satisfaction
Product Mix Intention to purchase
Willingness to provide WOM
6.63 p < .012
8.11 p < .006
9.37 p < .003
3.91 p < .05
8.85 p < .004
12.86 p < .001
As table 4-14 shows, customer reactions were only improved through higher proactiveness when persuasiveness was also high (high proactiveness:
M satis
= 4.08; M purchase
= 3.73; M
WOM
= 3.55; low proactiveness: M satis
= 2.87;
M purchase
= 2.64; M
WOM
= 2.34). These interaction effects are illustrated in Figure
4-11. Under conditions of low persuasiveness, this effect did not occur. Whether proactiveness was high (M satis
= 2.32; M purchase
= 1.98; M
WOM
= 1.86) or low
(M satis
= 1.90; M purchase
= 1.78; M
WOM
= 1.57) made no significant difference.
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Table 4-14: Mean values for customer reactions as dependent variables in experiment 2, interaction persuasiveness x proactiveness 18
Low Persuasiveness High Persuasiveness
Low Proactiveness
High Proactiveness
Low Proactiveness
High Proactiveness
Dependent Variables
Satisfaction
Mean
1,90
Intention to purchase
Manipulation Check
1,30
1,78
1,34
Willingness to provide WOM 1,57
1,08
1,96
1,46
Mean
2,32
1,46
1,98
1,38
1,86
1,26
5,29
1,55
Note: Numbers in italic letters are standard deviations
Mean
2,87
1,35
2,64
1,40
2,34
1,40
1,86
1,47
Mean
4,08
1,46
3,73
1,39
3,55
1,47
5,69
1,21
Figure 4-11: Interaction of persuasiveness and proactiveness in experiment 2; customer reactions as dependent variables 21
4,5
4,0
3,5
3,0
2,5
2,0
1,5
1,0
0,5
0,0
Satisfaction
Proactiveness
LOW
Proactiveness
HIGH
Persuasiveness
LOW
Persuasiveness
HIGH
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Figure 4-11(cont.): Interaction of persuasiveness and proactiveness in experiment 2; customer reactions as dependent variables 22
4,0
3,5
3,0
2,5
2,0
1,5
1,0
0,5
0,0
Persuasiveness
LOW
Persuasiveness
HIGH
Purchasing
Intention
Proactiveness
LOW
Proactiveness
HIGH
4,5
4,0
3,5
3,0
2,5
2,0
1,5
1,0
0,5
0,0
Persuasiveness Persuasiveness
LOW HIGH
Willingness to provide WOM
Proactiveness
LOW
Proactiveness
HIGH
92

For high persuasiveness, a t-test of contrast effects yielded two-tailed p-values <
0.001 for all three customer reaction variables. For low persuasiveness, p-values were > 0.09 (satisfaction), > 0.4 (purchasing intention) and > 0.16 (willingness to provide WOM). The findings are fully consistent with H
2 and reconfirm that the effect of proactiveness on customer reactions is contingent upon the level of persuasiveness.
Moderation of product mix.
Hypothesis 3 postulated that the effect of product mix on customer reactions was similarly contingent upon the level of persuasiveness. Indeed, as table 4-13 shows, experiment 2 yielded further evidence that this cue’s influence on satisfaction, purchasing intention and willingness to provide WOM was moderated by persuasiveness (F satis
(1,260) = 3.91, p < .05;
F purchase
(1,260) = 8.85, p < .004), F
WOM
(1,260) = 12.86, p < .001). Product mix had a positive effect on customer reactions only when persuasiveness of reasoning was high (balanced product mix: M satis
= 3.78; M purchase
= 3.47; M
WOM
= 3.28; biased product mix: M satis
= 3.11; M purchase
= 2.84; M
WOM
= 2.54). Under low persuasiveness, a similar interaction could not be observed (balanced product mix: M satis
= 2.15; M purchase
= 1.74; M
WOM
= 1.56; biased product mix:
M satis
= 2.06; M purchase
= 2.03; M
WOM
= 1.87). These values can also be obtained from Table 4-15.
For high persuasiveness, a t-test of contrast effects yielded two-tailed p-values <
0.05 for satisfaction, purchasing intention and willingness to provide WOM. For low persuasiveness, p-values were > 0.7 (satisfaction), > 0.23 (purchasing intention) and > 0.15 (willingness to provide WOM). Unlike experiment 1, the second experiment delivered consistent results for all three customer reaction variables, further corroborating Hypothesis 3.
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Table 4-15: Mean values for customer reactions as dependent variables in experiment 2, interaction persuasiveness x product mix 19
Low Persuasiveness of
Reasoning
Dependent Variables
Satisfaction
Biased
Prod. Mix
2,05
1,42
Intention to purchase
1,54
Willingness to provide WOM 1,87
1,36
Manipulation Check
2,03
2,08
1,18
Balanced
Prod.Mix
2,15
1,38
1,74
1,15
1,56
0,95
4,20
1,64
Note: Numbers in italic letters are standard deviations
High Persuasiveness of
Reasoning
Biased
Prod. Mix
3,11
1,56
2,84
1,41
2,54
1,41
2,16
1,29
Balanced
Prod.Mix
3,78
1,44
3,47
1,51
3,28
1,60
4,36
1,66
Figure 4-12 : Interaction of persuasiveness and product mix in experiment 2; customer reactions as dependent variables 23
4,0
3,5
3,0
2,5
2,0
1,5
1,0
0,5
0,0
Satisfaction
Product Mix
BIASED
Product Mix
BALANCED
Persuasiveness
LOW
Persuasiveness
HIGH
94

Figure 4-12 (cont.): Interaction of persuasiveness and product mix in experiment 2; customer reactions as dependent variables 24
2,0
1,5
1,0
0,5
0,0
4,0
3,5
3,0
2,5
Purchasing
Intention
Product Mix
BIASED
Product Mix
BALANCED
Persuasiveness
LOW
Persuasiveness
HIGH
3,5
3,0
2,5
2,0
1,5
1,0
0,5
0,0
Persuasiveness
LOW
Persuasiveness
HIGH
Willingness to provide WOM
Product Mix
BIASED
Product Mix
BALANCED
95

Up to this point, the analysis of Experiment 2 investigated a) the direct relationships between cues and customer reactions and b) the interaction among cues. As in the previous experiment, the research proceeded by focusing on the mediating role of customer-oriented attributions. In order to validate the hypotheses 4, 5 and 6, initial tests addressed the direct influence of the three cues on customer-oriented attributions and, again, potential interaction effects.
Subsequent testing then incorporated a stepwise analysis of all three sets of variables – cues, attributions and reactions.
Effect of persuasiveness on customer-oriented attributions.
In preparation of testing the hypotheses 4 to 6, the next analysis investigated whether the three behavioral cues had similar effects on “customer-oriented attributions” as the dependent variable. As in the previous experiment, the results (Table 4-16) suggest a significant relationship between persuasiveness and customer-oriented attributions (F attr
(1,260) = 59.76, p < .001). Respondents were more likely to attribute favorable motives behind the advisor’s actions when persuasiveness was high (M
HI Persuas
= 3.40) than when it was low (M
LO Persuas
= 2.29).
Table 4-16: Results of a univariate analysis in experiment 2; customeroriented attributions as dependent variable 20
F(1, 260) p
Persuasiveness (High / Low)
Proactiveness (High / Low)
Product mix (Balanced / Biased)
Persuasiveness x Proactiveness
Persuasiveness x Product Mix
59.76
33.23
1.10
9.66
3.73 p < .001 p < .001 p > .29 p < .003 p < .06
Moderation of proactiveness and product mix.
The initial results from an univariate analysis of variances suggest a significant interaction between persuasiveness of reasoning and proactiveness (F attr
(1,260) = 9.66, p < .003).
Under high persuasiveness, more favorable attributions were triggered when
96

proactiveness was high than when it was low (M
HI Proact
= 3.97; M
LO Proact
= 2.82).
When persuasiveness was low, the effect was considerably smaller, but still significant (M
HI Proact
= 2.49; M
LO Proact
= 2.12). T-testing delivered values for high persuasiveness t(136) = -6.10, p < .001) and low persuasiveness t(124) =
-1.90, p < .06).
Results also suggest a smaller, but still significant moderation of product mix through persuasiveness (F attr
(1,260) = 3.73, p < .06), be it under conditions of high persuasiveness (M
Balanced Mix
= 3.55; M
Biased Mix
= 3.22), or under low persuasiveness (M
Balanced Mix
= 2.23; M
Biased Mix
= 2.36). However, such an influence is not supported by the planned contrasts that were performed subsequently. Product mix had no significant influence on customer oriented attributions, be it under conditions of high persuasiveness (t(136) = -1.55, p >
.12), or under low persuasiveness (124) = 0.62, p > .54). The mean values are listed in Table 4-17, the interaction effects are illustrated in Figure 4-13.
Table 4-17: Mean values for customer-oriented attributions as dependent variables in experiment 2 21
Dependent Variables
Customer-oriented attrib.
Low Persuasiveness High Persuasiveness
2,29 3,40
1,10 1,23
Low Persuasiveness High Persuasiveness
Low Proactiveness
2,12
High Proactiveness
2,49
Low Proactiveness
2,82
High Proactiveness
3,97
1,06 1,12 1,03 1,16
Low Persuasiveness High Persuasiveness
Biased
Prod. Mix
2,36
1,15
Balanced
Prod. Mix
2,23
1,06
Note: Numbers in italic letters are standard deviations
Biased
Prod. Mix
3,22
1,31
Balanced
Prod. Mix
3,55
1,15
97

Figure 4-13: Interaction of persuasiveness and proactiveness / product mix in experiment 2; customer-oriented attributions as dependent variable 25
4,5
4,0
3,5
3,0
2,5
2,0
1,5
1,0
0,5
0,0
Proactiveness
Proactiveness
LOW
Proactiveness
HIGH
Persuasiveness
LOW
Persuasiveness
HIGH
4,0
3,5
3,0
2,5
2,0
1,5
1,0
0,5
0,0
Product Mix
Product Mix
BIASED
Product Mix
BALANCED
Persuasiveness
LOW
Persuasiveness
HIGH
Mediation of persuasiveness through customer-oriented attributions.
After the previous tests had confirmed significant relationships between the three cues on one side and customer-oriented attributions on the other side, experiment 2 proceeded analogue to the first experiment. The potential role of attributions as
98

mediator was investigated through a series of regression analyses, as proposed by Baron and Kenny (1986). The results, as illustrated in Figure 4-14, provide further support for H
4
which forwards the idea that customer-oriented attributions mediate the influence of persuasiveness on customer reactions.
Firstly, persuasiveness (dummy variable: low = 0, high = 1) had an impact on the dependent variables ( ȕ satisf
= .43, p < .001; ȕ purchase
= .42, p < .001; ȕ
WOM
=
.41, p <.001). Secondly, persuasiveness was also related to customer-oriented attributions ( ȕ attr
= .43, p < .001). Thirdly, customer-oriented attributions were a significant predictor of the dependent variables ( ȕ satisf
= .74, p < .001; ȕ purchase
=
.77, p < .001; ȕ
WOM
= .73, p <.001). Lastly, when both persuasiveness and customer-oriented attributions were included in the regression model, the mediator remained a significant predictor ( ȕ satisf
= .69, p < .001; ȕ purchase
= .73, p
< .001; ȕ
WOM
= .68, p <.001), whereas the impact of the independent variable decreased ( ȕ satisf
= .13, p < .005; Sobel: z = 6.76; ȕ purchase
= .11, p < .02; Sobel: z
= 6.91; ȕ
WOM
= .12, p <.02; Sobel: z = 6.70). Thus, H4 is confirmed.
Figure 4-14: Mediation of persuasiveness through customer-oriented attributions in experiment 2 26
0.43
Customeroriented attributions
Satisfaction: 0.69 (0.74)
Puchasing Int.: 0.73 (0.77)
WOM: 0.68 (0.73)
Persuasiveness of reasoning
Satisfaction: 0.13* (0.43)
Puchasing Int.: 0.11** (0.42)
WOM: 0.12** (0.41)
Customer reactions
Note: The total effect between the predictor and the criterion (i.e., before controlling for the mediator) is given in parentheses; the direct effect (i.e., after controlling for the mediator) is given outside the parentheses. p was significant at < .001 level, < .005 (*) or .02 (**)
99

Mediation of proactiveness through customer-oriented attributions.
The analysis of interaction effects among the three cues had consistently shown in experiments 1 and 2 that proactiveness influenced customer reactions only under conditions of high persuasiveness. In order to test whether the influence of proactiveness on customer reactions was mediated, two separate analyses were performed, splitting the sample into high (n=136) and low persuasiveness
(n=124) groups. The procedure was identical to experiment 1. Firstly, proactiveness (dummy variable: low=0, high=1) had an impact on the dependent variables when persuasiveness was high ( ȕ satisf
= .40, p < .001; ȕ purchase
= .37, p <
.001; ȕ
WOM
= .39, p <.001), but had no such effect under low persuasiveness
( ȕ satisf
= .15, p > 0.09; ȕ purchase
= .07, p > 0.4; ȕ
WOM
= .13, p > 0.16). Hence, no mediation was possible when persuasiveness was low. Proactiveness was also related to customer-oriented attributions (low persuasiveness: ȕ attr
= 0.17, p
<.06; high persuasiveness: ȕ attr
= .47, p < .001). Thirdly, customer-oriented attributions were a significant predictor of the dependent variables (low persuasiveness: ȕ satisf
= .72, p < .001; ȕ purchase
= .78, p < .001; ȕ
WOM
= .73, p
<.001; high persuasiveness: ȕ satisf
= .66, p < .001; ȕ purchase
= .68, p < .001; ȕ
WOM
=
.64, p <.001). Lastly, when both proactiveness and customer-oriented attributions were included in the regression model, the mediator remained a significant predictor (Low persuasiveness: ȕ satisf
= .72, p < .001; ȕ purchase
= .79, p
< .001; ȕ
WOM
= .73, p <.001; High persuasiveness: ȕ satisf
= .61, p < .001; ȕ purchase
=
.65, p < .001; ȕ
WOM
= .58, p <.001), whereas the impact of the independent variable was eliminated (low persuasiveness: ȕ satisf
= .03, p > .60; Sobel: z=1.867; ȕ purchase
= -.06, p > .25; Sobel: z = 1.875; ȕ
WOM
= .003, p > .95; Sobel: z
= 1.868; high persuasiveness: ȕ satisf
= .11, p > .10; Sobel: z = 4.90; ȕ purchase
= .06, p > .39; Sobel: z = 5.08; ȕ
WOM
= .12, p >.10; Sobel: z = 4.80. The relationships are depicted in Figure 4-15. Consistent with H
5
, it can be concluded that under high persuasiveness, the effect of proactiveness on customer reactions is fully mediated by customer-oriented attributions.
100

