International Journal of Production Research, 2017
Vol. 55, No. 17, 4912–4930, https://doi.org/10.1080/00207543.2016.1272765
Team diversity and manufacturing process innovation performance: the moderating role of
technology maturity
Jung Young Leea*, Morgan Swinkb and Temyos Pandejpongc
a
College of Business, Northern Illinois University, DeKalb, IL, USA; bNeeley Business School, Texas Christian University, Fort
Worth, TX, USA; cGraduate School of Management and Innovation, King Mongkut University of Technology Thonburi, Bangkok,
Thailand
(Received 16 December 2015; accepted 5 December 2016)
This research studies how technological maturity in manufacturing process innovation (MPI) projects moderates the
impacts of different types of team diversity on technical success. While researchers consider the variety aspect of team
diversity to be beneficial as a rich source of information, they consider disparity and separation to be detrimental as
sources of social barriers to information processing. However, demographic manifestations of diversity involve a
combination of these aspects. We therefore posit that technology maturity is an important moderator which may raise or
lower the influence of one diversity aspect over another. Specifically, we examine five manifestations of project team
diversity, including three types of variety (functional variety, full-time/part-time variety, location tenure variety) and two
types of disparity (education level disparity, experience level disparity). Results from 183 MPI projects in US companies
indicate that technology maturity negatively moderates the relationship between functional variety and MPI technical
performance. It positively moderates the relationships between experience level disparity and MPI technical performance,
and between location tenure variety and MPI technical performance. The impacts of education level disparity and
full-time/part-time variety do not appear to be moderated by technology maturity.
Keywords: project management; process innovation; team diversity; technology maturity; manufacturing process
1. Introduction
Both new product launches and production improvement efforts frequently engender manufacturing process innovation
(MPI) projects in which new manufacturing technologies are implemented. Technological innovations can range from
minor procedural modifications or tool changes to major software/hardware system upgrades in core manufacturing processes (Damanpour 1987, Carrillo and Gaimon, 2000). In order to achieve better problem solving and implementation
results, such projects require the flows and stocks of knowledge of team members (Carrillo and Gaimon 2000;
Subramaniam and Youndt 2005; Rogers 2003; Lee, Swink, and Pandejpong 2011; Zhen, Jiang, and Song 2011).
Accordingly, organisations seek to create diverse MPI project teams. Demographic, cognitive, and personality differences among team members will not only provide wide knowledge assets for innovation, but also accurately represent
end users of new technologies and conform to societal expectations of diversity (Dahlin, Weingart, and Hinds 2005).
However, attempts to enhance team diversity in MPI projects have been seriously challenged by the lack of evidence
for its value (Van Knippenberg and Schippers 2007). For instance, past empirical studies have produced surprisingly
mixed results, making team diversity a ‘double-edge sword’ (Harrison and Klein 2007; Horwitz and Horwitz 2007).
Recent meta-analyses also conclude that the correlation between team member diversity and team performance is near
zero or very weak (Stewart 2006; Hülsheger, Anderson, and Salgado 2009; Bell et al. 2011). In order to respond to this
challenge, many researchers promote elaborating possible contingency factors to clarify the connection between withinunit diversity and the unit-level outcome (Van Knippenberg, De Dreu, and Homan 2004; Horwitz and Horwitz 2007;
Hülsheger, Anderson, and Salgado 2009; Kearney, Gebert, and Voelpel 2009). This study thus considers a moderator,
technology maturity, in order to address the question of how to compose MPI project teams for successful implementations of new manufacturing technologies. We define technology maturity as the degree to which the functional characteristics of a focal manufacturing technology have been applied and proven by prior adopters. Technology maturity is an
important consideration for several reasons. First, manufacturing managers do vary in their willingness to adopt and
implement immature technologies. Prior to adoption, managers typically evaluate a technology’s maturity level,
*Corresponding author. Email: younglee@niu.edu
© 2016 Informa UK Limited, trading as Taylor & Francis Group
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investigating whether or not other organisations have already adopted it (Rogers 2003). While some firms tend to implement only technologies for which ample evidence of success and full rate production has been demonstrated (e.g. best
practices or off-the-shelf technologies) (DiMaggio and Powell 1983; Ketokivi and Schroeder 2004; Liu et al. 2010;
Wheeler and Ulsh 2010; EWI 2011), others are willing to act as lead adopters in choosing risky, immature technologies
because of potential first-mover advantages (Katila and Ahuja 2002; Wheeler and Ulsh 2010). Nonetheless, how to
effectively manage MPI projects involving different levels of technology maturity remain unexplored. Second, technology maturity potentially influences both the motivations and communication norms of team members, thereby influencing the effects of team diversity on project performance (Szulanski 1996; Rogers 2003). Perceptions of provenness or
riskiness are likely to influence levels of acceptance or scepticism among team members (DiMaggio and Powell 1983;
Liu et al. 2010; Rogers 2003). Combined with a specific team structure, these perceptions would then affect their information-seeking behaviours for problem solving (Szulanski 1996; Pelled, Eisenhardt, and Xin 1999). Despite the logically apparent interactions of technology maturity and team diversity, neither innovation nor team diversity literature has
examined them critically.
In addition to the investigation of technology maturity, this study augments the concepts and methods used for
research of team diversity: by refining diversity concepts and by measuring its more practical manifestations. Many
researchers point to mismatches between theoretical concepts of diversity and their operationalisations as another reason
for the mixed findings on the value of diversity (Harrison and Klein 2007; Bell et al. 2011). We thus employ the typology offered by Harrison and Klein (2007), and study the effects of variety and disparity as key dimensions of diversity
in the context of MPI projects. Harrison and Klein define variety as a ‘composition of differences in kind, source, or
category of relevant knowledge or experience among unit members’ (2007, 1203). They define disparity as a ‘composition of (vertical) differences in proportion of socially valued assets or resources held among unit members’ (2007,
1203). Specifically, we focus on five commonly occurring manifestations of diversity within an MPI project team
including: functional variety, full-time/part-time variety, location tenure variety, education level disparity and experience
level disparity. Designing their teams, managers would be able to manipulate these diversity characteristics more easily
than other demographic characteristics such as age, gender, or ethnicity.
Overall, our research contributes to both innovation and team diversity research streams by elaborating the level of
technology maturity under which a specific team member diversity element is particularly effective in conducting an
MPI project. Our findings also have practical implications. Along with the adoption of mature ‘best practices’ and existing automation solutions, manufacturers today are also pursuing numerous immature manufacturing technologies (e.g.
new forms of automation, additive manufacturing, materials forming, etc.) (EWI 2011). This research provides insights
for managers who seek to design proper MPI project teams to capitalise upon those different types of innovation
opportunities.
