Journal of Computer Information Systems
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/ucis20
Big Data, Marketing Analytics, and Firm Marketing
Capabilities
Guangming Cao , Na Tian & Charles Blankson
To cite this article: Guangming Cao , Na Tian & Charles Blankson (2021): Big Data, Marketing
Analytics, and Firm Marketing Capabilities, Journal of Computer Information Systems, DOI:
10.1080/08874417.2020.1842270
To link to this article: https://doi.org/10.1080/08874417.2020.1842270
Published online: 05 Feb 2021.
Submit your article to this journal
Article views: 246
View related articles
View Crossmark data
Full Terms & Conditions of access and use can be found at
https://www.tandfonline.com/action/journalInformation?journalCode=ucis20
JOURNAL OF COMPUTER INFORMATION SYSTEMS
https://doi.org/10.1080/08874417.2020.1842270
Big Data, Marketing Analytics, and Firm Marketing Capabilities
Guangming Caoa, Na Tianb, and Charles Blanksonc
a
Ajman University, Ajman, UAE; bNorthwestern Polytechnical University, Xi’an, China; cUniversity of North Texas, Denton, TX, USA
ABSTRACT
KEYWORDS
While big data, marketing analytics, and firm marketing capabilities are all potential drivers of compe­
titive advantage, there is limited research that investigates the interrelationship between them. This
study aims to address this gap by examining the mechanisms through which big data and marketing
analytics can be used to enhance firm marketing capabilities. Drawing on the dynamic capability view,
a research model is developed and tested based on an analysis of 316 survey responses. The findings
demonstrate positive effects of the use of big data on the use of marketing analytics, and the latter’s
effect on firm marketing planning, marketing implementation, brand management, customer relation­
ship management, and product development management. This study helps advance our understand­
ing of firm marketing capability-enhancing processes through the use of big data and marketing
analytics. It also provides practical implications to guide firms in using big data and marketing analytics
to improve their marketing capabilities.
Big data; marketing
analytics; dynamic
capability; firm marketing
capabilities
1. Introduction
The dynamic capability view, defined as “the capacity of an
organization to purposefully create, extend, and modify its
resource base”,1 [p. 94], suggests that in order for a firm to
gain sustained competitive advantage in a rapidly changing
environment, the firm should be capable of sensing opportu­
nities, seizing opportunities by mobilizing resources, and
transforming the firm through continuous renewal.2–4
Specifically, dynamic marketing capabilities are aimed at
developing, releasing, and integrating market knowledge and
resources to match and create market and technological
change.5–7 Dynamic marketing capabilities become key8
because they reflect the firm’s ability to engage in marketbased learning and further use the resulting insights to
achieve market effectiveness6 and sustained competitive
advantage.9,10 Research suggests that advance in modern
information technologies (ITs) is one of the most important
factors to enable dynamic marketing capabilities.7,11 In parti­
cular, research suggests that firms can develop dynamic mar­
keting capabilities through utilizing big data/marketing
analytics [e.g.9,12,13]
Big data commonly refers to datasets that are very high in
velocity, volume, and variety.14–16 Research suggests that big
data offers remarkable opportunities for firms across indus­
trial sectors [e.g.17,1819,2021] to gain useful insights into custo­
mers and operations, thereby improving their marketing,22
decision-making,23 new product development,24,25 among
other areas. Expressly, big data becomes a significant disrup­
tor in online and offline marketing approaches,26 deemed “the
new oil”,[2728 p.1]. In contrast, marketing analytics pertains to
the collection, management, and analysis of big data to extract
useful insights to support marketing decision-making.29–32
CONTACT Guangming Cao
g.cao@ajman.ac.ae
Some empirical studies [e.g.9,29,33,34] suggest that firms could
use marketing analytics to significantly improve, inter alia,
marketing decision-making, marketing effectiveness, new pro­
duct
development,
organizational
performance.