Figure 4-15: Mediation of proactiveness through customer-oriented attributions (under conditions of high persuasiveness) in experiment 2 27
0.47
Customeroriented attributions
Satisfaction: 0.61 (0.66)
Puchasing Int.: 0.65 (0.68)
WOM: 0.58 (0.64)
Proactiveness
Customer reactions
Satisfaction: n.s.* (0.40)
Puchasing Int.: n.s.** (0.37)
WOM: n.s.* (0.39)
Note: The total effect between the predictor and the criterion (i.e., before controlling for the mediator) is given in parentheses; the direct effect (i.e., after controlling for the mediator) is given outside the parentheses. p was significant at < .001 level. And not significant at >.10
(*) or .375 (**)
Mediation of product mix through customer-oriented attributions.
A final analysis examined if customer-oriented attributions also mediated the impact of product mix. Again, it was necessary to perform separate analyses for the two persuasiveness conditions since the relationship between product mix and customer reactions should be positive in the high persuasiveness and insignificant in the low persuasiveness conditions. As expected, the impact of product mix (dummy variable: low=0, high=1) on the dependent variables was contingent on the level of persuasiveness (low persuasiveness: ȕ satisf
=.04, p >
.70; ȕ purchase
= -0.11, p > .2; ȕ
WOM
= -0.13, p > .15; high persuasiveness: ȕ satisf
=.22, p < .01; ȕ purchase
=.21, p < .015; ȕ
WOM
=.24, p <.006). Hence, only under high persuasiveness, a first critical prerequisite of any mediation was given.
However, the regression analysis yielded little evidence for any relation between product mix and customer-oriented attributions (low persuasiveness: ȕ attr
= -
0.06, p >.50; high persuasiveness: ȕ attr
=.13, p > .125). This is in line with the planned contrasts performed earlier which found no significant relationship
101