2. Literature review and theory development
2.1 Dimensions of team diversity and MPI performance
Team diversity is roughly conceptualised as the distribution of differences among members in any attribute, such as
demographic variables (e.g. age, gender, race), cognitive traits (e.g. values, skills, knowledge, functional background)
and personality (e.g. openness to experience) (Van Knippenberg and Schippers 2007). However, different theoretical perspectives suggest that different aspects of diversity (i.e. separation, disparity and variety) produce different effects on
performance, and require different methods of measurement (see Table 1).
Social categorisation and social stratification are two theoretical perspectives that suggest negative effects of team
diversity on group effectiveness. First, social categorisation theory views diversity as separation, defined as a ‘composition of differences in (lateral) position or opinion among unit members’ (Harrison and Klein 2007, 1203). Because people generally interact more with those who have similar preferences or personalities (Williams and O’Reilly 1998), a
diverse team tends to divide itself into distinguished subgroups via the social categorisation process. As a result, dissimilar subgroups may experience distrust, anger and frustration within the team (Pelled 1996; Jehn, Northcraft, and Neale
1999). Social stratification theory focuses on the disparity aspect of diversity (Harrison and Klein 2007). A high level
of disparity implies that a majority of resources, prestige or power is given to a small portion of team members. As a
result, the other team members might experience interruption on their tasks, less information about the project, more
threats of punishment, and suppressed chances of declaration (Keltner, Gruenfeld, and Anderson 2003). In sum, both
separation and disparity are seen to be unfavourable diversity aspects, because they generate relationship conflicts and
impede collaboration, which in turn increase turnover among team members.
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J.Y. Lee et al.
Table 1. Team diversity types (Adapted from Harrison and Klein (2007)).
Diversity
type
Meaning
Maximum
amount of
diversity
Foundational
theories
Examples
Separation
Differences in lateral position or
opinion among team members
Social
categorisation
Disagreement in opinions,
beliefs, values, and attitudes
especially regarding team goals
and processes
Disparity
Differences in vertical position or
proportion of socially valued assets
or resources held among team
members
Differences in kind or source of
relevant knowledge among team
members; unique or distinctive
information
Social
stratification
Inequality or relative
concentration in income,
status, prestige, decisionmaking authority, social power
Distinctive content expertise,
functional background, nonredundant network ties
Variety
Information
processing
Operational
index (Assumed
scale of
measurement)
Standard
deviation
(Interval)
Mean Euclidean
Distance
(Interval)
Coefficient of
Variation (Ratio)
Blau
(Categorical)
Entropy
(Categorical)
On the other hand, a theory of information processing addresses the concept of diversity as the variety of information brought by team members. Such variety is thought to have positive effects on cognitive task performance (Williams
and O’Reilly 1998; Van Knippenberg and Schippers 2007). The less overlap among team members’ information sets,
the more effectively members can collect higher quality decision alternatives and avoid suboptimal solutions (Brodbeck
et al. 2007). The presence of different knowledge stocks and perspectives might spur task-related conflicts, such as arguments on goals or procedures of tasks to finish a project (Jehn, Northcraft, and Neale 1999). However, as opposed to
the relational conflicts caused by either separation or disparity, these task-related conflicts can be beneficial to the final
outcomes of cognitive tasks. If the conflicts are not severe, team members will scrutinise the diverse ideas and opinions
for reconciliation (Pelled 1996; Pelled, Eisenhardt, and Xin 1999). As a result, they reach a greater understanding of
issues, and improve the overall quality of decisions made in an innovation project (Putnam 1994).
While the foregoing discussion treats separation, disparity and variety as distinct types of diversity, it has recently
been argued that each commonly occurring element of diversity in practice holds the potential to produce both beneficial
information effects and detrimental social effects (Van Knippenberg, De Dreu, and Homan 2004). For example, as
Harrison and Klein (2007) noted, factors such as gender and group membership tenure can be a clear expression of variety, as they are a representation of different types of knowledge and experience. However, they also represent different
behaviours, values, and means of communication of team members, which reflect separation. They also can characterise
the different positions and amounts of power/resources that team members possess, which reflect disparity. Drawing
upon this perspective, we conceptualise our variables according to their most salient dimensions (i.e. either disparity or
variety), while also considering the potential that each diversity variable will manifest multiple dimensions of diversity.
We then posit that technology maturity makes certain dimensions of a diversity variable more or less significant in
implementing MPI projects successfully.
2.2 Technology maturity as an important moderator
Extant literature on team diversity has examined various moderators that can represent the nature of projects, including
task routineness, task interdependence, and task complexity (e.g. Jehn, Northcraft, and Neale 1999; Pelled, Eisenhardt,
and Xin 1999; Schippers et al. 2003; Gardner, Gino, and Staats 2012). The main assumption is that such task-level
moderators can determine whether information differences among team members are appropriately extracted and
embraced, or otherwise, suppressed or even ignored (Kristof-Brown, Zimmerman, and Johnson 2005; Brodbeck et al.
2007). While prior research has refined team diversity theory, further research should be done in the context of implementing MPI projects, where not only task-level but also technology effects can significantly influence the motivations
and behaviours of team members.
We thus examine technology maturity, conceptualising it as the extent to which the functional characteristics of a
manufacturing process technology have been applied and proven by other organisations. This conceptualisation is based
on Rogers’s (2003) theory of innovation diffusion and Szulanski’s (1996) concept of unproveness. While different types
of innovations exist, including administrative innovations (i.e. changing an organisation’s structure or its administrative
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processes such as recruiting and budgeting) and ancillary innovations (i.e. new community services such as adult continuing education programmes of a library), our main focus is technological innovations associated with a new tool,
device, technique, and system (Damanpour 1987). Organisations are often driven to adopt immature manufacturing technologies by the belief that they can obtain newer and broader ranges of solutions to production problems (Katila and
Ahuja 2002). However, lacking a proven record of usefulness and stability, such technologies are not easily transferred
and accepted by the organisations and their employees (Szulanski 1996). Conversely, when a manufacturing technology
has a strong and visible record of successes, potential adopters are more likely to perceive the technology as mature and
advantageous (DiMaggio and Powell 1983; Liu et al. 2010; Rogers 2003). Team members who perceive different levels
of technology maturity likely exhibit different types of information-seeking behaviours to solve project-related problems,
in turn making a certain diversity aspect (i.e. disparity, separation, or variety) more or less critical in MPI projects
(Szulanski 1996; Pelled, Eisenhardt, and Xin 1999).
Despite its potential significance in the context of MPI implementation projects, technology maturity has not been
well operationalised and empirically examined by researchers. Practitioners assess manufacturing technology maturity
critically at the earliest stages of MPI projects, in order to identify any shortfalls and costs associated with immature
technologies and to develop proper risk management systems (Wheeler and Ulsh 2010). However, previous studies on
innovation have mainly focused on differences between incremental and radical innovation projects (e.g. Tatikonda and
Montoya-Weiss 2001; Peng, Heim, and Mallick 2012), with a concentration on the effects of technology novelty on
organisational learning.