However, despite evidence that big data, marketing analytics,
and firm marketing capabilities are all potential drivers of superior
performance, a few significant gaps remain. First, although the
extant literature has examined the performance impacts of big
data, many firms investing in big data often fail to attain the
expected advantages.22,35 There is limited research on how firms
transform the potentials of big data into actual firm performance in
the competitive marketing environment.13,36 Second, while analytics
arguably depends on the availability of big data, big data has rarely
been included as a construct by existing analytics studies [e.g.­
9,29,34,37
] Thus, the relationship between big data and analytics is
underdeveloped. Third, although the literature suggests that mar­
keting capabilities are important drivers of firm competitiveness
[e.g.,6,11,38,39] very little is known about how firms improve their
marketing capabilities [e.g.31,40–42] In particular, it was not until
recently that dynamic marketing capabilities and their performance
effects were being studied [e.g.5–7]; thus, scant research has exam­
ined how to build dynamic marketing capabilities.8,39 Finally,
although analytics research [e.g.43–46] has shown that some firms
can use business/marketing analytics to improve their dynamic
capabilities2–4 and eventually firm competitiveness [e.g.,9,29,31]
many firms are yet to obtain value from their analytics
investment.47 In spite of the growing body of analytics studies [e.­
g.,9,29,33,34
] scholars still struggle to theorize the value realization of
big data analytics.33,36 Thus, little research has systematically linked
big data and marketing analytics to organizational capabilities33,44
and/or dynamic marketing capabilities.9
Therefore, this study aims to examine one key research
question: what are the mechanisms through which big data
College of Business Administration, Ajman University, Ajman, UAE.
© 2021 International Association for Computer Information Systems
2
G. CAO ET AL.
and marketing analytics can be used to enhance firm market­
ing capabilities? To answer this question, this study develops
a research model to conceptually and empirically examine the
link from the use of big data and marketing analytics to firm
marketing capabilities, drawing on the dynamic capability
view2–4 and literature on marketing capabilities [e.g.,38,48,49]
dynamic marketing capabilities,5–7 and analytics [e.g.9,43–45]
Specifically, this study demonstrates that a firm can use big
data and marketing analytics to develop its market-sensing
capability [e.g.48,5051] to uncover meaningful marketing
knowledge and insights, which in turn enable the firm to
enhance its dynamic marketing capabilities by developing
and integrating market knowledge and resources,5–7 thereby
seizing market opportunities.2–4
The remainder of this paper is structured as follows. The
next section explicates the study’s theoretical background,
followed by hypotheses development. Then, the research
methodology is discussed, including research design, sampling
process, operationalization of constructs, and fieldwork, fol­
lowed by the data analysis and presentation of results. Finally,
theoretical and managerial implications, study limitations and
directions for future research are provided.
2. Theoretical background
2.1 Big data and marketing analytics
Big data often refers to datasets exhibiting key characteristics
such as high volume, high variety, and high velocity.14–16
Although it is well recognized that big data could offer remark­
able opportunities for firms to gain useful insights to signifi­
cantly change for example online and offline marketing
approaches26 and to improve business operations, processes,
and firm performance [e.g.,22–24] many firms investing in big
data often fail to attain the expected advantages,2235: “Most firms
are still stumbling around in the dark as they seek to fully
understand the function and capabilities of big data”[52, p.1].
On the other hand, limited academic research exists to examine
how to use big data to improve organizational decisionmaking53 and to sense and respond quickly to opportunities
for innovation.13 In spite of big data applications becoming
pervasive in marketing research, researchers still struggle to
explain how firms realize value from big data.14,36
Marketing analytics (descriptive, predictive, and prescriptive) is
a subdomain of business analytics54 or big data analytics47 that
supports marketing decision-making.29–31 While a number of
empirical studies [e.g.9,29,33,34] suggest that firms could use market­
ing analytics to significantly improve marketing processes/opera­
tions, marketing effectiveness, and firm performance, scholars still
struggle to theorize the value realization of big data analytics [e.­
g.13,33,36,44] For example, while,36 based on a literature review of 67
papers focusing on data analytics, calls for further empirical studies
to carefully examine how firms could actually realize value from big
data analytics,44 suggests that extant analytics studies lack under­
standing of the mechanisms through which big data analytics may
lead to improved firm performance. Additionally, although a few
studies suggest that firms create value from a whole “big data chain”
consisting of big data and analytics [e.g.,22,53,55,56] extant analytics
studies have rarely included big data as a key construct [e.g.9,29,34,37]
Thus, the relationship between big data and analytics is under­
developed in the literature.
2.2 Dynamic capabilities and marketing capabilities
Organizational capabilities refer to “complex bundles of skills
and accumulated knowledge, exercised through organizational
processes, that enable firms to coordinate activities and make
use of their assets” [48, p. 38], which could be categorized into
dynamic and operational capabilities.6,44,57 Operational capabil­
ities allow a firm to perform basic functional activities58 focus­
ing on sustaining the status quo59 to make its living in the short
term.44 In contrast, dynamic capabilities explain how firms
attain and sustain competitive advantage in environments of
rapid technological change.60 They are path dependent4 and
future-oriented6162 capabilities.