between product mix and customer-oriented attributions. Customer-oriented attributions were a significant predictor of the dependent variables (low persuasiveness: ȕ satisf
= .72, p < .001; ȕ purchase
= .78, p < .001; ȕ
WOM
= .73, p
<.001; high persuasiveness: ȕ satisf
=.66, p < .001; ȕ purchase
=.68, p < .001; ȕ
WOM
=.64, p <.001). Lastly, when both product mix and customer-oriented attributions were included in the regression model, the mediator remained a significant predictor (low persuasiveness: ȕ satisf
=.73, p < .001; ȕ purchase
=.78, p <
.001; ȕ
WOM
=.72, p <.001; high persuasiveness: ȕ satisf
=.64, p < .001; ȕ purchase
=.67, p < .001; ȕ
WOM
=.62, p <.001), whereas the impact of the independent variable was diminished (low persuasiveness: ȕ satisf
= 0.08, p > .20; Sobel: z= -.62; ȕ purchase
= -.06, p > .25; Sobel: z=-.62; ȕ
WOM
= -.09, p > .15; Sobel: z=-.62; high persuasiveness: ȕ satisf
=.14, p < .04; Sobel: z=1.53; ȕ purchase
=.12, p <.06; Sobel: z=1.53; ȕ
WOM
=.16, p <.02; Sobel: z=1.53). The insignificance of the relationship between product mix and customer-oriented attributions seems too marginal to conclude that – with regard to hypothesis 6 – the results of experiment 2 are in sharp contrast of those achieved in experiment 1. After all, all other relationships in the mediation model depicted in Figure 4-16 show values that are in a similar bandwidth to those found in the previous experiment (see also Figure 4-8). In any case, in contrast to Study 1, the results of Study 2 do not support H
6
.
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Figure 4-16: Mediation of product mix through customer-oriented attributions (under conditions of high persuasiveness) in experiment 2 28 n.s.
(p>.125)
Customeroriented attributions
Satisfaction: 0.64 (0.66)
Puchasing Int.: 0.67 (0.68)
WOM: 0.62 (0.64)
Product mix
Customer reactions
Satisfaction: 0.14** (0.22)*
Puchasing Int.: 0.12** (0.21)*
WOM: 0.16** (0.24)*
Note: The total effect between the predictor and the criterion (i.e., before controlling for the mediator) is given in parentheses; the direct effect (i.e., after controlling for the mediator) is given outside the parentheses. p was significant at < .001 level., < .015 (*) or .06 (**).
4.3.5
Discussion
Study 2 confirmed that in an open-architecture sales context, the persuasiveness of reasoning, the proactiveness in offering third-party products and the mix of recommended products are powerful cues that are perceived by clients and affect their reactions. Using a different medium for the experimental stimuli and a different services category from Study 1, Study 2 provides consistent results pertaining to the moderating role of persuasiveness on other behavioral cues.
With the exception of H
6
, it also supports the mediation of those cues’ effects through customer-oriented attributions. Thus, it bolsters the generalizability and robustness of the hypotheses forwarded in this dissertation. Two relevant points of difference between the detailed results of the two experiments must be noted.
Firstly, unlike the first experiment, the second one reveals an interaction effect between persuasiveness and product mix for all customer reactions, including purchasing intention. Thus, it provides even broader evidence of the general moderating influence that persuasiveness has on the two other cues. On the other
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hand, no direct influence of product mix on customer-oriented attributions was found. Consequently, the hypothesis that these attributions mediate the effect of product mix on customer reactions is supported by the first, but not the second study.
The research presented in this dissertation focuses on how customers evaluate the motives behind a specific form of product offering and on the reactions they subsequently display. As with almost any experimental research, there will be a wide variety of factors which have not been addressed by the conceptual model but may have a potential influence on these reactions. One of the more obvious factors might be the product expertise that customers themselves bring to the table. A customer’s own expertise with a product or service category has been found to frequently influence attitudes and behaviors that occur in a sales context (e.g., Alba and Hutchinson 1987; de Bont and Schoormans 1995; Rao and
Monroe 1988); often in a moderating role (Bell, Auh, and Smalley 2005; Bell and Eisingerich 2007; Sharma and Patterson 2000). In line with the definition of
Bell, Auh, Smalley (2005, p. 174), expertise is regarded here as "the extent of a customer's prior product knowledge and ability to assess product performance."
Rao and Monroe’s (1988) research suggests that customers’ prior knowledge about a product strongly affects their usage of specific cues when evaluating the product. Sharma and Patterson (2000) argue that the relevance of trust in building the advisor-client relationship decreases with growing expertise on the client's side. This appears plausible: The more confident clients are in their own knowledge, the less they will feel dependent on the advisor – and the advisor’s trustworthiness. Moreover, the attributions that customers with varying levels of product knowledge generate may be different ones. With regard to financial investments, for instance, clients often struggle to evaluate the quality of finan-
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cial advice they have received. One reason for this is that they lack the expertise and experience to compare the potential outcomes of alternative financial planning scenarios (Sharma and Patterson 1999). Clients with considerable knowledge of investment matters may see things differently. For instance, they tend to prioritize aspects of technical service quality in their judgment, as they are capable of evaluating the actual core offering, e.g., the performance of certain investment funds (Bell and Eisingerich 2007).
In order to shed some light on this relationship, additional analyses were performed on the data of experiments 1 and 2. The data sets of both experiments were each separated into two equally sized groups, split at the median of the variable “customer expertise”. Three items measuring customer expertise had been taken from Sharma and Patterson (2000) who had employed them in assessing clients’ product norm experience, but they have also been used to measure client expertise (Bell and Eisingerich 2007). The items focus strongly on the service elements provided through the advisor, while the present experiments revolve around the actual product offering made by the advisor. Therefore the number of items was extended by one that addresses clients’ product familiarity. The resulting 4-item construct yielded alphas of .92/.94 (Experiment 1/2).
Table 4-18: Influence of customer expertise on customers’ attributional thinking and reactions 22
Experiment 1
F(1, 238) p
Experiment 2
F(1, 260) p
Dependent Variables
Customer-oriented attributions
Satisfaction
Intention to purchase
Willingness to provide WOM
0.51
0.69
1.37
2.92
> .47
> .41
> .24
> .08
0.04
0.22
0.45
0.27
> .84
> .63
> .50
> .61
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As can be derived from the values in Tables 4-18 and 4-19, none of the experiments provided any evidence for a significant influence of customer expertise.
Table 4-19: Influence of customer expertise on customers’ attributional thinking and reactions (mean values) 23
Experiment 1 Experiment 2
Low
Expertise
High
Expertise
Low
Expertise
High
Expertise
Dependent Variables
Customer-oriented attributions
Satisfaction
Intention to purchase
Willingness to provide WOM
2.57
1.11
2.61
1.55
2.80
1.54
2.20
1.46
2.48
0.91
2.45
1.41
2.57
1.48
1.90
1.29
2.89
1.16
2.87
1.55
2.63
1.51
2.41
1.47
2.86
1.42
2.77
1.68
2.49
1.63
2.31
1.57
Note: Numbers in italic letters are standard deviations
These results are not necessarily inconsistent with the research cited above.
Those earlier studies have argued, for instance, that customer expertise moderates the influence of technical and functional service quality on satisfaction (Bell et al. 2005; Bell and Eisingerich 2007). The data of the present dissertation do not contradict such findings, but merely suggest that customers’ reaction to the specific salesperson cues proactiveness, product mix and persuasiveness are not influenced by their expertise. Additional thoughts on this matter are discussed in the final chapter of this dissertation.
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This chapter will present a final discussion of the results and implications of this dissertation. It is structured as follows: The first section will briefly reiterate the reasoning, hypotheses and results for the two quantitative studies. The second part will outline the theoretical and managerial contributions of this dissertation, followed by a discussion of the limitations of the studies. The dissertation will conclude by making a number of suggestions for future research.
Selling competitor products through one's proprietary distribution channels would seem a double-edged business strategy. Its obvious risks will need to be outweighed by strong and positive overall customer reactions. Against this background, it is surprisingly difficult to find any consistent and strong evidence of the benefits and implications of an open-architecture offering, both in academic and in managerial literature. Therefore, this dissertation has set out to answer a number of research questions that were formulated in the introductory chapter:
1.
How do customers react towards an open-architecture offering?
Are there specific behavioral cues that salespeople provide during the sales episode that will influence these customer reactions?
2.
To what extent, if any, is such an influence subject to interaction effects among the different cues?
3.
Lastly, is the relationship between salesperson cues and customer reactions mediated by attributions that customers generate in order to explain the salesperson's behavior?
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In order to evaluate whether the present research has provided satisfactory answers to these questions, its main findings will be summarized in the following paragraph. Building on existing consumer research literature, a conceptual model was developed. Firstly, the model postulates that different behaviors displayed by salespeople affect customer reactions towards an openarchitecture offering. It also forwards the idea that among these behavioral cues, interaction effects may occur. Finally, the model claims that the relationship between salesperson cues and customer reactions may not always be direct, but that cues also influence the attributional thinking of customers, which in turn affects their reactions.
A series of qualitative interviews helped to further explore the postulated relationships and to identify specific factors of influence relevant in an openarchitecture context. As one result, the qualitative study yielded a variety of different and distinctive attributions, of which the majority were customer- rather than suspicion-oriented. The latter finding was in line with the assumption that customers’ preference for variety and the counter-intuitiveness of an open product architecture would support a favorable perception of the offering.
Furthermore, the interviews helped to identify three relevant behavioral cues that customers seem to evaluate when making causal inferences about an open product architecture: the proactiveness of the salesperson in offering competitor products, the ‘balance’ of in-house vs. external solutions in the recommended mix of products and, finally, the persuasiveness of the salesperson’s reasoning behind each product. These cues do certainly not represent an exhaustive list of all stimuli that are available to and evaluated by customers over the course of a sales episode. However, they are more specific to the scenario of an openarchitecture offering than others (such as salesperson appearance, similarity or messaging style), and for this reason, they were chosen to be further investigated in this dissertation. Based on findings from the literature review and the qualitative study, a number of hypotheses were forwarded and tested in form of two experiments.
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Experiment 1 demonstrated that in an open-architecture sales context, the persuasiveness of reasoning, the proactiveness in offering third-party products and the mix of recommended products are powerful cues that are perceived by clients and affect their reactions. While persuasiveness of reasoning had a significant direct effect on customer reactions, proactiveness and product mix only had a positive effect on customer reactions when at the same time persuasiveness was high. In other words, if a salesperson disappoints in providing a convincing explanation behind his or her choice, the proactive offering of thirdparty products or a balanced mix of in-house and third-party products will not save the day. These findings support the hypotheses 1, 2 and 3. The results from
Study 1 also suggest that the influence of all three cues on customer reactions is substantially and, in some cases, fully mediated by customer-oriented attributions. For proactiveness and product mix, this effect is contingent on the level of persuasiveness, which is in line with the interaction effects previously highlighted. Consequently, hypotheses 4, 5 and 6 could be accepted. In order to heighten the generalizability of results and demonstrate their robustness, the second experiment investigated the same relationships in another setup. Using a different medium for the experimental stimuli, an alternative services category and a different sampling approach from Study 1, Study 2 provides consistent results pertaining to the influence of the three behavioral cues, the moderating role of reasoning persuasiveness on the other two cues and on the mediation of these effects through customer-oriented attributions. With the exception of H
6
, which was not supported, Study 2 confirms the validity of the first study’s findings. A final analysis was performed in order to investigate a potential influence of customers’ own expertise (with investment funds / insurance) on customer attributions and reactions. The fact that no significant effects were found would seem to contradict existing research that claims a substantial relationship between customer expertise and product evaluations. However, it seems more likely that only for customers’ interpretation of the specific cues tested in the present research, customer expertise had little relevance.
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Up to this point, the specific outcomes of the dissertation research have been discussed and summarized. The following chapter will therefore outline the more general implications that the present studies contribute to different lines of marketing research.
5.2.1
Contribution to Literature on Open Product Architectures
First of all, this dissertation aims to shed light onto the behavioral and cognitive consequences triggered by a sales approach that has been hardly researched before. The fact that firms, like banks or own-label retailers, open their proprietary distribution channel(s) to competitor products, has received considerable attention in the consumer press and industry publications.
However, the effects of such an open-architecture offering have, to the author's knowledge, not been subject of any consumer behavioral research before. A number of conclusions can be drawn from fields of study that bear a certain resemblance or relevance to the topic – such as counter-intuitive sales messaging, disconfirmed expectancies and attribution theory in general. These can help to understand isolated aspects of how an open architecture may affect customer perceptions and actions, but they do not allow to gain a comprehensive and integrative understanding of the different effects at work. The present dissertation attempts to close this gap by proposing a specific conceptual model that addresses both cognitive and behavioral reactions customers are likely to display when faced with an open-architecture offering. Specifically, it outlines how salesperson behavior and customers' attributional thinking interact in influencing these reactions. The model ties back to a broad range of earlier findings from marketing and services research. In addition, it has been refined based on a qualitative study that explored actual bank customers' views on financial advice and their interpretations of an open product architecture. In order to reflect the likely complexity of causal relationships, care was taken to
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account for both mediation and moderation effects. The hypothesized relationships articulated by the conceptual model have been empirically tested in form of two quantitative experimental studies. In spite of certain limitations that will be discussed later in this chapter, the results suggest a satisfactory validity of the conceptual model.
5.2.2
Contribution to Literature on Cue Influence in Selling
This dissertation underlines the influence that salesperson behavior observed during a sales episode has on customers' reactions, adding to a considerable body of existing evidence (e.g. Bell et al. 2005; Bell and Eisingerich 2007;
Doney and Cannon 1997; Hawes et al. 1989; Kennedy et al. 2001; Pornpitakpan
2004; Sirdeshmukh et al. 2002; Sparks and Areni 2002; Sujan et al. 1986; Swan et al. 1999; Weitz 1981; Weitz et al. 1986; Wood et al. 2008a; Wood et al.
2008b). Specifically, the present work extends our understanding of the effects of counter-intuitive selling approaches. Past research on the effects of counterintuitive selling has often focused on communication messages that seem incongruent with the communicator's expected motivations and beliefs. Typical experiments have featured, for instance, an individual that advocates an unexpected opinion (Kohn and Snook 1976; McPeek and Edwards 1975) or a salesperson or advertisement that points out not only the strengths, but also some weaknesses of a promoted product (Etgar and Goodwin 1982; Golden and
Alpert 1987; Pechmann 1992). Key findings in this field have been that consumers often perceive seemingly counter-intuitive behaviors and messages from salespersons as signs of benevolence (Sirdeshmukh et al. 2002) or
'authentic' product information (Hunt et al. 1982), and that the advocacy of an unexpected position can improve a communicator’s credibility (Crowley and
Hoyer 1994; Eisend 2007; Etgar and Goodwin 1982; Golden and Alpert 1987;
Koeske and Crano 1968; Kohn and Snook 1976; Pechmann 1992). The research presented in this dissertation supports these earlier findings. More importantly,
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though, it extends the existing body of evidence by focusing on counter-intuitive stimuli other than the sales message itself.
For one, the qualitative pre-study provides a first indication that an openarchitecture offering in itself represents a case of counter-intuitive selling – the majority of interviewees expressed their astonishment at such a sales approach and their likelihood to ‘wonder why’. More importantly, the experimental findings clearly show that in the context of an open architecture, a customer's observation of two specific counter-intuitive cues can lead to higher satisfaction, purchasing intention and willingness to provide word-of-mouth. The first one, proactiveness, is given when a salesperson offers competitor products at his or her own initiative, rather than only following the customer's explicit request.
The second one, a balanced product mix, is provided when the salesperson's recommendation considers in-house and competitor products in equal parts, i.e., without demonstrating an obvious bias for one or the other side. However, the occurrence of both effects is contingent upon the level of persuasiveness that the advisor's reasoning offers.
The qualitative study also hints at the negative effects that too strong counterintuitive signals may have. Asked what they would think if the advisor presented them only with competitor products, the interviewees responded with predominately negative inferences. Several respondents saw such a behavior as an indication of the in-house products' low quality. A similar risk has been pointed out for two-sided advertisements that highlight a product's weak points too prominently: Crowley and Hoyer (1994) argue that at some point, the credibility gained by exposing a product's weaknesses may be more than outweighed by detrimental effects on a customer's actual intention to buy the product. One respondent in the qualitative study saw a recommendation of only external products as the advisor's all-too-obvious attempt to appear objective.
This suggests that such an approach could be perceived as a deliberate persuasion tactic and thus trigger unfavorable responses (Friestad and Wright
1994; Tormala and Petty 2004). The latter negative findings help to illustrate the
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richness of potential reactions to an open-architecture offering, but they were not tested as part of the experiments and should therefore be treated with caution.
Lastly, the research presented in this dissertation underlines the importance of investigating cue effects not in an isolated, but integrative manner. It has been criticized that a considerable amount of cue research examines isolated effects, while in reality, consumers will mostly form their opinion based on a variety of cues. On their specific subject of product quality, Purohit and Srivstava (2001, p.
123) argue: “although consumers are exposed to multiple cues simultaneously in the marketplace, there is relatively little understanding of how these cues are combined or integrated in assessments.” Thus, by illustrating the moderation effects among proactiveness, product mix and persuasiveness, a more integrated view on cue effects is supported.
5.2.3
Contribution to Literature on Customers' Attributional Thinking
In addition to investigating the influence of cues, the present research also sheds further light on the specific role of attributional thinking in the attitudeformation process that leads to customer reactions. Firstly, the qualitative prestudy hints at the function of expectancy disconfirmation as a trigger of customers' causal searches. Secondly, by listing a typology of various customeroriented and suspicion-oriented attributions, it provides a clear and tangible impression of attributions as they are actually generated by customers. Thirdly, the quantitative results of two experiments show a highly significant relationship between customer-oriented attributions and customer reactions, confirming the substantial influence that attributional thinking has been claimed to have. But most importantly, the experimental results contribute to a better understanding of the overall relationship between cues, attributions and customer reactions.
Earlier research has explored attributional thinking and also its link to the disconfirmation of consumers’ expectancies (e.g. Hastie 1984; Hunt et al. 1982;
McPeek and Edwards 1975; Pyszczynski and Greenberg 1981; Smith and Hunt
1987; Sujan et al. 1986). However, only few studies have jointly investigated the
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interaction of cues and attributions in instigating customer reactions. This thesis extends previous findings of DeCarlo (2005) and supports the causal relationships the author has established. It provides detailed evidence of the mediating role of attributions in the context of an open-architecture offering. In doing so, the dissertation highlights both potential antecedents and consequences of attributional thinking and thereby helps us to gain a more holistic understanding of this cognitive process.
In addition, some individual points are worth noting. Firstly, the experiments have shown both direct and indirect effects on customer reactions for all three cues – even though these were provided in different phases of the sales encounter. This would support the idea that customers use a variety of cues
(Burnkrant 1975) and form their opinion along the whole sales episode, rather than making up their mind after observing the first cue. Moreover, the last cue in the experiment – persuasiveness of reasoning – turned out to be the one that moderated the influence of the other previous two, proactiveness and product mix. They had no positive impact on customer reactions unless persuasiveness of reasoning was high. Put differently, it would seem that the positive motives associated with high proactiveness or a balanced product mix were discounted as long as the advisor's low persuasiveness suggested a lack of expertise or interest in the customer's benefit.
The results of this dissertation have a number of implications for managers of companies that either have an open-architecture offering in place or intend to introduce such a sales model to their customers. The following sections will review these implications, starting with those that are most directly linked to the original research questions and the corresponding findings. Specifically, consequences for the management and education of a sales force are outlined.
Subsequently, the wider implications for areas such as marketing
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communications and brand management are pointed out, leading from particular to more general considerations.
5.3.1
Delivering an Open-Architecture Offering to Clients
In order to improve their sales effectiveness, companies that employ an openarchitecture sales model will want to instigate favorable customer reactions towards such an offering, and they will want to avoid negative ones. With this goal in mind, the present dissertation has explored whether there are specific cues that influence customer perceptions and intentions in a personal-selling, open-architecture context. Moreover, different qualitative and quantitative methods have been used to understand the cognitive processes that lead from the observation of a cue to the attribution of causes and onwards to a customer reaction. The research findings suggest that, indeed, there are specific behaviors that a salesperson can demonstrate in order to instigate positive customer reactions. A major part of these effects is indirect, i.e., the cues trigger favorable customer-oriented attributions, which in turn lead to improved satisfaction, purchasing intention and willingness to recommend. There is clear evidence that both a proactive offering of third-party products and a balanced mix of inhouse versus external products are appreciated by customers. However, if the sales agent fails to explain in a convincing and transparent manner why individual products – and especially the third-party ones – have been selected, neither proactiveness nor a balanced product mix can make up for this blunder.
These conclusions have several critical implications for the management of an open or "guided" architecture offering, and possibly, for any sales approach in which own-manufactured products are sold next to third-party ones.
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5.3.1.1
Sales Force Competence
Firstly, companies that expect their sales force to successfully sell not only inhouse, but also third-party products need to make sure that their sales agents have the necessary competence to provide a persuasive reasoning for these products. Salesperson competence is a major driver of customer trust, and according to Swan, Bowers and Richardson (1999) is perceived as displayed through skills, ability and expertise. The importance of salesperson expertise in building customer trust and improving the buyer-seller relationship has been highlighted by numerous studies (e.g. Crosby et al. 1990; Doney and Cannon
1997; Wood et al. 2008a; Wood et al. 2008b). A company may be accustomed to building sales force expertise through product trainings and seminars – but not necessarily on the topic of third-party products. Hence, one challenge will be to offer a comprehensive education program that ensures sales agents to be just as knowledgeable about third-party products as they are about in-house offerings. This should prove difficult, if not impossible, since the amount of external products is – in categories such as investment products – virtually infinite. Most likely, firms will therefore have to chose carefully which thirdparty products are offered proactively and then provide a sufficient training for those.
Similar considerations apply for the competence dimensions of skill and ability.
The findings of this dissertation suggest that the persuasiveness of a salesperson's reasoning may be one of the critical cues that determine the ultimate success or failure of an open-architecture sales encounter. This is in line with a considerable amount of research that has highlighted the importance of selling skills such as presentation techniques, clear language and effective messaging (e.g., Anselmi and Zemanek Jr 1997; Dion and Notarantonio 1992;
Plouffe et al. 2009; Rentz et al. 2002). In order to fully capitalize on the positive expectancy disconfirmation that an open-architecture offering may trigger, companies must make sure that their sales agents have the right skills to explain the benefits of this sales model. From a customer's point of view, the need for a
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clear overview and a convincing reasoning and recommendation is likely to grow with the amount of options available. Moreover, salespersons must be able to convincingly explain why in some cases a third-party product is better suited than an in-house one, and do so without tarnishing their own company's brand.
In conclusion, strong selling skills will be even more urgently needed in an open-architecture context than in other sales encounters.
The third dimension of competence as forwarded by Swan, Bowers and
Richardson (1999) is ability. Ability is demonstrated by executional excellence, as opposed to expertise, which is based on superior knowledge (Sirdeshmukh et al. 2002). If salespeople are expected to genuinely deliver an open-architecture offering to their clients – i.e., demonstrate the ability – then this has a substantial impact on the processes and infrastructure at their command. For instance, firms have to provide their salespeople with equally concise and accessible information on the available third-party products as on their own, in-house products. Similarly, the actual process of transferring ownership of the product to the client should not differ in convenience, whether it is an in-house or a third-party product: If the sale is straightforward in one case, but in the other, the salesperson and customer have to go through complex paperwork full of legal disclaimers, this will put competitor products at a clear disadvantage. In both cases – availability of information and ease of transaction - there is an obvious risk that advisors and sales agents will rather stick to in-house solutions because these are what they know best or sell with greater ease.
5.3.1.2
Sales Force Incentivization
Where a sound and convincing reasoning for the recommended products is given, the two other behaviors that were investigated can further improve customer reactions. If a company’s customers know that an open-architecture product range is in place – e.g., because the firm has advertised it – then the company’s sales agents should offer it on their own initiative. If they do not,
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customers may feel that potentially better options are withheld from them.
Similarly, companies that promote an open-architecture offering should make sure that their customers experience this extended choice as genuine: If sales agents manage to present a balanced mix of in-house and third-party products, customers tend to show positive reactions. If, on the other hand, competitor products are only added to the recommendation as an afterthought, then customers are likely to discard the proposal as a fraud. Both cues bear fundamental implications for the ways in which companies currently set their sales targets and incentivize their sales force. If companies want their sales force to proactively and genuinely offer competitor products, they have to make sure that the sales targets and reward systems support this objective. In the banking industry, for instance, this may not always be the case, as Chapter 1.2 of this dissertation suggests. As long as banks indirectly incentivize their advisors to sell in-house products (Rasch 2003) and allocate their clients' money to ownlabel funds because of higher profit margins (Ross 2010), it seems likely that many advisors will not treat external products equally to their own. Open architecture remains a concept that should be administered in careful doses, though. Overly eager salespeople or advisors that strongly push the open architecture and keep stressing the objectiveness of their advice may be perceived as ‘trying too hard’. Such deliberate provision of a certain cue in order to activate a consumer's use of specific (and favorable) decision heuristics could provoke unfavorable counter-reactions from clients (Friestad and Wright 1994;
Tormala and Petty 2004).
5.3.2
Promoting an Open-Architecture Prior to the Sales Encounter
5.3.2.1
Advertising Communication
In line with the main research focus of this dissertation, the previous section has summarized different salesperson behaviors that are helpful (proactiveness, product mix) or even critical (persuasiveness of reasoning) in the successful
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delivery an open-architecture offering to clients. It has been argued that in order to foster such behaviors, companies must take great care to develop the knowledge and selling skills of their sales force. They also have to make sure their sales personnel have the ability to include third-party products in their recommendation and to execute a sale of such products. Finally, it would seem obvious that a firm’s sales incentives should not discourage its sales agents from offering external products, if its open-architecture offering is expected to be successful.
Beyond the effect of specific salesperson cues, the conceptual model has forwarded the idea that an open-architecture offering per se is likely to be perceived favorably by customers. This argument was based on previous research into the appeal of greater assortment variety and, secondly, into the effects of a positive disconfirmation of expectations. Indeed, results from the qualitative pre-study suggest that many customers would be positively surprised by being offered third-party products. Assuming that such a positive effect exists, it seems perfectly understandable if companies try to actively promote their open architecture: Chapter 1.2 has provided a number of examples for how
German banks advertised their open architecture at the time they first introduced this offering to their home market. If an open architecture disconfirms many customers’ long-held expectations and instigates favorable attributions towards the company and salesperson, it appears logical to advance this effect from the time of the sales encounter to a much earlier point in the acquisition process.
Otherwise, a valuable selling proposition would be wasted: As long as the availability of third-party products is ‘revealed’ only during the sales episode, it can merely improve the satisfaction of existing clients or of those prospects that have proceeded quite far towards becoming a client; the appeal of such an offering is not utilized in attracting new prospects.
Those companies that decide to leverage the positive appeal of an open architecture in their communication and actively promote it – for instance, through advertising – will create a specific expectation on the side of their
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potential and existing clients. This expectation must be fulfilled, i.e., customers need to experience the open product architecture as an authentic offering. If not, the effect may be detrimental: customers who feel they have been deceived by either misleading or false advertising are likely to distrust the sender and develop a negative attitude towards advertising in general (Darke and Ritchie
2007). And, while many products and services in areas like the financial industry have credence qualities, firms should not fall under the illusion that when the moment of truth comes, clients are not able to tell whether the promise of an open architecture has been met. Instead, the present research suggests that many customers do form an opinion on whether they’ve been offered third-party products and whether the offer was genuine, and they will draw corresponding conclusions. Hence, before making it a subject of their marketing communications, companies that dispose of an open product architecture need to make sure they also deliver it to their customers in practice.
Where a firm does decide to actively promote its open architecture to customers, the advertised reasons and benefits behind such an offering should be carefully considered. This dissertation has argued that customers will wonder about a firm’s motives if it offers them competitor products, and they will search for explanations. In line with this, the persuasiveness of reasoning provided by a salesperson turned out to be a powerful cue in eliciting positive customer reactions. It seems therefore plausible that just like the salesperson is expected to reason why a certain product (and especially a third-party one) has been recommended, any advertising must explain in a convincing way why a firm would offer an open-architecture in the first place. This reasoning may prove to be a rather delicate task: if, for instance, the promise of “best-in-class” products is emphasized too strongly, this may suggest to some customers that the company’s own in-house products are of inferior quality. Such brand-related considerations will be addressed in the final sections of this chapter.
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5.3.2.2
Brand Positioning
The above advertising example highlights that companies fundamentally change their value proposition when they begin to sell competitor products through their proprietary distribution channels. The introduction of an open architecture transports a message to customers that needs to be carefully aligned with the company's brand positioning. What is the primary role that the firm intends to be associated with: that of a manufacturer or that of a retailer? In the qualitative pre-study, several interviewees attributed an open architecture to a firm's motivation to extend the depth and bandwidth of its product offering. A common perception was that very few firms can credibly claim that their own products are the best in every single product category of their portfolio. Hence, it is perfectly understandable to clients that a company would source certain products – e.g., those that require a very specific manufacturing expertise – from external partners. In other words, customers are prepared to accept a company’s hybrid function of both manufacturer and retailer, as long as the two are not contradicting each other.
The emphasis that companies put on each of the functions may differ considerably, as they find themselves faced with a general and strategic decision
– whether to seek a positioning as either a solution provider / retailer with certain manufacturing capabilities, or as a product manufacturer with its own distribution channel. In the first case, a firm should not promote its own product brand as the best one on every possible dimension, as this would obviously contradict the idea behind its "open" sales model. The firm may even consider to brand its own products in a way that their connection to the corporate brand is less obvious, thereby suggesting that the in-house offering is treated like any other external provider. Examples would be DWS Investments, the mutual funds label of Deutsche Bank, or Union Investment, the funds management branch of the German ‘Volksbanken Raiffeisenbanken’. Similarly, firms that want to strengthen the relationship between the corporate brand and the product brand may opt for an obvious reference in the brand name. After its own fund business
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for private investors, branded “Adig”, had lost ground after the introduction of an open product architecture, Commerzbank renamed the business to
“Cominvest”, aiming to leverage the parent company’s brand equity
(Anonymous 2006a, b).
5.3.2.3
Impact of Third-Party Brands
Finally, it may not be enough for managers to consider how the overall promise of an open architecture changes customer perceptions of their firm and its brand.
There are also several reasons why the association with a specific third-party brand that is distributed by the company could affect the firm’s own brand. For one, the results of this dissertation suggest that customers react to an openarchitecture offering with a rich set of attributions. It has also been shown that in the development of such causal inferences, customers evaluate different cues. In the present research, these cues were provided in form of certain types of behavior displayed by the salesperson. It seems highly plausible that the brand image of one or several third-party brands offered to a customer would serve as another input to this evaluation process. After all, there is abundant evidence for the fundamental role that brands take in consumer decision processes (cf. Keller and Lehmann 2006). Further support for such an influence of third-party brands can be gained from research into the relationship between retailer brands and assortment brands. Jacoby and Mazursky (1984) have demonstrated that customers who associate a retailer brand with a manufacturer brand tend to average their perception of both brands - the weaker one’s image is improved, while the stronger brand suffers. Moreover, it has been argued that individual
‘anchor’ brands on the one hand and the number of well-known brands in a firm’s assortment have separate and significant effects on the retailer brand
(Porter and Claycomb 1997). A direct relationship is also underlined by Mulhern
(1997) who concludes from a research review that the quality and reputation of the brands listed by a retailer affect the retailer brand directly, and not necessarily only by improving the firm’s perceived assortment quality. Such
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brand effects have not been researched in this dissertation. However, the present findings have demonstrated significant relationships between cues, customeroriented attributions and customer reactions. If, for good reasons, it is assumed that the brands of external products represent another relevant cue, then similar effects must be considered. Consequently, managers should carefully evaluate not only whether they open their proprietary distribution channels to other parties, but also to which ones exactly.
In summary, managers should be aware of the manifold implications of an openarchitecture sales model, some of which have been highlighted by the findings of this dissertation. Even though anecdotal evidence from several Swiss and
German banks suggests a different thinking, the opening of a proprietary distribution channel to third parties is not like any other “me-too” extension of a firm’s product assortment. In order for this particular sales strategy to be successful, managers have to rethink their approach to providing the right competence and incentives to their sales force. Moreover, they should consider early on how the open product architecture alters their company’s value proposition and how this must be reflected in marketing communications and branding.
The present research bears some of the typical limitations imposed by experimental studies. Firstly, laboratory experiments allow the researcher to quite effectively control a defined set of independent variables and therefore generate results of considerable internal validity (Bateson and Hui 1992; Bitner
1990). However, by excluding an unknown variety of other potential influences, the scenarios presented to provide certain stimuli will rarely manage to fully recreate an authentic environment, i.e., the ‘real world’. The present work, for instance, has focused on the effects of three distinct behavioral cues provided
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during the sales episode. Most customers, though, do not enter a sales encounter completely unprejudiced. They rather hold long-established, robust views - for instance, about the motives that drive a salesperson (Campbell and Kirmani
2000; Friestad and Wright 1994). The data provide some support for this notion:
The level of ‘general mistrust’ towards banks that the test subjects were asked to indicate during Study 1 turned out to have a significant negative relationship with customer-oriented attributions and customer reactions. This allows the assumption that a broader exploration of both customer pre-dispositions and cues provided during the sales episode may yield further insights and reveal their relative importance in customers’ evaluation of an open-architecture offering.
A second factor that may limit the external validity of the research results lies in the medium used to present respondents with the different cues. Experiment 1 employed written scenarios to depict the customer-salesperson encounter. Such text stimuli offer the researcher effective means to control specific cues, but have been criticized of lacking realism (Bateson and Hui 1992). Therefore, experiment 2 employed video vignettes of the sales scenario, a simulation that has been argued to provide greater realism (Grandey et al. 2005) and lead to results of satisfactory external and internal validity (Baker et al. 2002; Bateson and Hui 1992; Grandey et al. 2005). Still, both experiments bear the inherent limitations of laboratory research and a confirmation of the experimental findings in a more externally valid context would clearly be desirable.
Beyond the limitations common to many research contributions, it should be noted that this dissertation has refrained from making a number of distinctions that may allow an even more detailed understanding of certain relationships. The first one concerns the differentiation of sales agent and company. Customers who are faced with an open-architecture sales scenario may attribute certain salesperson behaviors either to the salesperson him- or herself or to the company that this person is working for. Consequently, attributions of responsibility (see
Chapter 2.2.1) for a certain action may differ. The qualitative interviews
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produced a few statements along this line, with respondents expressing their belief that bank advisors would prefer to sell in-house products because a) this earned them a higher commission, or because b) they were forced to do so by their company’s demanding sales targets. However, the overall evidence found for such a distinction by the customer was not compelling enough to be reflected in the conceptual model. The majority of responses suggest that the interviewees did not strongly distinguish between the advisor’s and the bank’s specific role in offering third-party products. On the one hand, this is supported by findings that customers often tend to ‘generalize’ a salesperson’s behavior, perceiving the sales agent and the firm or brand as one and the same (Crosby et al. 1990;
Wentzel, Tomczak, and Herrmann 2008). On the other hand, Wentzel (2009) argues that a customer will be more likely to distinguish between a company and its salesperson the more he or she depends on this person’s assistance. It seems plausible that the number of product choices available represents one reason why such a perceived ‘outcome-dependency’ would grow (another might be, for instance, the perceived risk of a certain type of investment product). In other words, if an open product architecture offers a vast assortment that only an expert can ‘navigate’, many customers will feel reliant on the sales agent – and, consequently, form a more individual impression of this person. Further research could help to clarify these effects.
Finally, this dissertation does often use the terms ‘salesperson’ / ‘sales agent’ and ‘(client) advisor’ synonymously. The reason for this is that the present research is focused on the very concrete example of an open product architecture as it has been implemented by many banks. Hence, the frequent usage of the term ‘advisor’. Doubtlessly, many financial institutions would object such a synonymous use, pointing out that an advisor’s role – in contrast to a salesperson’s – is not primarily to sell, but to advise clients on financial matters, and ideally do so in a client-oriented, neutral way. However, both the controversial discussion outlined in chapter 1.2 and results of the qualitative study suggest that many bank clients do not perceive their advisor as a
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completely neutral source of information, but are well aware of the sales motives (and, therefore, conflicts of interest) that are rooted in the close relationship of an advisor and his or her bank (Bolton et al. 2007; Rasch 2003).
Some of the positive ‘surprise’ created by an open-architecture offering would seem to have its root in those very expectations. Many statements gathered in the qualitative pre-study suggest that it was the selling motivation of advisors that many interviewees saw disconfirmed by an open architecture. Concluding, it seems fair to assume that many of the presented findings should apply to encounters with ‘salespeople’ as much as with ‘client advisors’.
This dissertation attempts to improve our understanding of how customers react to an open-architecture product offering and what role salesperson behavior and attributional thinking play in this context. To this point, specific research on open-architecture selling is scarce and the author is not aware of any earlier contributions that apply a consumer-behavior angle . And while there are other sales approaches that bear a certain resemblance – such as competitor collaboration or two-sided advertising – further investigations into different aspects of open-architecture selling seem promising.
5.5.1
High versus low Customer Expertise
Chapter 4.4 has argued that the financial literacy of customers themselves could be another factor that influences reactions to an open architecture. Expert customers may show much greater appreciation for such an extended offering since they are confident to understand the differences among products and consequently can choose the right one. Less knowledgeable customers, on the other hand, may be confused by an open architecture and thus feel an even
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greater dependency on the advisor's counsel, leading them to disapprove of such an extended, but potentially intransparent offering. The fact that the results of the present dissertation did not provide any evidence for such a relationship does not disprove its existence. On this issue, several points need to be considered.
First of all, the experimental results only indicate that customers’ own expertise has no significant effect on their evaluation of the three specific salesperson behaviors that have been analyzed here. The data do not suggest that customer expertise has also little influence on other aspects of how customers perceive and react towards an open-architecture offering. It is important to remember that the entire research design – such as the experimental approach and the provided stimuli – aimed to investigate specific customer attributions and reactions. It was not intended to measure the influence of customer expertise and may, for instance, have presented cues for whose interpretation the level of customer expertise had little relevance. Other cues, such as an advisor’s attempt to
‘educate’ the client on investment basics may have led to more different reactions, depending on the customer’s pre-existing financial knowledge.
It should also be noted that many of the previously cited studies on customer expertise have employed a different research methodology. Several empirical studies that have found a significant impact of customer expertise (Bell et al.
2005; Bell and Eisingerich 2007; Sharma and Patterson 2000) employed largesample surveys investigating actual customer experiences and behaviors over a long time period. It seems plausible that the evaluations of the technical and functional aspects of a product or service that test subjects have actually experienced would depend more strongly on their individual expertise than would their evaluation of a scenario observed during a laboratory experiment.
Hence, a more appropriate approach for testing the influence of customer expertise may feature a large-scale survey among customers who have actually been confronted with an open-architecture offering. While such a survey would hardly allow to isolate individual cues – as has been done in this dissertation –, it could yield more general insights as to whether and how customers’ expertise affects their evaluation of an open product architecture.
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5.5.2
Manufacturer Role versus Retailer Role
The research presented in this dissertation has underlined the importance of cues in customers’ evaluation of a sales or service encounter. Beyond the three behavioral cues that have been investigated in the experimental studies, there is obviously a variety of other cues that may directly or indirectly influence a customer’s perception of and reaction towards an open-architecture offering. In the context of an open-architecture sales model, one such cue could bear a special relevance, namely what could be called the firm’s main role in the distribution chain. How do customers perceive a bank, a car dealership or an airline’s travel agency? Are they primarily the distribution outlet of a specific manufacturer brand – or are they a retailer of products from a great variety of sources, their own brand only one among many? This question seems to carry significant importance for at least two reasons: Firstly, customers usually perceive the reputation of a manufacturing brand and of a retailer as two separate and relevant cues. Secondly, it seems likely that those customers who perceive a firm as a retailer rather than a manufacturer will also be less surprised by its open architecture: the counter-intuitiveness of the offering diminishes, and with it, possibly, the urge for attributional thinking.
Previous research has demonstrated that consumers’ evaluation of a product’s quality is significantly influenced by the reputation of both a product’s manufacturer and the retailer through which the product is purchased (Purohit and Srivastava 2001). Among the two, manufacturer reputation may be the most important cue in such an assessment (Dodds, Monroe, and Grewal 1991; Grewal et al. 1998; Rao and Monroe 1988). However, as retailers provide the interface between customers and manufacturers, their image also bears a weight, and consumers consequently take both cues into consideration (Purohit and
Srivastava 2001). In their investigation of a) the influence and b) the interaction of manufacturer and retailer reputation on quality judgments, some of the above studies have indeed distinguished between a manufacturer that sells through a retailer (Dodds et al. 1991) and one that sells its products through its own,
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proprietary distribution channels (Boulding and Kirmani 1993; Purohit and
Srivastava 2001). Typical examples for the first case would be consumer goods such as Mars chocolate bars or Gillette razors, while the IKEA furniture store is an example of the second case. Among other results, it was found that consumer perceptions of product quality were higher when the manufacturer sold its product through a reputed retailer relative to selling directly (Purohit and
Srivastava 2001). However, such a distinction does not cover the unique characteristics of an open-architecture product offering. In the case of an open architecture, the manufacturer is more than just its own distributor: Many banks, for example, are well established manufacturing brands (of services as much as of financial products), and at the same time, they take over a role of a fullyfledged retailer that also sells third-party and even competitor products. How do customers perceive such a company’s reputation? Do they still perceive two distinct reputational cues – manufacturer brand(s) and retailer brand – and if so, how do they interact in the attitude formation process? Which of the two (very different) roles dominates their perception? And how does this perception then influence their evaluation of the firm’s offering? An initial hypothesis is that this may strongly depend on different types of customer. Some will “buy the brand”, and show little interest in other parts of the offering. It seems quite plausible that a certain type of Audi customer could not care less about the fact that the dealer’s next door showroom sports Volkswagen cars. Another type of client may just be looking for a mid-size quality car and therefore appreciate the chance to directly compare the Audi A3 and the VW Golf in form of a “onestop-shopping”. It would therefore seem promising if further research investigated the different perceived roles of companies that offer an open architecture, how these perceptions influence customer reactions and how they relate to customer characteristics and needs.
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5.5.3
Perceived Product and Range Fit
Finally, a field of potential research emerges from differences not among customers or firms, but products. Some of the statements from the qualitative pre-study hint at an interesting effect: In what they regard as ‘standard’ investment categories (e.g., a mutual fund replicating the Dow Jones index), the financially experienced clients will expect and demand their bank to offer some of its own products. If this is not the case, some clients tend to attribute this to the overall bad quality of the bank’s products, questioning its role as a manufacturer of investment solutions (“If they cannot make that, what can they?”). These unfavorable attributions do not occur where ‘specialty’ investments are discussed (e.g., a fund that invests in fine art). Here, financially experienced clients welcome the offering of third-party products, because their manufacturers promise a superior expertise that the bank itself is not expected to have. The perceived proximity or distance of a product to its manufacturer’s core offering has been found influential in other studies, especially on brand extensions and product fit. They suggest that customers perceive new products sold under a specific brand more favorably if they share a certain similarity with the category or categories in which the parent brand traditionally operates
(Aaker and Keller 1990; Boush and Loken 1991; Broniarczyk and Alba 1994;
Buil, de Chernatony, and Hem 2009). Keller and Aaker (1992) argue that one dimension of such fit evaluations pertains to a firm's perceived ability to manufacture the extension. From a customer’s view-point, certain products may
‘overstretch’ a firm’s expertise and capabilities (Smith and Andrews 1995). In the context of the present research, this means: While many customers will, for instance, acknowledge Bank of America's (BofA) extensive range of products and global investment expertise, they may be doubtful of its ability to set up a highly specialized mutual fund on ‘classical European art’. Instead, they may welcome it if their BofA advisor offered them such a fund from an Amsterdambased fund management ‘boutique’ specialized on art investment. In short, an open-architecture extension of a firm’s product portfolio may be best accepted
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by customers if it focused on those products that are dissimilar to the firm’s inhouse offering and require capabilities in which the company can claim little competence.
Does this on the other hand mean that companies can sell virtually anything, as long as it is from a third-party supplier and not branded as their own? That seems quite unlikely. Rather, even the range of third-party products that can be sold through an open architecture will be limited. Assortment variety research has shown that consumers also evaluate the overall fit of a retailer's image and a specific product range (‘range fit’). If a product range is too dissimilar to a retailer's image, consumers can perceive those products as ‘inappropriate’ for the retailer (Hart and Davies 1996). Great American Bank, for instance, a
Kansas-based local community bank, may not be perceived to have sufficient expertise for even selling any investment products that cover, e.g., Asian-Pacific economies, no matter whether they are own-label or sourced from an external provider. It can be concluded that, in order for the consumer to appreciate thirdparty products, these products may need to be sufficiently dissimilar to the manufacturer's traditional offering, and sufficiently similar to its image as a retailer. Additional research in this area seems promising and relevant – it may reveal insights into which part of a company’s offering could be enhanced by adding third-party products and which should not.
131