In the next section, we explain our expectations regarding the ways that technology maturity moderates the relationships between five elements of team diversity. We conceptualise these diversity elements in terms of variety and disparity, respectively, since they are the most salient dimensions of diversity. However, as discussed earlier, it is important to
remember that each diversity element holds the potential for other dimensions of diversity at the same time, producing
both beneficial information processing effects and detrimental social effects (Van Knippenberg, De Dreu, and Homan
2004; Harrison and Klein 2007). Table 2 provides a summary of these theoretical arguments. Technology maturity is
Table 2. Summary of theoretical arguments.
Relevant theoriesa
Social categorisation
Diversity element
Similarity attraction
Functional variety
Full/Part-time variety
Location tenure variety
Educational disparity
Experience disparity
Impacts on knowledge
development and
dissemination
–b
–
–
–
–
–
•
•
•
a
•
Information processing Variation,
selection, retention
Law of requisite variety
+
+
+
+
+
–
•
Relationship to
technology
immaturity
Social stratification
Status hierarchy
Distributive justice and equity
Impedes interaction
(through distrust,
frustration,
miscommunication)
Diminishes morale and
cohesion
Increases non-productive
conflict (subgroup
cohesion)
Lowers psychological
safety
•
•
Especially damaging
when implementing
immature technologies
•
•
•
•
•
Promotes followership
Fosters information
hoarding, reduces
quality of
communication
Promotes task
interruptions
Increases internal
competition
Lowers psychological
safety
Suppresses declaration
•
Especially damaging
when implementing
immature technologies
•
•
•
•
Please refer Harrison and Klein (2007) for detailed explanations of these theories.
Minus and plus signs indicate the expected effect of the diversity element on MPI project performance.
b
Enriches knowledge
stocks, ideas, approaches,
and task orientations
Increases network
connections to knowledge
sources
Promotes productive
conflict
Stimulates more
comprehensive analysis
Especially valuable when
implementing immature
technologies
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J.Y. Lee et al.
then hypothesised as an environmental determinant in MPI projects by which the merits of richer information and more
diverse perspectives (i.e. variety) are superseded by the detriments of social differences among team members (i.e. separation or disparity).
2.2.1 Functional background variety
Functional background variety is greater when proportions of team members representing various functions are spread
more evenly across a greater number of functional areas. An MPI project team that has more cross-functional representation has greater access to the various function-specific information and knowledge to solve problems (Jehn, Northcraft,
and Neale 1999; Simons, Pelled, and Smith 1999). When implementing an immature technology, this variety would play
a critical role in enhancing the technical performance of MPI projects at least in two ways. First, multi-functional
knowledge helps a team evaluate the potential impacts, possible problems, and risks of the technology from diverse perspectives (Putnam 1994; Jehn, Northcraft, and Neale 1999; Simons, Pelled, and Smith 1999; Kong et al. 2015). As such
problems are likely to involve issues that exceed any single functional domain, the multi-functional expertise can comprehend and solve the problems faster and better (Brodbeck et al. 2007). Second, functional variety increases the communications between team members and outside experts (Ancona and Caldwell 1992). Since total requirements of an
immature technology are difficult to specify before the project begins, extended knowledge networks provide more intellectual resources to handle unexpected problems in the projects.
However, for projects which attempt to implement a mature technology, this functional variety might be less beneficial. Since most technological irregularities have been resolved by other organisations, teams would face fewer problems
and task-related conflicts (Rogers 2003). The reduced demands for in-depth information processing and inter-functional
debates make functional variety less valuable (Simons, Pelled, and Smith 1999). As a result, the darker side of functional diversity may become more prevalent: Functionally diverse teams could experience struggles for dominance
among different departments, separations in opinions, and relational conflicts, which are driven by department-specific
languages, priorities, and thought worlds (Ancona and Caldwell 1992; Dahlin, Weingart, and Hinds 2005; Harrison and
Klein 2007; Huang and Wang 2013). Therefore, we expect that the tendency of functional variety to enhance the technical performance of MPI projects with enhanced problem-solving capabilities decreases with the implementation of a
mature technology.
Hypothesis 1: Technology maturity moderates the relationship between team functional variety and the technical performance
of MPI projects: the positive influence of functional variety becomes weaker (i.e. less positive or even negative) in projects
involving more mature technologies.
2.2.2 Full-time/part-time team member variety
Due to the use of popular matrix structures and multiple-project environments for innovation, MPI projects teams frequently consist of both full-time and part-time team members (Ford and Randolph 1992; Laslo 2010). However, this
type of variety has not received attention in the team diversity literature. For projects implementing immature technologies, a variety of full-time and part-time members is expected to enhance technical performance. Full-time team members offer focused commitment and constant attention to project needs. On the other hand, part-time members are often
experts who have scarce skills and knowledge that are demanded by multiple projects across diverse areas of the organisation (Hendriks, Voeten, and Kroep 1998; Laslo 2010). From the information processing perspective, they serve as conduits for knowledge spillovers and cross-project learning (Davis et al. 1977; Laslo 2010). The richer sets of knowledge
provided by the mix of full-time and part-time members could enable project teams to effectively deal with irregularities
associated with the immature technologies.
However, when MPI projects involve mature technologies, the knowledge-related benefits of variety could be mitigated by the negative social effects brought by diversity in membership status: social effects (i.e. separation and disparity) among team members due to differences in levels of commitment and in authority to access key information
(Harrison and Klein 2007). For instance, people often think that it is promising and easy to implement mature technologies because of the presence of ample guidelines and proven instances (DiMaggio and Powell 1983; Leonard-Barton
and Deschamps 1988). The psychological comfort of team members in such a project could make part-time team members less committed, and more interested in other challenging projects to which they are assigned (Laslo 2010). As a
consequence, full-time members might deem part-time members unreliable or superfluous, and might either intentionally
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or unintentionally withhold information or communicate less with part-time members. The resulting social separation
and information disparity across groups would tend to negate the variety-based, information processing advantages of
having a mix of workers on the project team. Accordingly, we offer the following hypothesis.
Hypothesis 2: Technology maturity moderates the relationship between full-time/part-time team member variety and the technical performance of MPI projects: the positive influence of full-time/part-time variety becomes weaker (i.e. less positive or even
negative) in projects involving more mature technologies.
2.2.3 Location tenure variety
Innovation projects often make new hires and re-locate personnel from other sites in order to bring new perspectives
and knowledge stocks for problem solving (Von Hippel 1994). Based on the information processing theory, the distinction between the newcomers and team members who have a history of working at the project site can represent information variety within an MPI project team. For reasons similar to the foregoing arguments for other variety elements, MPI
projects that implement immature technologies would receive greater benefits from location tenure variety. While existing team members have a profound understanding about the current manufacturing system at the site, newcomers from
other sites/organisations are not as locked-in to the status quo as those incumbent team members might be (Von Hippel
1994). Newcomers also booster knowledge transfer and mutual learning across team boundaries (Katz and Allen 1982;
Kogut and Zander 1992; Tsai 2001). This enhanced knowledge capability and creativity of a team will be crucial to
attack irregularities associated with the immature technologies, thereby improving the technical performance of MPI
projects.