Operational marketing capabilities include advertising,
product development, channel management, marketing com­
munication, selling, marketing information management,
marketing planning, and marketing implementation.63,64
Compared with these static marketing capabilities, dynamic
marketing capabilities, a subset of dynamic capabilities,5
emphasize a firm’s cross-functional process-changing capabil­
ity to respond to market changes10,65 by developing, releasing,
and integrating market knowledge and resources,5–7 thereby
achieving market effectiveness6 and sustained competitive
advantage.9,10
Research indicates that when a firm emphasizes genera­
tion, dissemination, and responsiveness to market intelli­
gence, the firm will be able to better align its marketing
resources to respond to fast-changing markets,49,5766 because
the firm’s static marketing capabilities become dynamic mar­
keting capabilities49,57, manifested in marketing decisionmaking, product development management, supply chain
management, brand management, and customer relationship
management (CRM) [e.g.8,9,39]
However, despite that marketing capabilities are considered
important drivers of firm competitiveness [e.g.,6,11,38,39] enhan­
cing marketing capabilities is difficult67 and the ways in which
firms improve their marketing capabilities remain underex­
plored [e.g.31,40–42] For example,38 suggests that “very little is
known about how firms improve their marketing capabilities”
(p.736); and40 emphasizes the need to “explain the mechanisms
leading to the creation and management of marketing capabil­
ities” (p. 369). In particular, while dynamic marketing capabil­
ities and their performance effects were studied recently
[e.g.,5–7] scant research has examined how to build dynamic
marketing capabilities.8,39 Furthermore, although analytics
research [e.g.43–46] has shown that business/marketing analy­
tics can be used to improve firm dynamic capabilities,2–4 scho­
lars still struggle to theorize the value realization of big data
analytics.11,33,36 Thus, there is a gap in the literature for a study
to understand the under-researched link between big data,
marketing analytics, organizational capabilities33,44 and/or
dynamic marketing capabilities.9
JOURNAL OF COMPUTER INFORMATION SYSTEMS
3. Hypotheses development
Based on the above discussion, six testable hypotheses are repre­
sented in the following conceptual framework (Figure 1). These
five marketing capabilities are included because they are under­
stood to be some of the most important marketing constructs
[e.g.26,68–71] While the relationship between firm marketing
capabilities and competitive advantage has been well established
by prior marketing studies, this study has focused on the underresearched relationships between big data, marketing analytics,
and firm marketing capabilities.
3.1 Linking big data and marketing analytics
Although a few scholars [e.g.53,55] suggest that big data and
analytics are different, the effect of big data on organizational
decision-making has received little attention in the
literature53,72 in that big data has rarely been examined as
a construct. In particular, scholars [e.g.53,55] suggest that in
order to extract meaningful insights from big data, firms need
to develop two types of processes or capabilities: data manage­
ment and analytics, since “Big data without analytics is just
a massive amount of data. At the same time, analytics without
big data are simply mathematical and statistical tools and
applications”[55, p. 28]. In reality, while some firms find it is
challenging to unlock the benefit of their data because they
have analyzed less than 0.5% of all collected data,17 others are
facing a different problem: their analytics initiatives are stifled
because they lack relevant data.73
Thus, it is conceivable to postulate that a firm’s use of big
data enables it to be able to use marketing analytics more
effectively to uncover meaningful knowledge and insights
from the data. When big data and marketing analytics work
together, it is possible to model, analyze, and interpret big
data.74 This is consistent with the idea that the joint use of
assets or combining resources in a firm is value enhancing,2
synergistic,7576 and advantageous [e.g.77] Therefore, this study
hypothesizes that:
3
H1: The use of big data has a positive effect on the use of
marketing analytics.