Aaker, David A. and Kevin Lane Keller (1990), "Consumer Evaluations of
Brand Extensions," Journal of Marketing , 54 (1), 27-41.
Ahearne, Michael, Ronald Jelinek, and Eli Jones (2007), "Examining the Effect of Salesperson Service Behavior in a Competitive Context," Journal of the Academy of Marketing Science , 35 (4), 603-16.
Alba, Joseph W. and J. Wesley Hutchinson (1987), "Dimensions of Consumer
Expertise," Journal of Consumer Research , 13 (4), 411-54.
Aldlaigan, Abdullah H. and Francis A. Buttle (2001), "Consumer Involvement in Financial Services: An Empirical Test of Two Measures," International
Journal of Bank Marketing , 19 (6), 232.
Amaldoss, Wilfred, Robert J. Meyer, Jagmohan S. Raju, and Amnon Rapoport
(2000), "Collaborating to Compete," Marketing Science , 19 (2), 105-26.
Anderson, Eugene W. and Mary W. Sullivan (1993), "The Antecedents and
Consequences of Customer Satisfaction for Firms," Marketing Science , 12
(2), 125-43.
Anonymous (2006a), "Adig-Fonds Werden in Cominvest Umbenannt," www.fondscheck.de
, September 26, 2006, Accessed: April 20, 2011, http://www.fondscheck.de/artikel/news-Sonstiges-1376124.html.
Anonymous (2006b), "Commerzbank Stampft Die Marke Adig Ein," www.handelsblatt.com
, Accessed: April 20, 2011, http://www.handelsblatt.com/finanzen/fonds/nachrichten/commerzbankstampft-die-marke-adig-ein/2657402.html.
Anonymous (2010), "Private Banking Units Staff Up," in Euromoney .
Anselmi, Kenneth and James E. Zemanek Jr (1997), "Relationship Selling: How
Personal Characteristics of Salespeople Affect Buyer Satisfaction,"
Journal of Social Behavior & Personality , 12 (2), 539-50.
Areni, Charles S. and K. Chris Cox (1995), "Assessing the Impact of Message
Cues and Arguments in Persuasion: Conceptual and Methodological
Issues," Advances in Consumer Research , 22 (1), 198-202.
132