Interestingly, however, some empirical research on organisation tenure diversity suggests a different story: Detrimental separation effects of location tenure variety might dominate informational benefits in the case of immature technologies. For instance, differences in years of experience at the project site generate emotional conflicts due to different
attitudes and values of team members, which result in employee turnover and diminishing team performance (Pelled
1996; Pelled, Eisenhardt, and Xin 1999; Harrison and Klein 2007). A ‘not-invented-here’ syndrome also implies similar
relationship conflicts caused by tenure diversity: Resident members often think that they possess a monopoly on the specialised knowledge needed to deal with current manufacturing systems and do not consider new information brought by
‘outsiders’ seriously (Katz and Allen 1982). This tendency in which tenure diversity generates interpersonal frustration
becomes stronger if members work on highly uncertain tasks (Pelled, Eisenhardt, and Xin 1999). From this perspective,
a mix of incumbent members and new personnel at the site for immature technology implementation could engender
severe separation effects that offset positive informational variety benefits. The foregoing arguments lead to the following two competing hypotheses.
Hypothesis 3a: Technology maturity moderates the relationship between team member location tenure variety and the technical
performance of MPI projects: the positive influence of location tenure variety becomes weaker (i.e. less positive or even negative) in projects involving more mature technologies.
Hypothesis 3b: Technology maturity moderates the relationship between team member location tenure variety and the technical
performance of MPI projects: the positive influence of location tenure variety becomes weaker (i.e. less positive or even negative) in projects involving more immature technologies.
2.2.4 Education level disparity
Differences in team members’ education levels can define an MPI project team’s social stratification (Harrison and Klein
2007). Typically, more educated team members are in higher positions, and engage in more conceptual and integrative
tasks (e.g. engineering design) which require intense information processing (Der Foo, Kam Wong, and Ong 2005). One
the other hand, less educated members tend to work on more practical tasks (e.g. repairing or maintenance) within an
MPI project (Der Foo, Kam Wong, and Ong 2005). We contend that this education disparity can generate emotional
and relational conflicts which are more likely to be unleashed by uncertain project environments associated with immature technologies. While highly educated people tend to be more receptive to the uncertainties and risks of new technologies, less educated people are more sceptical and unwilling to embrace them (Kimberly and Evanisko 1981;
Wiersema and Bantel 1992). Those less educated members may then become more reluctant to take responsibility for
project tasks, less willing to express their ideas, and more likely to passively follow the opinions and decisions of highly
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educated members (Kahn 1990; Edmondson and Roloff 2009). As a result, an educationally diverse team could not fully
utilise the merits of multiple sources information, vocabularies, and cognitive patterns for problem solving provided by
the entire team.
When a mature technology is implemented, the potential for the voice suppression of less educated members likely
diminishes. Due to ample evidence of effectiveness of the technology, less-educated members are more likely to perceive it as valuable, safe, useful, and legitimate (Rogers 2003). They also are more likely to actively participate in discussions and decision-making related to the technology (Edmondson and Roloff 2009). An MPI project team whose
members represent a wide range of education levels could then enjoy broader sets of vocabularies and diverse cognitive
patterns for information search (Cohen and Levinthal 1990; Drach-Zahavy and Somech 2002; Dahlin, Weingart, and
Hinds 2005). These benefits from a high level of information variety would result in better problem solving and implementation outcomes of the technology (Simons, Pelled, and Smith 1999; Der Foo, Kam Wong, and Ong 2005). The
foregoing discussions lead to the following hypothesis.
Hypothesis 4: Technology maturity moderates the relationship between team member education level disparity and the technical
performance of MPI projects: the negative influence of education disparity becomes weaker (i.e. less positive or even negative)
in projects involving more mature technologies.
2.2.5 Experience level disparity
Experience level disparity, which has not been well examined in the literature, refers to differences in the years of working experience of team members. People with different working histories possess different understandings of organisational events, technologies, policies, and various ways to accomplish the same project tasks (Cohen and Levinthal 1990;
Schenk, Vitalari, and Davis 1998). In addition to possible separation effects, differences in experience level tend to display disparity in the authority and supportive resource levels within a team (Harrison and Klein 2007).
We expect that the implementation of immature technologies would make the negative social effects of experience
level disparity stronger, leading to poor decision-making in MPI projects. As discussed above with education level disparity, the ambiguities inherent in the technologies tend to suppress the voices of less experienced team members. Overwhelmed by the high level of uncertainties and risks, team members with less experience, lower status, and less power
tend to simply follow decisions made by members with longer years of experience and higher managerial positions
(Kahn 1990; Edmondson and Roloff 2009). This limits diverse viewpoints, voices, and engagement within an MPI project team, and impedes effective information search for problem solving (Taylor and Greve 2006).
On the other hand, the implementation of a mature manufacturing technology would make the foregoing issues of
experience disparity less problematic. While highly experienced members are able to generate more realistic strategies
for applying a mature technology, less experienced novices could play complementary roles by providing inputs to break
knowledge equilibrium within a project team (March 1991; Schenk, Vitalari, and Davis 1998). Novices are likely more
aware of recent research findings, technological trends, and methods. Therefore, they might understand the basic contents of new technologies better than others with older, less relevant knowledge. Since the perceived risk of a mature
technology is low, less experienced members then easily participate in discussions, and express their opinions with less
fear (Kahn 1990; Edmondson and Roloff 2009). The improved communication allows MPI project teams to integrate
each individual’s unique reflection and relevant experience on the current manufacturing systems in order to build more
concrete knowledge for advanced decision-making (Taylor and Greve 2006; Anand, Ward, and Tatikonda 2010;
Gardner, Gino, and Staats 2012).
Hypothesis 5: Technology maturity moderates the relationship between team member experience level disparity and the technical performance of MPI projects: the negative influence of experience disparity becomes weaker (i.e. less positive or even negative) in projects involving more mature technologies.
3. Methodology
3.1 Sample
In order to test our hypotheses, we conducted a cross-sectional, on-line survey in accordance with Dillman’s (2000) protocol. Targeted respondents were mid-to-high level managers in manufacturing companies (SIC 20-39) who were likely
to have solid information about MPI projects and associated teams. We identified possible participants by using job titles
from the mailing list of the Society of Manufacturing Engineers. Search terms included manager, senior, director, leader,
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engineering, project, process, system, production, operations, plant, and quality. Each survey recipient was given the definition of an MPI project, with examples, and asked to describe a specific MPI project in which they had recently participated and had been completed. The first invitation email was estimated to have reached a total of 2870 managers. After
employing four email reminders, we received 183 usable responses. This results in an initial estimate of an overall
responses rate of 6.3%.