3.2 Linking the use of marketing analytics and firm
marketing capabilities
Analytics literature suggests that a firm’s use of marketing
analytics is the manifestation of its market sensing
capability,9,45 referring to “analytical systems (and individual
capacities) to learn and to sense, filter, shape and calibrate
opportunities” [2, p. 1326], which in turn enables the firm to
develop its seizing and transforming capabilities [e.g.44,45]– as
manifested by the firm’s marketing capabilities.9
First, the use of marketing analytics may significantly
enhance a firm’s ability to “extract previously unknown,
potentially useful, and interesting knowledge” [78, p. 363], to
glean intelligence79 on customer lifecycle-encompassing
acquisition, retention, and expansion,73 and/or “competitors’
key-product features, pricing strategies, and customer feed­
back” [34, p. 1563]. Essentially, learning about customers,
competitors, and the broader market environment using mar­
keting analytics helps the firm to better sense market threats
and opportunities [e.g.9,44,45]
Second, while knowledge is critical for any dynamic
capabilities,76,80 the insights and knowledge derived from
the firm’s market sensing capability, as manifested by its
use of marketing analytics, are expected to provide the
knowledge foundation for the firm to further enhance its
other marketing capabilities.38,41,57 For example, research
suggests that a firm using marketing analytics to develop its
sensing capability is able to make better strategic marketing
decisions9,30 and to assist in implementing marketing
strategies78 since sensing relates directly to the strategic con­
cept of diagnosis.45 This means that a firm with such
a market-sensing capability is highly likely to further enhance
its marketing planning, its ability to conceive marketing
strategies that optimize the match between the firm’s
resources and the expectations of its customers,64 and mar­
keting implementation, the processes by which intended
Figure 1. A conceptual framework showing the relationship between the use of big data, the use of marketing analytics and firm marketing capabilities.
4
G. CAO ET AL.
marketing strategy is transformed into realized resource
deployments.64
Product development could also be more successful when
big data is leveraged to gain customer insights to transform
new product development in dynamic marketplaces.13,24
Similarly, firms gaining insights from its market sensing cap­
ability are more likely to have new product success.34,41
Additionally,78,81 suggest that a firm gaining higher levels of
marketing knowledge from data analytics will be more likely
to maximize the benefits of CRM, the firm’s ability to build
relationships with potential customers and ability to leverage
the established relationship with customers thereby acquiring
new customers and retaining existing customers.38,47,64 This is
because the firm’s strong market-sensing capability allows it
to better understand and/or accurately forecast changes in
customer needs and requirements, thereby developing longterm customer relationships,4182 or amending the frequent
internal focus of CRM implementation and its negative
impact on the revenue outcomes of CRM investments.80
While evidence in marketing research suggests that a firm’s
market sensing capability could inform its branding41 and
thereby attract new customers,80,83 one can further argue
that such firms could improve their brand management,
with the view of attracting new customers with valued
products8485 while striving to maintain attractive value pro­
positions relative to competing offerings.38,64 There is also
evidence that top-performing firms use business analytics to
manage brand.86
Furthermore,80 suggests that a firm’s market-sensing cap­
ability provides valuable insights to allow the firm to allocate
resources, such as better targeting of the resources deployed
and the firm’s media spending, in serving attractive pro­
spects and existing customers or building the firm’s brands,
which can be seen as seizing market opportunities by trans­
forming its customer relationship and brand management.
The research indicates that in order for a firm to better fit
market needs and to seize emerging marketing opportu­
nities, the firm needs to transform or reshape its marketing
approaches,44,87,88 such as transforming existing modes of
operation45 as well as adapting products and services to suit
customer needs44; prioritizing target customers,89 allocating
resources to accommodate customer needs,90 and translating
strategic key performance indictors into operational metrics
to inform decision-making.91 Therefore, it is plausible that
a firm’s marketing capabilities developed based on its market
sensing capability as manifested by the use of marketing
analytics can demonstrate the firm’s seizing and transform­
ing capabilities (see.9)
Hence, drawing on the dynamic capability view, and the
marketing and analytics literature, the following hypotheses
are put forward:
H2: The use of marketing analytics has a positive effect on
marketing planning capability.
H3: The use of marketing analytics has a positive effect on
marketing implementation capability.
H4: The use of marketing analytics has a positive effect on
brand management capability.
H5: The use of marketing analytics has a positive effect on
customer relationship management.
H6: The use of marketing analytics has a positive effect on
product development management.
4. Research methodology
4.1 Measures
The constructs listed in Table 1 were measured using scales
adapted from items that were validated across a variety of
relevant studies. The use of big data was measured using items
from.24 The use of marketing analytics was measured using
indicators from.9 Firm marketing capabilities–including mar­
keting planning, marketing implementation, brand manage­
ment, customer relationship management, and product
development management–were measured using scales
adapted from prior studies.38,49,64
Additionally, following prior studies [e.g.,38,92] firm size,
industry type, as well as respondent job title and tenure, may
have a possible effect on the relationships examined in this
study and were thus included as control variables.
4.2. Sample and data collection
Primary data were collected from Chinese firms to verify the
research model using a survey approach. The questionnaire
was developed using the back-translation process,93 which
was repeated three times until the originator of the questions
was satisfied that the Chinese version was representative of
the original source. Then, the questionnaire was pilot-tested,
leading to a number of formatting and presentation modifica­
tions. Table 1 shows the questions used in the survey to
measure the research constructs.