Baker, Julie, A. Parasuraman, Dhruv Grewal, and Glenn B. Voss (2002), "The
Influence of Multiple Store Environment Cues on Perceived Merchandise
Value and Patronage Intentions," Journal of Marketing , 66 (2), 120-41.
Baron, Reuben M. and David A. Kenny (1986), "The Moderator–Mediator
Variable Distinction in Social Psychological Research: Conceptual,
Strategic, and Statistical Considerations," Journal of Personality and
Social Psychology , 51 (6), 1173-82.
Barr, N. (2009), "Marks and Spencer to Start Selling Branded Food Products.," http://www.express.co.uk
, November 4, 2009, Accessed: November 14,
2009, http://www.express.co.uk/posts/view/138154/Marks-Spencer-tostart-selling-branded-food-products.
Bateson, John E. G. and Michael K. Hui (1992), "The Ecological Validity of
Photographic Slides and Videotapes in Simulating the Service Setting,"
Journal of Consumer Research , 19 (2), 271-81.
Baum, Klaus-Jürgen (2005), "Institute Öffnen Sich Fremden Fonds,"
Handelsblatt , April 6, b08.
Bell, Simon J., Seigyoung Auh, and Karen Smalley (2005), "Customer
Relationship Dynamics: Service Quality and Customer Loyalty in the
Context of Varying Levels of Customer Expertise and Switching Costs,"
Journal of the Academy of Marketing Science , 33 (2), 169-83.
Bell, Simon J. and Andreas B. Eisingerich (2007), "The Paradox of Customer
Education: Customer Expertise and Loyalty in the Financial Services
Industry," European Journal of Marketing , 41 (5/6), 466-86.
Bender, Yuri (2009), "European Banks Cut Access to External Fund Providers," www.ft.com
, September 11, 2010, Accessed: November 28, 2010, http://www.ft.com/cms/s/0/76abe988-9979-11de-ab8c-
00144feabdc0.html?nclick_check=1.
Berger, Ida E., Peggy H. Cunningham, and Robert V. Kozinets (1999),
"Consumer Persuasion through Cause-Related Advertising," Advances in
Consumer Research , 26 (1), 491-97.
133

Beverland, Michael, Francis Farrelly, and Zeb Woodhatch (2007), "Exploring the Dimensions of Proactivity within Advertising Agency--Client
Relationships," Journal of Advertising , 36 (4), 49-60.
Bitner, Mary Jo (1990), "Evaluating Service Encounters: The Effects of Physical
Surroundings and Employee Responses," Journal of Marketing , 54 (2),
69-82.
Bloemer, Josée, Ko de Ruyter, and Martin Wetzels (1999), "Linking Perceived
Service Quality and Service Loyalty: A Multi-Dimensional Perspective,"
European Journal of Marketing , 33 (11/12), 1082-106.
Boatwright, Peter and Joseph C. Nunes (2001), "Reducing Assortment: An
Attribute-Based Approach," Journal of Marketing , 65 (3), 50-63.
Boles, James S., Julie T. Johnson, and Hiram C. Barksdale Jr (2000), "How
Salespeople Build Quality Relationships: A Replication and Extension,"
Journal of Business Research , 48 (1), 75-81.
Bolton, Patrick, Xavier Freixas, and Joel Shapiro (2007), "Conflicts of Interest,
Information Provision, and Competition in the Financial Services
Industry," Journal of Financial Economics , 85 (2), 297-330.
Bolton, Ruth N. and Katherine N. Lemon (1999), "A Dynamic Model of
Customers' Usage of Services: Usage as an Antecedent and Consequence of Satisfaction," Journal of Marketing Research (JMR) , 36 (2), 171-86.
Boorom, Michael L., Jerry R. Goolsby, and Rosemary P. Ramsey (1998),
"Relational Communication Traits and Their Effect on Adaptiveness and
Sales Performance," Journal of the Academy of Marketing Science , 26 (1),
16-30.
Boulding, William and Amna Kirmani (1993), "A Consumer-Side Experimental
Examination of Signaling Theory: Do Consumers Perceive Warranties as
Signals of Quality?," The Journal of Consumer Research , 20 (1), 111-23.
Boush, David M. and Barbara Loken (1991), "A Process-Tracing Study of
Brand Extension Evaluation," Journal of Marketing Research (JMR) , 28
(1), 16-28.
134

Broniarczyk, Susan M. and Joseph W. Alba (1994), "The Importance of the
Brand in Brand Extension," Journal of Marketing Research (JMR) , 31 (2),
214-28.
Broniarczyk, Susan M., Wayne D. Hoyer, and Leigh McAlister (1998),
"Consumers' Perceptions of the Assortment Offered in a Grocery
Category: The Impact of Item Reduction," Journal of Marketing Research
(JMR) , 35 (2), 166-76.
Brown, Tom J., Thomas E. Barry, Peter A. Dacin, and Richard F. Gunst (2005),
"Spreading the Word: Investigating Antecedents of Consumers' Positive
Word-of-Mouth Intentions and Behaviors in a Retailing Context," Journal of the Academy of Marketing Science , 33 (2), 123-38.
Buil, Isabel, Leslie de Chernatony, and Leif E. Hem (2009), "Brand Extension
Strategies: Perceived Fit, Brand Type, and Culture Influences," European
Journal of Marketing , 43 (11/12), 1300-24.
Burger, Jerry M. and Lawton T. Hemans (1988), "Desire for Control and the
Use of Attribution Processes," Journal of Personality , 56 (3), 531-46.
Burnkrant, R. E. (1975), "Attribution Theory in Marketing Research: Problems and Prospects," Advances in Consumer Research , 2 (1), 465.
Campbell, Margaret C. and Amna Kirmani (2000), "Consumers' Use of
Persuasion Knowledge: The Effects of Accessibility and Cognitive
Capacity on Perceptions of an Influence Agent," Journal of Consumer
Research , 27 (1), 69-83.
Casado Diaz, Ana B. and Francisco J. Más Ruíz (2002), "The Consumer's
Reaction to Delays in Service," International Journal of Service Industry
Management , 13 (2), 118.
Challagalla, Goutam, R. Venkatesh, and Ajay K. Kohli (2009), "Proactive
Postsales Service: When and Why Does It Pay Off?," Journal of
Marketing , 73 (2), 70-87.
Chebat, Jean-Charles, Pierre Filiatrault, Claire Gélinas-Chebat, and Alexander
Vaninsky (1995), "Impact of Waiting Attribution and Consumer's Mood on Perceived Quality," Journal of Business Research , 34 (3), 191-96.
135

Chernev, Alexander (2003), "Product Assortment and Individual Decision
Processes," Journal of Personality & Social Psychology , 85 (1), 151-62.
Chernev, Alexander (2006), "Decision Focus and Consumer Choice among
Assortments," Journal of Consumer Research , 33 (1), 50-59.
Chernev, Alexander and Leigh McAllister (2005), "Product Assortment and
Variety-Seeking in Consumer Choice," Advances in Consumer Research ,
32 (1), 119-21.
Crosby, Lawrence A., Kenneth A. Evans, and Deborah Cowles (1990),
"Relationship Quality in Services Selling: An Interpersonal Influence
Perspective," Journal of Marketing , 54 (3), 68.
Crowley, Ayn E. and Wayne D. Hoyer (1994), "An Integrative Framework for
Understanding Two-Sided Persuasion," Journal of Consumer Research ,
20 (4), 561-74.
Cunningham, John D. and Harold H. Kelley (1975), "Causal Attributions for
Interpersonal Events of Varying Magnitude1," Journal of Personality , 43
(1), 74-93.
Curren, Mary T. and Valerie S. Folkes (1987), "Attributional Influences on
Consumers' Desires to Communicate About Products," Psychology &
Marketing , 4 (1), 31-45.
Darke, Peter R. and Robin J. B. Ritchie (2007), "The Defensive Consumer:
Advertising Deception, Defensive Processing, and Distrust," Journal of
Marketing Research (JMR) , 44 (1), 114-27. de Bont, Cees J. P. M. and Jan P. L. Schoormans (1995), "The Effects of
Product Expertise on Consumer Evaluations of New-Product Concepts,"
Journal of Economic Psychology , 16 (4), 599. de Matos, Celso Augusto and Carlos Alberto Vargas Rossi (2008), "Word-of-
Mouth Communications in Marketing: A Meta-Analytic Review of the
Antecedents and Moderators," Journal of the Academy of Marketing
Science , 36 (4), 578-96.
136

DeCarlo, Thomas E. (2005), "The Effects of Sales Message and Suspicion of
Ulterior Motives on Salesperson Evaluation," Journal of Consumer
Psychology (Lawrence Erlbaum Associates) , 15 (3), 238-49.
Dion, Paul A. and Elaine M. Notarantonio (1992), "Salesperson Communication
Style: The Neglected Dimension in Sales Performance," Journal of
Business Communication , 29 (1), 63-77.
Dixon, Andrea L., Rosann L. Spiro, and Maqbul Jamil (2001), "Successful and
Unsuccessful Sales Calls: Measuring Salesperson Attributions and
Behavioral Intentions," Journal of Marketing , 65 (3), 64-78.
Dodds, William B., Kent B. Monroe, and Dhruv Grewal (1991), "Effects of
Price, Brand, and Store Information on Buyers' Product Evaluations,"
Journal of Marketing Research (JMR) , 28 (3), 307-19.
Doney, Patricia M. and Joseph P. Cannon (1997), "An Examination of the
Nature of Trust in Buyer-Seller Relationships," Journal of Marketing , 61
(2), 35.
Dubinsky, Alan J., Steven J. Skinner, and Tommy E. Whittler (1989),
"Evaluating Sales Personnel: An Attribution Theory Perspective," Journal of Personal Selling & Sales Management , 9 (1), 9.
Dussauge, Pierre and Bernard Garrette (1999), Cooperative Strategy :
Competing Successfully through Strategic Alliances , New York: John
Wiley.
Dussauge, Pierre, Bernard Garrette, and Will Mitchell (2004), "Asymmetric
Performance: The Market Share Impact of Scale and Link Alliances in the
Global Auto Industry," Strategic Management Journal , 25 (7), 701-11.
Eagly, Alice H., Wendy Wood, and Shelly Chaiken (1978), "Causal Inferences
About Communicators and Their Effect on Opinion Change," Journal of
Personality and Social Psychology , 36 (4), 424-35.
Eisend, Martin (2007), "Understanding Two-Sided Persuasion: An Empirical
Assessment of Theoretical Approaches," Psychology and Marketing , 24
(7), 615-40.
137

Elig, Timothy W. and Irene H. Frieze (1979), "Measuring Causal Attibutions for
Success and Failure," Journal of Personality and Social Psychology , 37
(4), 621-34.
Enzle, Michael E. and Donald Schopflocher (1978), "Instigation of Attribution
Processes by Attributional Questions," Personality and Social Psychology
Bulletin , 4 (4), 595-99.
Etgar, Michael and Stephen A. Goodwin (1982), "One-Sided Versus Two-Sided
Comparative Message Appeals for New Brand Introductions," Journal of
Consumer Research , 8 (4), 460-65.
Fein, Steven (1996), "Effects of Suspicion on Attributional Thinking and the
Correspondence Bias," Journal of Personality & Social Psychology , 70
(6), 1164-84.
Felsted, Andrea (2009), "M&S to Expand Brands Sales in Grocery Wars," www.FT.com
, November 4, 2009, Accessed: November 16, 2009, http://proquest.umi.com/pqdweb?did=1893831871&Fmt=7&clientId=456
08&RQT=309&VName=PQD.
Finch, Julia (2009), "This Is Not Just M&S Food. Store Gives Shelf Space to
Top Brands," www.guardian.co.uk
, November 4, 2009, Accessed:
November 4, 2009, http://www.guardian.co.uk/business/2009/ nov/04/marks-and-spencer-food.
Fletcher, Garth J. (1984), "Psychology and Common Sense," American
Psychologist , 39 (3), 203-13.
Folkes, Valerie S. (1984), "Consumer Reactions to Product Failure: An
Attributional Approach," Journal of Consumer Research , 10 (4), 398-409.
Folkes, Valerie S. (1988), "Recent Attribution Research in Consumer Behavior:
A Review and New Directions," Journal of Consumer Research , 14 (4),
548-65.
Folkes, Valerie S., Susan Koletsky, and John L. Graham (1987), "A Field Study of Causal Inferences and Consumer Reaction: The View from the
Airport," Journal of Consumer Research , 13 (4), 534-39.
138

Friestad, Marian and Peter Wright (1994), "The Persuasion Knowledge Model:
How People Cope with Persuasion Attempts," Journal of Consumer
Research , 21 (1), 1-31.
Garella, Paolo G. and Martin Peitz (2007), "Alliances between Competitors and
Consumer Information," Journal of the European Economic Association ,
5 (4), 823-45.
Gimbel, Florian and Tony Major (2002), "Germany Flocks to One-Stop Shops:
Tony Major and Florian Gimbel on the Banking Trend Towards 'Open
Architecture'," Financial Times , September 16, 07.
Goff, Brent G., James S. Boles, Danny N. Bellenger, and Carrie Stojack (1997),
"The Influence of Salesperson Selling Behaviors on Customer Satisfaction with Products," Journal of Retailing , 73 (2), 171-83.
Golden, Linda L. (1977), "Attribution Theory Implications for Advertisement
Claim Credibility," Journal of Marketing Research (JMR) , 14 (1), 115-17.
Golden, Linda L. and Mark I. Alpert (1987), "Comparative Analysis of the
Relative Effectiveness of One- and Two-Sided Communication for
Contrasting Products," Journal of Advertising , 16 (1), 18-68.
GoldmanSachs (2011), "Print Advertisement," in Harvard Business Review ,
Vol. 89, 2-3.
Grandey, Alicia A., Glenda M. Fisk, Anna S. Mattila, Karen J. Jansen, and Lori
A. Sideman (2005), "Is "Service with a Smile" Enough? Authenticity of
Positive Displays During Service Encounters," Organizational Behavior
& Human Decision Processes , 96 (1), 38-55.
Greenleaf, Eric A. and Donald R. Lehmann (1995), "Reasons for Substantial
Delay in Consumer Decision Making," Journal of Consumer Research , 22
(2), 186-99.
Greenwald, Anthony G. and Clark Leavitt (1984), "Audience Involvement in
Advertising: Four Levels," Journal of Consumer Research , 11 (1), 581-
92.
Grewal, Dhruv, R. Krishnan, Julie Baker, and Norm Borin (1998), "The Effects of Store Name, Brand Name and Price Discounts on Consumers'
139