The sample of 183 MPI projects includes a wide range of manufacturing processes: 44 teams (24%) worked on a
job shop process innovation, 61 teams (33%) worked on a batch process innovation, 66 teams (36%) worked on an
assembly line process innovation and 12 teams (7%) worked on a continuous flow process innovation projects. The
scope of operations affected by the MPI projects varied from a single work centre (12%), to a production line (38.3%),
a department (27.3%), and the entire plant (22.4%). Examples of MPI projects include transferring a current production
line to an automated one, combining four different processes to one complete manufacturing cell, installing a new power
paint system, installing a new welding work centre, implementing new high speed tools and high pressure coolants,
removing some operations by installing a new tooling, updating existing equipment to use a new control and handling
system, implementing a new surface treatment technology, implementing new leak testing equipment for online inspection of finished goods, replacing a liquid coating system with powder coating, etc.
We evaluated non-response bias in three ways. First, we contacted 359 randomly-selected non-respondents by phone
to identify reason for non-response. Our conversations with the managers indicated that those non-respondents also possessed relevant positions and knowledge with their MPI projects. 66 eligible managers stated reasons for non-response
including confidentiality, lack of time, and lack of interest. The other 293 managers were ineligible due to the strict
qualifications we made in order to raise the validity of the data. Thus, only 66 of the 359 selected non-respondents
(18.4%) were both available and eligible to complete our survey. Assuming that this proportion identified in the followup is representative of our target population, the estimated effective response rate would be 183/(2870 × 18.4%) =
33.7%. This high response-rate reduces the risk of non-response bias. Second, we conducted tests for differences in
plant size and in industry (SIC) across respondents and non-respondents. Third, we compared early and late respondents’ scores for differences in plant size and for project performance measures (Armstrong and Overton 1977). None of
the above tests indicated significant differences. We therefore concluded that there are no serious demographic or project-related non-response biases present in the data.
3.2 Measures
An often used approach to measure diversity is to assess team members’ perceptions of diversity. For example, perceptual measures of functional variety have been widely employed by empirical operations management research (e.g.
Pinto, Pinto, and Prescott 1993; Chen and Paulraj 2004; Ketokivi and Schroeder 2004). Such measures, however, can
inflate explanatory power through common method variance and respondents’ overestimations of diversity (Harrison and
Klein 2007; Hülsheger, Anderson, and Salgado 2009). Indeed, correlations between perceived and actual measures of
team variety appear to be low (Harrison et al. 2002). Therefore, we used only objective measurements for the three variety and two disparity variables.
3.2.1 Variety
Two representative measurements for variety are the Entropy index (VE) (Teachman 1980) and Blau’s index (VB) (Blau
1977).
VE ¼ P
½Pi lnðPi Þ, where Pi stands for the proportion of the team that has each variety characteristic.
VB ¼ 1 P
Pi2 , where Pi stands for the proportion of the team that has each variety characteristic.
The Entropy index is always larger than Blau’s index, and tends to be more sensitive to the presence of more categories, rather the presence of even distributions among a smaller number of categories. Nonetheless, both measures have
been widely used to measure information richness in the literature on team diversity (e.g. Ancona and Caldwell 1992;
Jehn, Northcraft, and Neale 1999; Boone and Hendriks 2009; Kearney, Gebert, and Voelpel 2009). Harrison and Klein
(2007) offer further discussions and comparisons of these two indexes.
For functional variety, the survey asked each respondent to report the percentage of the personnel on the referred
MPI project team from the following five functional categories: (1) production and planning (e.g. manufacturing
engineering, quality control, production control, manufacturing department, management information system, etc.);
4920
J.Y. Lee et al.
(2) purchasing; (3) product design; (4) maintenance; (5) others. We measured full-time/part-time variety by asking the
number of staffs assigned full-time to the project and the number of staffs assigned on a part-time basis to the project.
Finally, we measured location tenure variety by asking the percentage of the personnel on the team who worked at the
implementation site before the project began.
3.2.2 Disparity
Often used as an objective measure of income inequality in the sociology literature, the coefficient of variation (CV) is
the primary measure for disparity (Harrison and Klein 2007).
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ffi
P
CV ¼ ½ ½Di Dmean 2 =n Dmean , where Di stands for the level of a socially valued or desired resource (e.g. pay, power,
prestige, status, position).
Accordingly, we calculated education level disparity by asking the percentage of the personnel on the MPI project
team who had the following educational backgrounds and assigning appropriate values of Di: (1) a high school degree
(Di = 0); (2) a 2-year associate degree (Di = 2); (3) an undergraduate degree (Di = 4); (4) a graduate degree (Di = 6). We
also measured experience level disparity by asking the percentage of the personnel on the MPI project team with the
following experience level categories and assigning the median experience level of each category as D: (1) less than
5 years of work experience (Di = 2.5); (2) between 5 and 15 years of work experience (Di = 10); (3) more than 15 years
of work experience (Di = 20).
3.2.3 Technology maturity
Three items for technology maturity, the moderator variable in this study, were developed from the extant literature on
knowledge proven-ness and diffusion of new technology (e.g. Szulanski 1996; Tatikonda and Montoya-Weiss 2001;
Rogers 2003). Using a five-point Likert scale (1, ‘strongly disagree’, to 5, ‘strongly agree’), managers rated the statements given in Table 3.
Table 3. Construct measures validity and reliability analysis.
Items
Technology maturity (CR = 0.81; AVE = 0.68)
Before we implemented this practice/technology, its effectiveness had been demonstrated by successful adoptions
in many other organisations
This practice/technology had been implemented successfully in other organisations before we adopted it
Irregularities of this practice/technology had been resolved before we adopted it. (dropped)
MPI Project Technical Performance (CR = 0.86; AVE = 0.55)
Using the new practice/technology, we achieved greater productivities than we originally planned
The new practice/technology improved the quality of our process more than we had originally expected it would
The new practice/technology improved our responsiveness more than we had hoped
The new practice/technology achieved significantly better overall technical improvements than we had originally
expected it would (dropped)
The new practice/technology performed significantly better than we had originally expected it would
The new practice/technology was not as effective or efficient as we had originally hoped it would be (reverse
coded)
Team member expertise (CR = 0.75; AVE = 0.50)
Team members were highly skilled
Team members were considered among the best people in the organisation
Team members were experts in their particular jobs and functions
Each individual on the implementation team had useful experience (dropped)
Standardised
loading
0.881
0.766
0.873
0.686
0.710
0.762
0.657
0.727
0.716
0.671
Notes: Overall model fit: χ2 = 58.001; df = 33; p < 0.005; CFI = 0.949; GFI = 0.942; IFI = 0.951; TLI = 0.931; 90% interval of
RMSEA = (0.036, 0.091); CR = composite reliability; AVE = average variance extracted.