Evaluations and Purchase Intentions," Journal of Retailing , 74 (3), 331-
52.
Hagedoorn, John, Albert N. Link, and Nicholas S. Vonortas (2000), "Research
Partnerships," Research Policy , 29 (4-5), 567-86.
Hamel, Gary, Yves L. Doz, and C. K. Prahalad (1989), "Collaborate with Your
Competitors--and Win," Harvard Business Review , 67 (1), 133-39.
Hart, Cathy A. and Mark A. P. Davies (1996), "Consumer Perceptions of Non-
Food Assortments: An Empirical Study," Journal of Marketing
Management , 12 (4), 297-312.
Hastie, Reid (1984), "Causes and Effects of Causal Attribution," Journal of
Personality and Social Psychology , 46 (1), 44-56.
Hawes, Jon M., Kenneth E. Mast, and John E. Swan (1989), "Trust Earning
Perceptions of Sellers and Buyers," Journal of Personal Selling & Sales
Management , 9 (1), 1.
Heider, Fritz (1958), The Psychology of Interpersonal Relations , New York:
Wiley.
Herkner, Werner (1981), Einführung in Die Sozialpsychologie , Bern, Stuttgart,
Wien: Verlag Hans Huber.
Hess Jr, Ronald L., Shankar Ganesan, and Noreen M. Klein (2003), "Service
Failure and Recovery: The Impact of Relationship Factors on Customer
Satisfaction," Journal of the Academy of Marketing Science , 31 (2), 127-
45.
Hoch, Stephen J., Eric T. Bradlow, and Brian Wansink (1999), "The Variety of an Assortment," Marketing Science , 18 (4), 527-46.
Howcroft, Barry, Robert Hamilton, and Paul Hewer (2007), "Customer
Involvement and Interaction in Retail Banking: An Examination of Risk and Confidence in the Purchase of Financial Products," Journal of
Services Marketing , 21 (7), 481-91.
Huffman, Cynthia and Barbara E. Kahn (1998), "Variety for Sale: Mass
Customization or Mass Confusion?," Journal of Retailing , 74 (4), 491-
513.
140

Hui, Michael K. and Roy Toffoli (2002), "Perceived Control and Consumer
Attribution for the Service Encounter," Journal of Applied Social
Psychology , 32 (9), 1825-44.
Hui, Michael K., Xiande Zhao, Xiuncheng Fan, and Kevin Au (2004), "When
Does the Service Process Matter? A Test of Two Competing Theories,"
Journal of Consumer Research , 31 (2), 465-75.
Hunt, James M., Teresa J. Domzal, and Jerome B. Kernan (1982), "Causal
Attributions and Persuasion: The Case of Disconfirmed Expectancies,"
Advances in Consumer Research , 9 (1), 287-92.
Hunt, James M., Michael F. Smith, and Jerome B. Kernan (1985), "The Effects of Expectancy Disconfirmation and Argument Strength on Message
Processing Level: An Application to Personal Selling," Advances in
Consumer Research , 12 (1), 450-54.
HypoVereinsbank (2001), "Print Advertisement," Munich: Historical Archive of
UniCredit Bank AG.
Iglesias, Victor (2009), "The Attribution of Service Failures: Effects on
Consumer Satisfaction," The Service Industries Journal , 29 (2), 127-41.
Iyengar, Sheena S. and Mark R. Lepper (2000), "When Choice Is Demotivating:
Can One Desire Too Much of a Good Thing?," Journal of Personality &
Social Psychology , 79 (6), 995-1006.
Jacoby, Jacob and David Mazursky (1984), "Research Note: Linking Brand and
Retailer Images--Do the Potential Risks Outweigh the Potential
Benefits?," Journal of Retailing , 60 (2), 105.
Johnson, Mark S. (2006), "A Bibliometric Review of the Contribution of
Attribution Theory to Sales Management," Journal of Personal Selling &
Sales Management , 26 (2), 181-95.
Johnson, Steve (2011), "Bank-Run Funds Are Poor Performers," www.ft.com
,
January 9, 2011, Accessed: January 23, 2011, http://proquest.umi.com/ pqdlink?did=2234118501&Fmt=7&clientId=45608&RQT=309&VName
=PQD.
141

Jonas, Eva and Dieter Frey (2003), "Information Search and Presentation in
Advisor–Client Interactions," Organizational Behavior & Human
Decision Processes , 91 (2), 154.
Jonas, Eva, Stefan Schulz-Hardt, Dieter Frey, and Norman Thelen (2001),
"Confirmation Bias in Sequential Information Search after Preliminary
Decisions: An Expansion of Dissonance Theoretical Research on
Selective Exposure to Information," Journal of Personality & Social
Psychology , 80 (4), 557-71.
Jones, Edward E. and Keith Davis (1965), "From Acts to Dispositions: The
Attribution Process in Person Perception," in Advances in Experimental
Social Psychology , Vol. 2, ed. Leonard Berkowitz, New York: Academic
Press, 219-66.
Kahn, Barbara E. (1995), "Consumer Variety-Seeking among Goods and
Services : An Integrative Review," Journal of Retailing and Consumer
Services , 2 (3), 139-48.
Kahn, Barbara E. and Donald R. Lehmann (1991), "Modeling Choice among
Assortments," Journal of Retailing , 67 (3), 274.
Kelleher, Ellen (2007), "Objective Advice - and Widest Range of Products Open
Architecture: Outside Managers Are Key to Banks' Strategies, Writes
Ellen Kelleher," Financial Times , June 23, 17.
Keller, Kevin Lane and David A. Aaker (1992), "The Effects of Sequential
Introduction of Brand Extensions," Journal of Marketing Research (JMR) ,
29 (1), 35-60.
Keller, Kevin Lane and Donald R. Lehmann (2006), "Brands and Branding:
Research Findings and Future Priorities," Marketing Science , 25 (6), 740-
59.
Kelley, Harold H. (1967), "Attribution Theory in Social Psychology," in
Nebraska Symposium on Motivation , ed. David Levine, Lincoln, NE:
University of Nebraska Press, 192-238.
Kelley, Harold H. (1973), "The Process of Causal Attribution," American
Psychologist (February), 21.
142

Kelley, Harold H. and John L. Michela (1980), "Attribution Theory and
Research," Annual Review of Psychology , 31, 457-501.
Kennedy, Mary Susan, Linda K. Ferrell, and Debbie Thorne LeClair (2001),
"Consumers' Trust of Salesperson and Manufacturer: An Empirical
Study," Journal of Business Research , 51 (1), 73-86.
Kirmani, Amna and Margaret C. Campbell (2004), "Goal Seeker and Persuasion
Sentry: How Consumer Targets Respond to Interpersonal Marketing
Persuasion," Journal of Consumer Research , 31 (3), 573-82.
Koeske, Gary F. and William D. Crano (1968), "The Effect of Congruous and
Incongruous Source-Statement Combinations Upon the Judged Credibility of a Communication," Journal of Experimental Social Psychology , 4 (4),
384-99.
Kohn, Paul M. and Suzi Snook (1976), "Expectancy--Violation, Similarity, and
Unexpected Similarity as Sources of Credibility and Persuasiveness,"
Journal of Psychology , 94 (2), 185.
Krausz, Miriam and Jacob Paroush (2002), "Financial Advising in the Presence of Conflict of Interests," Journal of Economics & Business , 54 (1), 55.
Kroeber-Riel, Werner (2003), Konsumentenverhalten , München: Kroeber-Riel,
Werner, Weinberg, Peter.
Lau, Richard R. and Dan Russell (1980), "Attributions in the Sports Pages,"
Journal of Personality and Social Psychology , 39 (1), 29-38.
Lichtenstein, Donald R. and William O. Bearden (1986), "Measurement and
Structure of Kelley's Covariance Theory," Journal of Consumer Research ,
13 (2), 290-96.
Lichtenstein, Donald R. and Scot Burton (1988), "The Measurement and
Moderating Role of Confidence in Attributions," Advances in Consumer
Research , 15 (1), 468-75.
Luo, Xueming, Aric Rindfleisch, and David K. Tse (2007), "Working with
Rivals: The Impact of Competitor Alliances on Financial Performance,"
Journal of Marketing Research (JMR) , 44 (1), 73-83.
143

Maxham III, James G. and Richard G. Netemeyer (2002), "Modeling Customer
Perceptions of Complaint Handling over Time: The Effects of Perceived
Justice on Satisfaction and Intent," Journal of Retailing , 78 (4), 239.
McPeek, Robert W. and John D. Edwards (1975), "Expectancy Disconfirmation and Attitude Change," Journal of Social Psychology , 96 (2), 193.
Michaels, Ronald E. and Ralph L. Day (1985), "Measuring Customer
Orientation of Salespeople: A Replication with Industrial Buyers,"
Journal of Marketing Research (JMR) , 22 (4), 443-46.
Mizerski, Richard W., Linda L. Golden, and Jerome B. Kernan (1979), "The
Attribution Process in Consumer Decision Making," Journal of Consumer
Research , 6 (2), 123-40.
Mulhern, Francis J. (1997), "Retail.Marketing: From Distribution to
Integration," International Journal of Research in Marketing , 14 (2), 103-
24.
Narat, Info (2002), "Teure Fonds-Demokratie," Handelsblatt , May 6, 29.
Niebuhr, Robert E., Charles C. Manz, and Kermit R. Davis Jr (1981), "Using
Videotape Technology: Innovations in Behavioral Research," Journal of
Management , 7 (2), 43-54.
Niemeyer, Hans-Georg (1993), Begründungsmuster Von Konsumenten :
Attributionstheoretische Grundlagen Und Einflussmöglichkeiten Im
Marketing , Heidelberg: Physica-Verlag.
Oppewal, Harmen and Kitty Koelemeijer (2005), "More Choice Is Better:
Effects of Assortment Size and Composition on Assortment Evaluation,"
International Journal of Research in Marketing , 22 (1), 45-60.
Pechmann, Cornelia (1992), "Predicting When Two-Sided Ads Will Be More
Effective Than One Sided Ads: The Role of Correlation and
Correspondent Inferences," Journal of Marketing Research (JMR) , 29 (4),
441-53.
Pfanner, Eric (2002), "Broadening Horizons When Times Are Tough,"
International Herald Tribune , October 31, 6.
144

Plouffe, Christopher R., John Hulland, and Trent Wachner (2009), "Customer-
Directed Selling Behaviors and Performance: A Comparison of Existing
Perspectives," Journal of the Academy of Marketing Science , 37 (4), 422-
39.
Pornpitakpan, Chanthika (2004), "The Persuasiveness of Source Credibility: A
Critical Review of Five Decades' Evidence," Journal of Applied Social
Psychology , 34 (2), 243-81.
Porter, Stephen S. and Cindy Claycomb (1997), "The Influence of Brand
Recognition on Retail Store Image," The Journal of Product and Brand
Management , 6 (6), 373.
Purohit, Devavrat and Joydeep Srivastava (2001), "Effect of Manufacturer
Reputation, Retailer Reputation, and Product Warranty on Consumer
Judgments of Product Quality: A Cue Diagnosticity Framework," Journal of Consumer Psychology (Lawrence Erlbaum Associates) , 10 (3), 123-34.
Pyszczynski, Thomas A. and Jeff Greenberg (1981), "Role of Disconfirmed
Expectancies in the Instigation of Attributional Processing," Journal of
Personality and Social Psychology , 40 (1), 31-38.
Rajaobelina, Lova and Jasmin Bergeron (2009), "Antecedents and
Consequences of Buyer-Seller Relationship Quality in the Financial
Services Industry," International Journal of Bank Marketing , 27 (5), 359-
80.
Rao, Akshay R. and Kent B. Monroe (1988), "The Moderating Effect of Prior
Knowledge on Cue Utilization in Product Evaluations," Journal of
Consumer Research , 15 (2), 253-64.
Rasch, Michael (2003), "Viele Fragezeichen Beim Drittfonds-Vertrieb," Neue
Zürcher Zeitung , July 25, 27.
Rentz, Joseph O., C. David Shepherd, Armen Tashchian, Pratibha A. Dabholkar, and Robert T. Ladd (2002), "A Measure of Selling Skill: Scale
Development and Validation," Journal of Personal Selling & Sales
Management , 22 (1), 13-21.
145

Richard, Buda and Zhang Yong (2000), "Consumer Product Evaluation: The
Interactive Effect of Message Framing, Presentation Order, and Source
Credibility," The Journal of Product and Brand Management , 9 (4), 229.
Rose, Randall L. and Peter R. Dickson (1987), ""He Says No, but Does He
Really Mean It?": Bargaining Behavior, Cue Consistency, and
Attribution," Advances in Consumer Research , 14 (1), 382-86.
Rose, Randall L. and Peter R. Dickson (1988), "An Initial Test of the Effects of
Cue Patterns on Behavior and Attributions in a Purchasing Negotiation,"
Advances in Consumer Research , 15 (1), 101-07.
Ross, Alice (2010), ""Own-Brand" Products Fly Off the Shelves," Financial
Times , July 3, 6-8.
Rust, Roland T. and Anthony J. Zahorik (1993), "Customer Satisfaction,
Customer Retention, and Market Share," Journal of Retailing , 69 (2), 193-
215.
Sanbonmatsu, David M. and Frank R. Kardes (1988), "The Effects of
Physiological Arousal on Information Processing and Persuasion,"
Journal of Consumer Research , 15 (3), 379-85.
Sandler, Kathy (2009), "M&S Will Add Name-Brand Goods – Retailer Ending
50 Years of Selling Only Its Brands in Nod to Consumer Demands," The
Wallstreet Journal Europe , November 5, 8.
Saxe, Robert and Barton A. Weitz (1982), "The Soco Scale: A Measure of the
Customer Orientation of Salespeople," Journal of Marketing Research
(JMR) , 19 (3), 343-51.
Schulz, Thomas M. (2002), "Anzeichen Eines Paradigmenwechsels in Der
Fondsproduktion," in Zeitschrift für das gesamte Kreditwesen .
Severin, Christin (2002), "Öffnung Für Den Vertrieb Von Drittfonds: Nicht
Immer Meinen Es Die Banken Wirklich Ernst," Neuer Zürcher Zeitung ,
January 29, 71.
Sharma, Arun (1996), "The Effect of Priming Cues in Sales Interactions:
Additional Perspectives," Journal of Personal Selling & Sales
Management , 16 (2), 49-52.
146

Sharma, Neeru and Paul G. Patterson (1999), "The Impact of Communication
Effectiveness and Service Quality on Relationship Commitment In,"
Journal of Services Marketing , 13 (2/3), 151.
Sharma, Neeru and Paul G. Patterson (2000), "Switching Costs, Alternative
Attractiveness and Experience as Moderators of Relationship
Commitment in Professional, Consumer Services," International Journal of Service Industry Management , 11 (5), 470.
Shaughnessy, Gary (2009), "Don't Kill Off Open Architecture and Choice,"
Financial Times , November 30, 6.
Simonson, Itamar (1990), "The Effect of Purchase Quantity and Timing on
Variety-Seeking Behavior," Journal of Marketing Research (JMR) , 27
(2), 150-62.
Sirdeshmukh, Deepak, Jagdip Singh, and Barry Sabol (2002), "Consumer Trust,
Value, and Loyalty in Relational Exchanges," Journal of Marketing , 66
(1), 15-37.
Skinner, Mark (2006), "Choice Galore with Open Access to Best Funds Open
Architecture Has Enabled Banks to Tap into Demand for Actively
Managed Funds, Accessing Europe's Most Talented Managers, Says Mark
Skinner," Financial Times , March 27, 6.
Smith, Michael F. and James M. Hunt (1987), "Disconfirmation of
Expectations: A Method for Enhancing the Effectiveness of Customer
Communications," Journal of Personal Selling & Sales Management , 7
(1), 9.
Sparks, John R. and Charles S. Areni (2002), "The Effects of Sales Presentation
Quality and Initial Perceptions on Persuasion: Multiple Role Perspective,"
Journal of Business Research , 55 (6), 517-28.
Speck, Kurt (2010), "Anlagefonds Sind Gewinner Der Krise," HandelsZeitung ,
February 3, 41.
Stafford, Thomas F. (1996), "Conscious and Unconscious Processing of Priming
Cues in Selling Encounters," Journal of Personal Selling & Sales
Management , 16 (2), 37-44.
147