International Journal of Production Research
4921
3.2.4 MPI project performance
The main purpose of MPI projects is to enhance the overall capabilities of manufacturing systems, as new technologies
help firms and plants improve their multiple capabilities simultaneously (Schmenner and Swink 1998). MPI Project Performance is defined as the degree to which an MPI project achieved or exceeded various operational goals. We examined earlier studies on process innovation projects (e.g. Cagliano and Spina 2000; Swink and Nair 2007) to identify
how to measure the overall manufacturing performance improvement. The six items shown in Table 3 were developed
to reflect various dimensions such as efficiency, quality, responsiveness, etc. They were rated based on a five-point
Likert scale (1, ‘strongly disagree’, to 5, ‘strongly agree’).
3.2.5 Control variables
We included many control variables. First, we measured team size as the sum of the numbers of full-time employees
and half of the number of part-time employees on the team at its peak employment. Team size is a key control variable
in prior research, since team diversity tends to increase with the number of members in the team (e.g. Simons, Pelled,
and Smith 1999; Boone and Hendriks 2009). Second, we controlled for the percentage of people with a graduate degree,
the percentage of people with more than 15 years of work experience, and overall team member expertise. These variables assess and control for the mean level of each team’s cognitive ability, which potentially influences project outcomes (Bell et al. 2011). Third, we measured project radicalness, or the degree of learning caused by the MPI project,
with a five-point Likert-type scale, “In order to use the new practice/technology, the operators/users had to learn new
skills and procedures.” This measure of project radicalness is similar to measures of project/technology novelty in the
new product development (NPD) literature (e.g. Tatikonda and Montoya-Weiss 2001; Peng, Heim, and Mallick 2012). It
is important to control for the effects of this variable, since it taps the magnitude and difficulty of change dictated by
the MPI project. While project radicalness is estimated at the team or organisation level, technology maturity is determined at the industry level. Even if a mature technology is adopted, for example, team members may still experience a
high level of transition when unfamiliar processes and procedures are needed for implementation. Fourth, we controlled
the type of manufacturing process, as the performance of an MPI project might be influenced by the level of customisation and the level of production volume within a plant. As stated earlier, there are four manufacturing process types represented in our sample. Therefore, we created three dummy variables. Last, we controlled for project scope, as it can
influence the difficulty and uncertainty of an MPI project as well. Three dummy variables were created to indicate the
four organisational levels at which projects were undertaken, including single work station (base case), production line,
department and plant(s).
3.2.6 Reliability and validity
We executed a confirmatory factor analysis (CFA) using AMOS 21 to examine reliability and validity of our measures
for technology maturity and technical performance of MPI projects. After dropping three items due to high cross loadings, we were able to obtain the expected three-factor measurement model that fit the data well (χ2 = 58.001; df = 33;
p < 0.005; CFI = 0.949; GFI = 0.942; IFI = 0.951; TLI = 0.931; 90% interval of RMSEA includes 0.05) (see Table 3).
All of factor loadings were above 0.5 and statistically significant, which suggests acceptable unidimensionality and convergent validity for the factors (Bagozzi, Yi, and Phillips 1991). The composite reliability for each factor was well
beyond the recommended minimum value of 0.7 (Fornell and Larcker 1981). The average variance extracted for each
factor was above 0.5, and was greater than the squared correlation between the two factors. Therefore, evidence for discriminant validity was acceptable (Fornell and Larcker 1981).
As a criterion validity check for the perceptual measures of MPI project performance, we correlated the values with
a more objective measure, project payback period values (in terms of years). We found a statistically significant correlation between the two measures, which is 0.151 (n = 169, p-value < 0.05). This result indicates that successful MPI projects tend to generate returns within a reduced period of time.
Following Podsakoff et al. (2003), we used several techniques to control and assess common method bias. First, our
measures were developed from an extensive literature review, and they were examined for clarity by five managers from
different manufacturing companies; each had participated in many past MPI projects. Second, we physically separated
diversity measures from MPI technical performance measures in the questionnaire. Between them, there were more than
20 other questions and a time lag (i.e. temporal separation), as well as a different cover page (i.e. psychological separation) to remind the respondent of definitions of key terms used in the survey. Third, we used different scale formats for
measures of diversity, technology maturity, and project technical performance, which include a mix of objective and
4922
J.Y. Lee et al.
latent factors. Fourth, we applied a more rigorous version of Harman’s one-factor test to check for the presence of common method bias. The χ2 difference between the original measurement model with three latent factors and a new measurement model with one common factor was statistically significant (Δχ2 = 326.999, df = 11, p-value < 0.001). Given
this result, we conclude that the risk of common method bias is not serious (Podsakoff et al. 2003).
4. Analysis and results
Table 4 shows the descriptive statistics and correlations among study variables.1 In order to test our hypotheses, we used
hierarchical ordinary least square regression analysis in which predictors are entered in a sequential order. Control variables are first entered into a regression model, then main variables (diversity elements and technology maturity), and
finally the interaction terms of variables. We mean-centred all variables to minimise possible variance inflation from
interaction terms (Aiken and West 1991). Table 5 summarises the results, with technical performance of MPI projects as
the dependent variable. All of the variance inflation factors (VIF) are between 1 and 5, which suggest that our predictors
are not highly correlated. Therefore, multicollinearity is not a major concern in this study. Model 1 presents the results
with variety variables operationalised by Blau’s index, while Model 2 shows the results computed using the Entropy
index. The results of both models are very consistent in terms of regression coefficients and their statistical significance
patterns. For brevity, we discuss only the results shown in Model 1.
Steps 1 and 2 in Model 1 enter control variables and main effect variables, respectively. None of the team diversity
main effects is significant. This was expected, given the competing variety/separation/disparity effects we described
above, and the similar findings in prior studies (Stewart 2006; Hülsheger, Anderson, and Salgado 2009; Bell et al.
2011). Step 3 in Model 1 shows that the addition of interaction terms explains a significant amount of variance in the
technical performance of MPI projects beyond that accounted for by control variables and main effect variables
(ΔR2 = 0.097, p < 0.01). Three out of five interaction terms are statistically significant.
First, supporting hypothesis 1, the interaction between functional variety and technology maturity is negative and
statistically significant (β = –0.200, p < 0.01). Figure 1 plots the relationship between functional variety and technical
performance of MPI projects under conditions of high and low technical maturity (one standard deviation above and
below the mean, respectively) (Aiken and West 1991). This plot shows that functional variety is positively related to
MPI technical performance when the focal technology is immature. However, if the technology is mature, greater functional variety is negatively associated with project performance.
Second, the interaction between location tenure variety and technology maturity is positive and statistically significant (β = 0.158, p < 0.05). The interaction plot given in Figure 2 shows that location tenure variety is beneficial in projects involving mature technology and detrimental in projects involving immature technology, supporting hypothesis 3b.
Third, consistent with hypothesis 5, technology maturity positively moderates the relationship between experience
level disparity and technical performance of MPI projects (β = 0.235, p < 0.01). Figure 3 shows the corresponding interaction plot, indicating that greater experience level disparity is positively associated with performance in projects involving mature technology. However, if the technology is immature, greater experience disparity is negatively associated
with MPI technical performance.
Contrary to hypothesis 2 and 4, the results provide no evidence of significant moderating effects of technology
maturity on the relationships between either full-time/part-time variety or education level disparity and project
performance.
5. Discussion
5.1 Theoretical implications
Our study contributes to current research on team diversity and MPI in several ways. First, our findings are consistent
with recent meta-analysis results that show non-significant or weak main effects of diversity variables on team performance (Stewart 2006; Hülsheger, Anderson, and Salgado 2009; Bell et al. 2011). This reemphasises the need for a contingency approach to specify environments in which diversity variables play more critical roles. To our knowledge, this
study is the first to conceptualise, operationalise, and empirically examine the concept of technology maturity as a significant moderator for the relationships between diversity variables and MPI project performance.
Second, our study identifies a wide set of commonly occurring diversity elements within MPI project teams, and
investigates them based on a modern perspective of diversity which assumes that each element entails both negative
social effects and positive information effects. Those competing effects are clarified by adopting the moderator, technology maturity. Specifically, we found that functional variety is beneficial when MPI projects implement an immature
b
3.86
48.03
12.96
3.88
13.06
3.32
0.39
0.29
0.13
0.40
0.35
3.65
0.92
31.92
21.91
0.66
19.95
1.02
0.20
0.20
0.19
0.22
0.20
0.76
SDa
1
0
0
1
2
1
0
0
0
0
0
1
Min
5
100
100
5
162
5
0.75
0.5
0.5
0.96
0.66
5
Max
−0.01
0.06
0.17*
−0.00
−0.03
0.11
0.18*
0.07
0.01
0.06
0.17*
1
0.00
0.22*
−0.04
−0.05
−0.14
−0.18*
−0.03
0.02
−0.47**
−0.10
2
0.05
−0.01
0.01
0.04
0.07
0.08
−0.21**
0.07
0.14
3
−0.05
−0.07
0.05
0.11
0.02
−0.08
−0.16*
0.16*
4
−0.05
0.05
−0.08
0.03
0.01
0.08
−0.16*
5
−0.14
−0.05
0.11
0.02
−0.01
−0.01
6
0.06
0.22**
−0.05
0.20**
−0.10
7
0.11
−0.08
0.13
0.05
8
−0.17*
0.16*
−0.07
9
0.08
0.05
10
0.11
11
*p < 0.05; **p < 0.01.
a
SD (Standard Deviation).
b
These variables are composite values, obtained from the average of the items of each factor. While this approach is preferred for generalisability (Hair et al. 2010), another way
to calculate the composite values is obtaining factor scores after CFA (Hair et al. 2010). We found that both ways generate very consistent results in Tables 4 and 5.
1. Radicalness
2. % of over 15 years
3. % of graduate degrees
4. Member expertise b
5. Team size
6. Technology maturity b
7. Functional variety
8. Full/Part-time variety
9. Location tenure variety
10. Education disparity
11. Experience disparity
12. MPI project technical performance
Mean
Table 4. Descriptive statistics and correlations.
International Journal of Production Research
4923
4924
J.Y. Lee et al.
Table 5. Regression analyses for moderation of the relationship between variety and MPI project technical performance by
technology maturity.
Model 1: Blau’s index
Step 1: Control variables
Team size
Radicalness
% of team members with graduate degrees
% of team members with over 15 year experience
Team member expertise
Job shop process
Batch process
Assembly process
Product line scope
Department scope
Plant(s) scope
Step 2: Main effects
Functional variety
Full-time/Part-time variety
Location tenure variety
Education level disparity
Experience level disparity
Technology maturity
Step 3: Interactions
Functional variety × Technology maturity
Full/Part-time variety × Technology maturity
Location tenure variety × Technology maturity
Education level disparity × Technology maturity
Experience level disparity × Technology maturity
R2
ΔR2
F
Step 2
Step 3
VIFa
1.081
1.202
1.155
1.468
4.124
4.969
4.851
2.705
2.596
2.492
1.181
−0.167*
0.125+
0.165*
−0.131
0.175*
−0.060
−0.224
−0.225
−0.126
−0.035
−0.145
−0.151*
0.108
0.197**
−0.099
0.179*
−0.074
−0.232
−0.245
−0.133
−0.065
−0.202+
1.085
1.212
1.156
1.471
4.128
4.981
4.844
2.720
2.610
2.501
1.180
−0.079
−0.034
−0.066
0.096
0.043
−0.030
1.149
1.245
1.150
1.593
1.232
1.195
−0.063
−0.010
−0.058
0.068
0.041
−0.038
−0.039
−0.026
−0.068
0.097
0.034
−0.023
1.272
1.198
1.239
1.157
1.597
1.146
−0.200**
−0.019
0.158*
−0.029
0.235**
0.273
0.097**
2.725**
1.215
1.145
1.220
1.237
1.250
−0.186*
−0.011
0.156*
−0.029
0.234**
0.263
0.094**
2.590**
1.236
1.271
1.229
1.156
1.217
Step 1
Step 2
Step 3
VIF
−0.167*
0.114
0.145*
−0.135+
0.162*
−0.040
−0.217
−0.232
−0.127
−0.026
−0.159
−0.168*
0.128+
0.164*
−0.136
0.178*
−0.066
−0.225
−0.227
−0.123
−0.031
−0.136
−0.153*
0.107
0.197**
−0.100
0.158*
−0.081
−0.234
−0.254+
−0.122
−0.058+
−0.189+
−0.103
−0.023
−0.051
0.068
0.048
−0.044
0.153
2.818**
0.175
0.022
2.065**
Model 2: Entropy index
a
0.169
0.016
1.976*
N = 183 MPI project teams.
+
p < 0.1; *p < 0.05; **p < 0.01;
a
VIF (Variance Inflation Factor).
Figure 1. Interaction of functional variety and technology maturity on MPI project technical performance.
International Journal of Production Research
4925
Figure 2. Interaction of location tenure variety and technology maturity on MPI project technical performance.
Figure 3. Interaction of experience level disparity and technology maturity on MPI project technical performance.
technology. This finding is consistent with previous studies that extol the knowledge-related virtues of cross-functional
integration for tasks under uncertainty (e.g. Pinto, Pinto, and Prescott 1993; Pelled 1996; Ketokivi and Schroeder 2004).