Stafford, Thomas F., Thomas W. Leigh, and Leonard L. Martin (1995),
"Assimilation and Contrast Priming Effects in the Initial Consumer Sales
Call," Psychology & Marketing , 12 (4), 321-47.
Su, Wanru and Michael J. Tippins (1998), "Consumer Attributions of Product
Failure to Channel Members and Self: The Impacts of Situational Cues,"
Advances in Consumer Research , 25 (1), 139-45.
Sujan, Mita, James R. Bettman, and Harish Sujan (1986), "Effects of Consumer
Expectations on Information Processing in Selling Encounters," Journal of Marketing Research (JMR) , 23 (4), 346-53.
Swan, John E., Michael R. Bowers, and Lynne D. Richardson (1999),
"Customer Trust in the Salesperson: An Integrative Review and Meta-
Analysis of the Empirical Literature," Journal of Business Research , 44
(2), 93-107.
Swan, John E. and Richard L. Oliver (1989), "Postpurchase Communications by
Consumers," Journal of Retailing , 65 (4), 516.
Swanson, Scott R. and Scott W. Kelley (2001), "Attributions and Outcomes of the Service Recovery Process," Journal of Marketing Theory & Practice ,
9 (4), 50.
Tom, Gail and Scott Lucey (1995), "Waiting Time Delays and Customer
Satisfaction in Supermarkets," Journal of Services Marketing , 9 (5), 20.
Tormala, Zakary L. and Richard E. Petty (2004), "Source Credibility and
Attitude Certainty: A Metacognitive Analysis of Resistance to
Persuasion," Journal of Consumer Psychology (Lawrence Erlbaum
Associates) , 14 (4), 427-42.
Trommsdorff, Volker (2009), Konsumentenverhalten , Stuttgart: Kohlhammer.
Tsiros, Michael, Vikas Mittal, and Willaim T. Ross Jr (2004), "The Role of
Attributions in Customer Satisfaction: A Reexamination," Journal of
Consumer Research , 31 (2), 476-83.
Weber, Katy (2002), "Aufgerissene Pforten Für Fremdfonds," www.managermagazin.de
, March 10, 2002, Accessed: October 9, 2008,
148

http://www.manager-magazin.de/unternehmen/artikel/
0,2828,192442,00.html.
Weiner, Bernard (1985a), "An Attributional Theory of Achievement Motivation and Emotion," Psychological Review , 92 (4), 548-73.
Weiner, Bernard (1985b), ""Spontaneous" Causal Thinking," Psychological
Bulletin , 97 (1), 74-84.
Weitz, Barton A. (1981), "Effectiveness in Sales Interactions: A Contingency
Framework," Journal of Marketing , 45 (1), 85-103.
Weitz, Barton A., Harish Sujan, and Mita Sujan (1986), "Knowledge,
Motivation, and Adaptive Behavior: A Framework for Improving Selling
Effectiveness," Journal of Marketing , 50 (4), 174-91.
Wentzel, Daniel (2009), "The Effect of Employee Behavior on Brand
Personality Impressions and Brand Attitudes," Journal of the Academy of
Marketing Science , 37 (3), 359-74.
Wentzel, Daniel, Torsten Tomczak, and Andreas Herrmann (2008), "Wirkung
Des Mitarbeiterverhaltens Auf Die Markenpersönlichkeit," Marketing
ZFP (3), 133.
White, Tiffany Barnett (2005), "Consumer Trust and Advice Acceptance:The
Moderating Roles of Benevolence,Expertise, and Negative Emotions,"
Journal of Consumer Psychology (Lawrence Erlbaum Associates) , 15 (2),
141-48.
Wiecking, Kai (2003), "Geheimnis À La Fort Knox " www.managermagazin.de
, December 4, 2003, Accessed: January 15, 2011, http://www.manager-magazin.de/finanzen/geldanlage/
0,2828,276868,00.html.
Wimer, Scott and Harold H. Kelley (1982), "An Investigation of the Dimensions of Causal Attribution," Journal of Personality and Social Psychology , 43
(6), 1142-62.
Wong, Paul T. and Bernard Weiner (1981), "When People Ask "Why"
Questions, and the Heuristics of Attributional Search," Journal of
Personality and Social Psychology , 40 (4), 650-63.
149

Wood, John Andy, James S. Boles, and Barry J. Babin (2008a), "The Formation of Buyer's Trust of the Seller in an Initial Sales Encounter," Journal of
Marketing Theory & Practice , 16 (1), 27-39.
Wood, John Andy, James S. Boles, Wesley Johnston, and Danny Bellenger
(2008b), "Buyers' Trust of the Salesperson: An Item-Level Meta-
Analysis," Journal of Personal Selling & Sales Management , 28 (3), 263-
83.
Worsfold, Kate, Jennifer Worsfold, and Graham Bradley (2007), "Interactive
Effects of Proactive and Reactive Service Recovery Strategies: The Case of Rapport and Compensation," Journal of Applied Social Psychology , 37
(11), 2496-517.
Zaichkowsky, Judith Lynne (1985), "Measuring the Involvement Construct,"
Journal of Consumer Research , 12 (3), 341-52.
Zeithaml, Valarie A. (2000), "Service Quality, Profitability, and the Economic
Worth of Customers: What We Know and What We Need to Learn,"
Journal of the Academy of Marketing Science , 28 (1), 67-85.
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a.
Introductory text
Recently, you've decided to benefit from soaring equity prices on the German and Swiss stock markets. In order to do this, you've put aside a certain amount of money that you'd like to invest into suitable mutual funds. It's important to you that you take a well thought-through, sound investment decision. Therefore, you have made an appointment with the bank that you also use for your other financial transactions. A while ago, you've read that some banks offer a socalled "open product architecture". Clients of these firms can not only buy inhouse funds managed by the bank itself, but they can choose from a wider offering of funds that includes external products from third parties. You do not know whether your bank offers such third-party products, but you intend to find out.
You meet with Thomas Breiter, your client advisor, in the lobby of the bank's local branch. After a short welcome, Mr. Breiter accompanies you to his office where you take seat at a small conference table. After his assistant has brought you an espresso, you and Mr. Breiter discuss your financial requirements and expectations for a while. Then, the client advisor suggests to present you with a selection of products that are suitable for your goals. b.
High proactiveness, balanced product mix, high persuasiveness
Mr. Breiter rises from his chair, walks over to his PC and begins to call up a variety of fund profiles. He turns around to you and says: "Maybe you've heard that our firm has an 'open product architecture'? As you nod, he continues: "That means you don't get only in-house funds from us, but also those of other
151

providers. I'd suggest that I'll also include those third-party products in my selection."
With a focused expression, the client advisor's glance sweeps across the wide range of different funds that his PC is displaying. Finally, he prints 1-page profiles for some of the funds and takes these to the conference table, where he spreads the pages out in front of you. You leaf through the six documents. Three of the selected funds are in-house products of the advisor's bank, the other three are from different external fund managers.
You take a sip of your espresso and give the different fund profiles another glance. "Ok", you go and lean back in your chair, "why do you recommend these specific funds?" "Of course", Mr. Breiter smiles, "let me explain that to you." He than gives you a very accurate and transparent explanation on why he's chosen each product and what their individual pros and cons are. He also illustrates very clearly, how these different funds will offer you a good diversification of your risk. c.
High proactiveness, balanced product mix, low persuasiveness
Mr. Breiter rises from his chair, walks over to his PC and begins to call up a variety of fund profiles. He turns around to you and says: "Maybe you've heard that our firm has an 'open product architecture'? As you nod, he continues: "That means you don't get only in-house funds from us, but also those of other providers. I'd suggest that I'll also include those third-party products in my selection."
With a focused expression, the client advisor's glance sweeps across the wide range of different funds that his PC is displaying. Finally, he prints 1-page profiles for some of the funds and takes these to the conference table, where he spreads the pages out in front of you. You leaf through the six documents. Three
152

of the selected funds are in-house products of the advisor's bank, the other three are from different external fund managers.
You take a sip of your espresso and give the different fund profiles another glance. "Ok", you go and lean back in your chair, "why do you recommend these specific funds?" "Oh, well" says Mr. Breiter and shrugs. "It's pretty difficult to explain this in detail". He goes in to long-winded elaboration, using a lot of jargon. Mr. Breiter does not explain the different pros and cons of each product, but assures that he's got a "good feeling" about the selection he has recommended. d.
High proactiveness, biased product mix, high persuasiveness
Mr. Breiter rises from his chair, walks over to his PC and begins to call up a variety of fund profiles. He turns around to you and says: "Maybe you've heard that our firm has an 'open product architecture'? As you nod, he continues: "That means you don't get only in-house funds from us, but also those of other providers. I'd suggest that I'll also include those third-party products in my selection."
With a focused expression, the client advisor's glance sweeps across the wide range of different funds that his PC is displaying. Finally, he prints 1-page profiles for some of the funds and takes these to the conference table, where he spreads the pages out in front of you. You leaf through the six documents. Five of the selected funds are in-house products of the advisor's bank, only one is from an external fund manager.
You take a sip of your espresso and give the different fund profiles another glance. "Ok", you go and lean back in your chair, "why do you recommend these specific funds?" "Of course", Mr. Breiter smiles, "let me explain that to you." He than gives you a very accurate and transparent explanation on why he's chosen each product and what their individual pros and cons are. He also
153

illustrates very clearly, how these different funds will offer you a good diversification of your risk. e.
High proactiveness, biased product mix, low persuasiveness
Mr. Breiter rises from his chair, walks over to his PC and begins to call up a variety of fund profiles. He turns around to you and says: "Maybe you've heard that our firm has an 'open product architecture'? As you nod, he continues: "That means you don't get only in-house funds from us, but also those of other providers. I'd suggest that I'll also include those third-party products in my selection."
With a focused expression, the client advisor's glance sweeps across the wide range of different funds that his PC is displaying. Finally, he prints 1-page profiles for some of the funds and takes these to the conference table, where he spreads the pages out in front of you. You leaf through the six documents. Five of the selected funds are in-house products of the advisor's bank, only one is from an external fund manager.
You take a sip of your espresso and give the different fund profiles another glance. "Ok", you go and lean back in your chair, "why do you recommend these specific funds?" "Oh, well" says Mr. Breiter and shrugs. "It's pretty difficult to explain this in detail". He goes in to long-winded elaboration, using a lot of jargon. Mr. Breiter does not explain the different pros and cons of each product, but assures that he's got a "good feeling" about the selection he has recommended. f.
Low proactiveness, balanced product mix, high persuasiveness
Mr. Breiter rises from his chair, walks over to his PC and begins to call up a variety of fund profiles. He turns around to you and says: "Our in-house funds
154

are really excellent, I'll pick a few for you." You respond: "Yeah, sure,…but, would it be possible that you also show me a few funds from other companies?"
Mr. Breiter hesitates briefly and answers: "Ok...sure…if you like…then I can do that."
With a focused expression, the client advisor's glance sweeps across the wide range of different funds that his PC is displaying. Finally, he prints 1-page profiles for some of the funds and takes these to the conference table, where he spreads the pages out in front of you. You leaf through the six documents. Three of the selected funds are in-house products of the advisor's bank, the other three are from different external fund managers.
You take a sip of your espresso and give the different fund profiles another glance. "Ok", you go and lean back in your chair, "why do you recommend these specific funds?" "Of course", Mr. Breiter smiles, "let me explain that to you." He than gives you a very accurate and transparent explanation on why he's chosen each product and what their individual pros and cons are. He also illustrates very clearly, how these different funds will offer you a good diversification of your risk. g.
Low proactiveness, balanced product mix, low persuasiveness
Mr. Breiter rises from his chair, walks over to his PC and begins to call up a variety of fund profiles. He turns around to you and says: "Our in-house funds are really excellent, I'll pick a few for you." You respond: "Yeah, sure,…but, would it be possible that you also show me a few funds from other companies?"
Mr. Breiter hesitates briefly and answers: "Ok...sure…if you like…then I can do that."
With a focused expression, the client advisor's glance sweeps across the wide range of different funds that his PC is displaying. Finally, he prints 1-page profiles for some of the funds and takes these to the conference table, where he
155

spreads the pages out in front of you. You leaf through the six documents. Three of the selected funds are in-house products of the advisor's bank, the other three are from different external fund managers.
You take a sip of your espresso and give the different fund profiles another glance. "Ok", you go and lean back in your chair, "why do you recommend these specific funds?" "Oh, well" says Mr. Breiter and shrugs. "It's pretty difficult to explain this in detail". He goes in to long-winded elaboration, using a lot of jargon. Mr. Breiter does not explain the different pros and cons of each product, but assures that he's got a "good feeling" about the selection he has recommended. h.
Low proactiveness, biased product mix, high persuasiveness
Mr. Breiter rises from his chair, walks over to his PC and begins to call up a variety of fund profiles. He turns around to you and says: "Our in-house funds are really excellent, I'll pick a few for you." You respond: "Yeah, sure,…but, would it be possible that you also show me a few funds from other companies?"
Mr. Breiter hesitates briefly and answers: "Ok...sure…if you like…then I can do that."
With a focused expression, the client advisor's glance sweeps across the wide range of different funds that his PC is displaying. Finally, he prints 1-page profiles for some of the funds and takes these to the conference table, where he spreads the pages out in front of you. You leaf through the six documents. Five of the selected funds are in-house products of the advisor's bank, only one is from an external fund manager.
You take a sip of your espresso and give the different fund profiles another glance. "Ok", you go and lean back in your chair, "why do you recommend these specific funds?" "Of course", Mr. Breiter smiles, "let me explain that to you." He than gives you a very accurate and transparent explanation on why he's
156

chosen each product and what their individual pros and cons are. He also illustrates very clearly, how these different funds will offer you a good diversification of your risk. i.
Low proactiveness, biased product mix, low persuasiveness
Mr. Breiter rises from his chair, walks over to his PC and begins to call up a variety of fund profiles. He turns around to you and says: "Our in-house funds are really excellent, I'll pick a few for you." You respond: "Yeah, sure,…but, would it be possible that you also show me a few funds from other companies?"
Mr. Breiter hesitates briefly and answers: "Ok...sure…if you like…then I can do that."
With a focused expression, the client advisor's glance sweeps across the wide range of different funds that his PC is displaying. Finally, he prints 1-page profiles for some of the funds and takes these to the conference table, where he spreads the pages out in front of you. You leaf through the six documents. Five of the selected funds are in-house products of the advisor's bank, only one is from an external fund manager.
You take a sip of your espresso and give the different fund profiles another glance. "Ok", you go and lean back in your chair, "why do you recommend these specific funds?" "Oh, well" says Mr. Breiter and shrugs. "It's pretty difficult to explain this in detail". He goes in to long-winded elaboration, using a lot of jargon. Mr. Breiter does not explain the different pros and cons of each product, but assures that he's got a "good feeling" about the selection he has recommended.
157

a.
Introduction Text
Mr. Staiger has decided to put some money aside by taking out a life insurance.
It's important to him to choose the insurance product most appropriate for his needs. Therefore, he's made an appointment with the local agency of a large insurance company. A while ago, Mr. Staiger has read that some insurance firms offer a so-called "open product architecture". Their customers can buy the firm's own-label insurance policies, but also those of third-party providers. He intends to find out whether his insurance company will also offer such third-party products.
After Mr. Müller, the insurance agent, has welcomed him at his office, the two of them discuss Mr. Staiger's financial requirements and expectations for a while. Then, Mr. Müller suggests to present his customer with a selection of insurance products that will meet his requirements. b. Introduction Scene
Note: A (Agent), C (Client)
Client meeting room. Agent and client sit at a conference desk, facing each other at a 90 degrees angle. The agent is nodding.
A: "Allright. What I'll do now is to present a few different insurance options that should be suitable for you."
C: "Right, thanks."
158

c. Film with high proactiveness, balanced product mix, high persuasiveness
Scene 1: High Proactiveness
Fade-in.
Agent browses through a variety of brochures that are laid out on a sideboard.
He turns around to face the client.
A: "You may have heard that our company offers an "open product
architecture"?
B: "Yes, I've read that."
A: "That means you don't get only in-house policies from us, but also those of other providers. I'd suggest that I'll also include those third-party products in my selection."
C: "Yeah, I'd like that."
Fade-out.
Scene 2: Balanced Product Mix.
Fade-in.
With a focused expression, the client advisor's glance sweeps across the different brochures. He picks one, puts it back. He picks another one, nodding.
Cut.
The agent takes the selected documents and walks over to the conference table, where he spreads out 6 different brochures in front of the client.
A: "Ok, let's have a look at this."
C: "And these are all life-insurance products?"
A: "That's right. What I've done is, I've picked three of our own insurance
products and another three of external providers, such as Allianz."
C: "Okay, I see."
Fade-out.
159