Interestingly, our results further show a clear trade-off of social effects and informational effects of functional variety:
when a mature technology is implemented, functional variety can actually become detrimental. With the help of prior
knowledge provided by pre-adopters to conduct the MPI project, there might be little benefit of information diversity to
offset the potentially high cost of relational conflicts from separate ‘thought worlds’ and interpretative differences among
team members who have different functional backgrounds (Dahlin, Weingart, and Hinds 2005; Huang and Wang 2013).
In this case, it might be better to utilise specialised expertise of a functionally homogenous team with more clarified
and focused priorities for the projects (Ancona and Caldwell 1992).
Location tenure variety, to our knowledge, has not been examined in the team diversity literature. The supported
hypothesis (H3b) suggests that, in MPI projects with immature technologies, the information benefits of mixing new
and incumbent team members are relatively weaker than detrimental social categorisation effects. Compared to functional variety, location tenure variety might engender more severe separation effects, since team members do not share
site-specific norms or cultures. However, location tenure variety seems to be valuable when implementing mature
4926
J.Y. Lee et al.
technologies. These results support what Easton and Rosenzweig (2012) assert in their study of six sigma project
success: the likelihood of the success of a ‘best practice’ initiative (a mature technology) increases with team members
who have diverse organisational experience or information. In the case of a mature technology implementation projects
newcomers might have relevant knowledge or experience with the technology. Recognising this relevant knowledge,
incumbent workers are likely to welcome and engage newcomers, thus overcoming social barriers and improving problem solving in MPI projects.
We also found that technology maturity changes the influence of experience level disparity on technical performance
of MPI projects. Novice team members may feel less confident in challenging opinions of experts, thereby suppressing
the informational benefit of this type of disparity and impeding the project performance. This suppression of voice may
be most damaging in immature technology implementations, where the additional insights of novices are likely to be of
most value. Conversely, in the implementation of mature technologies the skewed distribution of decision-making power
among novices and more experienced team members may serve to suppress distractions, providing focused attention to
factors already known to be critical. Future research should investigate the importance of technology maturity as another
factor which forms psychological conditions of a low-status team member’s engagement and disengagement in project
environments (Kahn 1990).
Contrary to our expectation, technology maturity did not moderate the relationship between full-time/part-time team
member variety and technical performance of MPI projects. After accounting for other diversity elements, remaining
informational benefits and relational conflicts that can be explained by the mix of full-time and part-time team members
might be marginal. For example, the existence of effective cross-project knowledge sharing systems of organisations
might replace the value of part-time team members as new sources of information and conduits of knowledge. Moreover, given the prevalence of matrix project structures in most organisations, many project workers are likely to have
gained ample experience in managing full-time and part-time relationships. As a consequence, associated social stratification or categorisation effects might be negligible. The validity of these interpretations can be tested by future research.
Technology maturity also did not moderate the relationship between education level disparity and technical performance of MPI projects. Instead, we note that the more graduate degree holders a team has, the higher MPI technical
performance (see Table 5). These result supports the argument of Bell et al. (2011): having many of highly educated
team members is better in augmenting knowledge within a team and enhancing team performance, rather than having
diverse levels of education. Instead, education level disparity seems to be more significant in other contexts. For example, researchers investigating more than 1500 teams in manufacturing, sales, marketing, distribution, and other administrative contexts demonstrated that teams with education level diversity received higher performance bonuses when their
tasks required better understanding of, and communications with, broader ranges of customers (Jehn and Bezrukova
2004). Education level disparity may be less important in MPI project teams, which tend to be internally focused. Future
research also needs to study this conjecture.
5.2 Managerial implications
Process innovation project teams tend to be created and disbanded more frequently than product innovation teams
(Anand, Ward, and Tatikonda 2010). Consequently, team members in MPI projects often lack intimacy and shared
responsibilities that can be helpful in achieving their technical goals. It is therefore important for managers to design
MPI project teams to overcome the challenges. Our findings suggest that managers should evaluate levels of maturity of
technologies embraced by projects, and carefully design the team compositions in order to capitalise upon various diversity elements as valuable assets.
When firms deliberately adopt well-established, mature, best practices, an ideal project team would be diverse in
location tenure and experience level, but not diverse in functional background. Such a team could presumably take
advantage of information variety and knowledge spillover without losing team identity or focus due to separation or disparity. If a functionally diverse team is already in place, managers should prioritise project goals and clearly specify
focused technical improvement targets so that team members do not become bogged down in unnecessary arguments
about the project direction and alternative solution paths that favour their department-specific interests or knowledge.
Conversely, when a MPI project seeks to apply an unknown and immature technology, managers should create a
cross-functional project team that provides a wide range of perspectives and skills. Such variety should improve the
overall understanding of the technology and the team’s creativity, which lead to better problem solving in the implementation. At the same time, managers should consider the possible negative impacts of location tenure variety and experience level disparity on the projects. As it is unlikely that the team can be composed entirely of highly experienced and
International Journal of Production Research
4927
incumbent team members, managers should invest in building proper team cultures to mitigate the status-related negative social effects that suppress the inputs of novices or newcomers.
5.3 Limitations and future research directions
The limitations of our study present opportunities for improvement and extension in the future. In choosing a survey
methodology, we traded the benefits of first-hand observation for the benefits of a large scale sample. As such, we relied
on a single manager’s retrospective descriptions of each team, rather than directly assessing each team’s variety characteristics. Future studies could directly observe team characteristics and behaviours by analysing individual team
member-level data and their communication patterns. That way, they could develop a richer understanding of the effects
that we have uncovered in this research. Such studies might also reveal strategies that managers either intentionally or
intuitively use to mitigate the potentially negative effects of functional and experience level variety.
Our data provide no support for the hypothesis regarding education level variety. We suspect that it is because we
studied MPI projects, which are typically dominated by technical issues and are not concerned with wide socioeconomic differences in user populations. Future research should investigate whether technology maturity moderates the
relationship between education level variety in other innovative contexts, such as new product development and marketing communications, where innovations must address wider audiences.
Finally, we focused on the variety aspect of diversity, with the main assumption that sources of information variety
enhance the performance of MPI projects. As Harrison and Klein (2007) assert, however, variety, separation, and disparity are likely to coexist within a team. Future research such as Narayanan, Swaminathan, and Talluri (2014) could more
intensely investigate possible interactions among those factors. Researchers have developed operationalisations of separation and disparity, including age (Kearney, Gebert, and Voelpel 2009), locus-of-control (Boone and Hendriks 2009),
gender and race (Pelled, Eisenhardt, and Xin 1999), value (Jehn, Northcraft, and Neale 1999) and so on. Importantly,
neither the entropy index nor Blau’s index (for variety) should be used to measure separation and disparity. For further
insights for proper measurements of diversity variables, please see Harrison and Klein (2007).
Disclosure statement
No potential conflict of interest was reported by the authors.
Note
1.
The correlations between scores of Blau’s index and the Entropy index for variety elements were above 0.9 (p < 0.01). For brevity, we report descriptive statistics and correlations only for Blau’s index.
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