Scene 3: High persuasiveness.
Fade-in.
The client sips at his water and takes a brief glance at the brochures
C: "Ok…"
Client reclines in his chair.
C: "Why do you recommend these particular policies? Could you explain that in a bit more detail?"
The agent smiles and makes an inviting gesture.
A: "Sure. I'll quickly give you an overview of the fundamental differences, and then we can go into the detail, okay?"
C: "Yeah, that's good."
The agent points at the individual brochures and elaborates in few words.
A: "These three products here are classical endowment policies, in which the payout sum depends almost exclusively on your fees. They vary in their terms and conditions, though, so we'll have to look at this closely. And these ones here are unit-linked life insurance policies. The return can be much better with these, but also much worse, since part of their capital is invested in mutual funds.
So much for a first overview – I'd suggest to go into the details now?"
C: "Yeah, I'd like that."
Fade-out. d. Film with high proactiveness, balanced product mix, low persuasiveness
Scene 1: High Proactiveness
Fade-in.
Agent browses through a variety of brochures that are laid out on a sideboard.
He turns around to face the client.
A: "You may have heard that our company offers an "open product
architecture"?
160

B: "Yes, I've read that."
A: "That means you don't get only in-house policies from us, but also those of other providers. I'd suggest that I'll also include those third-party products in my selection."
C: "Yeah, I'd like that."
Fade-out.
Scene 2: Balanced Product Mix.
Fade-in.
With a focused expression, the client advisor's glance sweeps across the different brochures. He picks one, puts it back. He picks another one, nodding.
Cut.
The agent takes the selected documents and walks over to the conference table, where he spreads out 6 different brochures in front of the client.
A: "Ok, let's have a look at this."
C: "And these are all life-insurance products?"
A: "That's right. What I've done is, I've picked three of our own insurance
products and another three of external providers, such as Allianz."
C: "Okay, I see."
Fade-out.
Scene 3: Low Persuasiveness of Reasoning
Fade-in.
The client sips at his water and takes a brief glance at the brochures
C: "Ok…"
Client reclines in his chair.
C: "Why do you recommend these particular policies? Could you explain that in a bit more detail?"
161

A: "Well…"
Agent shrugs.
A: "It's kind of difficult to explain that in detail."
Agent rubs his chin.
A: "You know, there's a thousand different factors that play into this, right.
Like, should this be a mixed life insurance, or not. Is it date-fixed, and if so, when? And should the whole thing feature investment funds? In that case, you can later always make a shift from your assets…into the fund, I mean. Or switching, there you make a reselection now and then."
C: "Can you explain this in more detail?"
A: "Well…they all have their own advantages. But you can trust me on this one. I've got the right feeling for what will be the right thing for you."
Fade-out.
e. Film with high proactiveness, biased product mix, high persuasiveness
Scene 1: High Proactiveness
Fade-in.
Agent browses through a variety of brochures that are laid out on a sideboard.
He turns around to face the client.
A: "You may have heard that our company offers an "open product
architecture"?
B: "Yes, I've read that."
A: "That means you don't get only in-house policies from us, but also those of other providers. I'd suggest that I'll also include those third-party products in my selection."
C: "Yeah, I'd like that."
Fade-out.
Scene 2: Biased Product Mix
162

Fade-in.
With a focused expression, the client advisor's glance sweeps across the different brochures. He picks one, puts it back. He picks another one, nodding.
Cut.
The agent takes the selected documents and walks over to the conference table, where he spreads out 6 different brochures in front of the client.
A: "Ok, let's have a look at this."
C: "And these are all life-insurance products?"
A: "That's right. What I've done is, I've picked five of our own insurance products."
He points at one brochure.
A: "And…then one more life insurance from another provider,…Allianz."
C: "Okay, I see."
Fade-out.
Scene 3: High persuasiveness.
Fade-in.
The client sips at his water and takes a brief glance at the brochures
C: "Ok…"
Client reclines in his chair.
C: "Why do you recommend these particular policies? Could you explain that in a bit more detail?"
The agent smiles and makes an inviting gesture.
A: "Sure. I'll quickly give you an overview of the fundamental differences, and then we can go into the detail, okay?"
C: "Yeah, that's good."
The agent points at the individual brochures and elaborates in few words.
163

A: "These three products here are classical endowment policies, in which the payout sum depends almost exclusively on your fees. They vary in their terms and conditions, though, so we'll have to look at this closely. And these ones here are unit-linked life insurance policies. The return can be much better with these, but also much worse, since part of their capital is invested in mutual funds.
So much for a first overview – I'd suggest to go into the details now?"
C: "Yeah, I'd like that."
Fade-out. f. Film with high proactiveness, biased product mix, low persuasiveness
Scene 1: High Proactiveness
Fade-in.
Agent browses through a variety of brochures that are laid out on a sideboard.
He turns around to face the client.
A: "You may have heard that our company offers an "open product
architecture"?
B: "Yes, I've read that."
A: "That means you don't get only in-house policies from us, but also those of other providers. I'd suggest that I'll also include those third-party products in my selection."
C: "Yeah, I'd like that."
Fade-out.
Scene 2: Biased Product Mix
Fade-in.
With a focused expression, the client advisor's glance sweeps across the different brochures. He picks one, puts it back. He picks another one, nodding.
164

Cut.
The agent takes the selected documents and walks over to the conference table, where he spreads out 6 different brochures in front of the client.
A: "Ok, let's have a look at this."
C: "And these are all life-insurance products?"
A: "That's right. What I've done is, I've picked five of our own insurance products."
He points at one brochure.
A: "And…then one more life insurance from another provider,…Allianz."
C: "Okay, I see."
Fade-out.
Scene 3: Low Persuasiveness of Reasoning
Fade-in.
The client sips at his water and takes a brief glance at the brochures
C: "Ok…"
Client reclines in his chair.
C: "Why do you recommend these particular policies? Could you explain that in a bit more detail?"
A: "Well…"
Agent shrugs.
A: "It's kind of difficult to explain that in detail."
Agent rubs his chin.
A: "You know, there's a thousand different factors that play into this, right.
Like, should this be a mixed life insurance, or not. Is it date-fixed, and if so, when? And should the whole thing feature investment funds? In that case, you can later always make a shift from your assets…into the fund, I mean. Or switching, there you make a reselection now and then."
165

C: "Can you explain this in more detail?"
A: "Well…they all have their own advantages. But you can trust me on this one. I've got the right feeling for what will be the right thing for you."
Fade-out.
g. Film with low proactiveness, balanced product mix, high persuasiveness
Scene 1: Low Proactiveness
Fade-in.
Agent browses through a variety of brochures that are laid out on a sideboard.
He turns around to face the client.
A: "So, erm, the insurance products of our company are really excellent. I'll pick a few for you right now."
C: "Yeah, sure…but, would it be possible that you also present a few products from other companies?"
The agent sighs and hesitates briefly.
A: "Okay…yes…sure…if you like…I can do that."
Fade-out.
Scene 2: Balanced Product Mix.
Fade-in.
With a focused expression, the client advisor's glance sweeps across the different brochures. He picks one, puts it back. He picks another one, nodding.
Cut.
The agent takes the selected documents and walks over to the conference table, where he spreads out 6 different brochures in front of the client.
A: "Ok, let's have a look at this."
C: "And these are all life-insurance products?"
166

A: "That's right. What I've done is, I've picked three of our own insurance
products and another three of external providers, such as Allianz."
C: "Okay, I see."
Fade-out.
Scene 3: High persuasiveness.
Fade-in.
The client sips at his water and takes a brief glance at the brochures
C: "Ok…"
Client reclines in his chair.
C: "Why do you recommend these particular policies? Could you explain that in a bit more detail?"
The agent smiles and makes an inviting gesture.
A: "Sure. I'll quickly give you an overview of the fundamental differences, and then we can go into the detail, okay?"
C: "Yeah, that's good."
The agent points at the individual brochures and elaborates in few words.
A: "These three products here are classical endowment policies, in which the payout sum depends almost exclusively on your fees. They vary in their terms and conditions, though, so we'll have to look at this closely. And these ones here are unit-linked life insurance policies. The return can be much better with these, but also much worse, since part of their capital is invested in mutual funds.
So much for a first overview – I'd suggest to go into the details now?"
C: "Yeah, I'd like that."
Fade-out.
167

h. Film with low proactiveness, balanced product mix, low persuasiveness
Scene 1: Low Proactiveness
Fade-in.
Agent browses through a variety of brochures that are laid out on a sideboard.
He turns around to face the client.
A: "So, erm, the insurance products of our company are really excellent. I'll pick a few for you right now."
C: "Yeah, sure…but, would it be possible that you also present a few products from other companies?"
The agent sighs and hesitates briefly.
A: "Okay…yes…sure…if you like…I can do that."
Fade-out.
Scene 2: Balanced Product Mix.
Fade-in.
With a focused expression, the client advisor's glance sweeps across the different brochures. He picks one, puts it back. He picks another one, nodding.
Cut.
The agent takes the selected documents and walks over to the conference table, where he spreads out 6 different brochures in front of the client.
A: "Ok, let's have a look at this."
C: "And these are all life-insurance products?"
A: "That's right. What I've done is, I've picked three of our own insurance
products and another three of external providers, such as Allianz."
C: "Okay, I see."
Fade-out.
168

Scene 3: Low Persuasiveness of Reasoning
Fade-in.
The client sips at his water and takes a brief glance at the brochures
C: "Ok…"
Client reclines in his chair.
C: "Why do you recommend these particular policies? Could you explain that in a bit more detail?"
A: "Well…"
Agent shrugs.
A: "It's kind of difficult to explain that in detail."
Agent rubs his chin.
A: "You know, there's a thousand different factors that play into this, right.
Like, should this be a mixed life insurance, or not. Is it date-fixed, and if so, when? And should the whole thing feature investment funds? In that case, you can later always make a shift from your assets…into the fund, I mean. Or switching, there you make a reselection now and then."
C: "Can you explain this in more detail?"
A: "Well…they all have their own advantages. But you can trust me on this one. I've got the right feeling for what will be the right thing for you."
Fade-out. i. Film with low proactiveness, biased product mix, high persuasiveness
Scene 1: Low Proactiveness
Fade-in.
Agent browses through a variety of brochures that are laid out on a sideboard.
He turns around to face the client.
A: "So, erm, the insurance products of our company are really excellent. I'll pick a few for you right now."
169

C: "Yeah, sure…but, would it be possible that you also present a few products from other companies?"
The agent sighs and hesitates briefly.
A: "Okay…yes…sure…if you like…I can do that."
Fade-out.
Scene 2: Biased Product Mix
Fade-in.
With a focused expression, the client advisor's glance sweeps across the different brochures. He picks one, puts it back. He picks another one, nodding.
Cut.
The agent takes the selected documents and walks over to the conference table, where he spreads out 6 different brochures in front of the client.
A: "Ok, let's have a look at this."
C: "And these are all life-insurance products?"
A: "That's right. What I've done is, I've picked five of our own insurance products."
.
He points at one brochure.
A: "And…then one more life insurance from another provider,…Allianz."
C: "Okay, I see."
Fade-out.
Scene 3: High persuasiveness.
Fade-in.
The client sips at his water and takes a brief glance at the brochures
C: "Ok…"
170

Client reclines in his chair.
C: "Why do you recommend these particular policies? Could you explain that in a bit more detail?"
The agent smiles and makes an inviting gesture.
A: "Sure. I'll quickly give you an overview of the fundamental differences, and then we can go into the detail, okay?"
C: "Yeah, that's good."
The agent points at the individual brochures and elaborates in few words.
A: "These three products here are classical endowment policies, in which the payout sum depends almost exclusively on your fees. They vary in their terms and conditions, though, so we'll have to look at this closely. And these ones here are unit-linked life insurance policies. The return can be much better with these, but also much worse, since part of their capital is invested in mutual funds.
So much for a first overview – I'd suggest to go into the details now?"
C: "Yeah, I'd like that."
Fade-out. j. Film with low proactiveness, biased product mix, low persuasiveness
Scene 1: Low Proactiveness
Fade-in.
Agent browses through a variety of brochures that are laid out on a sideboard.
He turns around to face the client.
A: "So, erm, the insurance products of our company are really excellent. I'll pick a few for you right now."
C: "Yeah, sure…but, would it be possible that you also present a few products from other companies?"
The agent sighs and hesitates briefly.
A: "Okay…yes…sure…if you like…I can do that."
171

Fade-out.
Scene 2: Biased Product Mix
Fade-in.
With a focused expression, the client advisor's glance sweeps across the different brochures. He picks one, puts it back. He picks another one, nodding.
Cut.
The agent takes the selected documents and walks over to the conference table, where he spreads out 6 different brochures in front of the client.
A: "Ok, let's have a look at this."
C: "And these are all life-insurance products?"
A: "That's right. What I've done is, I've picked five of our own insurance products."
He points at one brochure.
A: "And…then one more life insurance from another provider,…Allianz."
C: "Okay, I see."
Fade-out.
.
Scene 3: Low Persuasiveness of Reasoning
Fade-in.
The client sips at his water and takes a brief glance at the brochures
C: "Ok…"
Client reclines in his chair.
C: "Why do you recommend these particular policies? Could you explain that in a bit more detail?"
A: "Well…"
172

Agent shrugs.
A: "It's kind of difficult to explain that in detail."
Agent rubs his chin.
A: "You know, there's a thousand different factors that play into this, right.
Like, should this be a mixed life insurance, or not. Is it date-fixed, and if so, when? And should the whole thing feature investment funds? In that case, you can later always make a shift from your assets…into the fund, I mean. Or switching, there you make a reselection now and then."
C: "Can you explain this in more detail?"
A: "Well…they all have their own advantages. But you can trust me on this one. I've got the right feeling for what will be the right thing for you."
Fade-out.
173

Curriculum Vitae
Name
Date of Birth
Winfried Daun
18 February 1975 in Solingen, Germany
Education
2008 - 2011
1997
University of St. Gallen, Switzerland
Doctoral Candidate in Business Administration
Lund University, School of Economics and Management,
Lund, Sweden
Courses in Cross-Cultural Marketing und Human-
Computer Interaction
1994 - 2000
1985 - 1994
Work Experience
2011 -
2007 - 2011
UBS AG, Zurich, Switzerland
Director, Head Marketing Strategy and Development
UBS AG, Zurich, Switzerland
Director, Senior Branding Spezialist
2004 - 2006
2000 - 2004
University of Passau, Germany
Diplom-Kaufmann
Gymnasium Schwertstrasse, Solingen, Germany
Abitur
BBDO Consulting Suisse AG, Zurich, Switzerland
Manager
PricewaterhouseCoopers Unternehmensberatung GmbH
Consultant