The survey was conducted by a Chinese market research
firm for a fee as the firm has a database with more than
2.6 million Chinese firms and a professional reputation for
its survey quality control. A questionnaire was distributed by
e-mail to 11,562 Chinese firms. Within two weeks, 337
responses were received, of which 316 were usable responses.
However, the market research firm’s software for distributing
the survey had no means to know how many survey invita­
tions were actually delivered and opened. As a result, it was
not possible to calculate a meaningful response rate.
As there is no agreed method for conducting surveys with
mass e-mails yet, this study thus considered the number of
responses from the perspective of building an adequate
model.94 In the structural model, the maximum number of
arrows pointing at a construct is five. In order to detect
a minimum R2 value of 0.10 in any of the constructs at
a significance level of 1%, the minimum sample size required
JOURNAL OF COMPUTER INFORMATION SYSTEMS
5
Table 1. Constructs and indicators of the study.
Constructs
Use of Big Data
(UBD)
(Higher-order)
(Reflective)
24
Use of Marketing Analytics (UMA)*
(Higher-order)
(Formative)
9
Marketing Planning Capability (MPC)
(Reflective)
49
Marketing Implementation Capabilities (MIC)
(reflective)
49
Brand Management Capability (BMC)
(Reflective)
38
Customer Relationship Management (CRM)
(Reflective)
38
Product development management (PDM)
(Reflective)
64
Indicators (based on Likert scale from 1- strongly disagree to 7-strongly agree)
Please indicate your agreement or disagreement on the following statements
Volume (lower-order, reflective)
VOL1-My company analyses large amounts of data
VOL2-The quantity of data we explore is substantial
VOL3-We use a great deal of data
VOL4-We scrutinize copious volumes of data
Variety (lower-order, reflective)
VAR1-We use several different sources of data to gain insights
VAR2-My company analyses many types of data
VAR3-We have many databases from which we can run data
VAR4-We examine data from a multitude of sources
Velocity (lower-order, reflective)
VEL1-We analyze data as soon as we receive it
VEL2-The time period between us getting and analyzing data is short
VEL3-My company is lightning fast in exploring our data
VEL4-My company analyses data speedily
To what extent has your company implemented marketing analytics in each of the following
areas?
Customer-related (CMA) (lower-order, formative)
UMA1-Customer insight
UMA2-Customer acquisition
UMA3-Customer retention
UMA4-Segmentation
Product-related (PMA) (lower-order, formative)
UMA5-New product or service development
UMA6-Product or service strategy
UMA7-Promotion strategy
UMA8-Pricing strategy
UMA9-Marketing mix
UMA10-Branding
General marketing-related (GMA) (lower-order, formative)
UMA11-Digital marketing
UMA12-Social media
UMA13-Multichannel marketing
How does your company perform the following activities relative to your key competitors?
MPC1-Marketing planning skills
MPC2-Ability to effectively segment and target market#
MPC3-Marketing management skills and processes
MPC4-Thoroughness of marketing planning processes
How does your company perform the following activities relative to your key competitors?
MIC1-Allocating marketing resources effectively
MIC2-Organizing to deliver marketing programs effectively
MIC3-Translating marketing strategies into action
MIC4-Executing marketing strategies quickly#
How does your company perform the following activities relative to your key competitors?
BMC1-Routinely use customer insight to identify valuable brand positioning#
BMC2-Consistently establish desired brand associations in consumers’ minds
BMC3-Maintain a positive brand image relative to competitors
BMC4-Achieve high levels of brand awareness in the market on a regular basis
BMC5-Systematically leverage customer-based brand equity into preferential channel
positions#
How does your company perform the following activities relative to your key competitors?
CRM1-Routinely establish a “dialogue” with target customers
CRM2-Get target customers to try our products/services on a consistent basis
CRM3-Focus on meeting customers’ long term needs to ensure repeat business#
CRM4-Systematically maintain loyalty among attractive customers
CRM5-Routinely enhance the quality of relationships with attractive customers#
How does your company perform the following activities relative to your key competitors?
PDM1-We have the ability to develop new products/services
PDM2-We are able to commercialize ideas fast
PDM3-We have a number of product/service innovations
PDM4-We are able to successfully launch new products/services
PDM5-We are able to achieve productivity gains from R&D investments#
Mean
SD
5.6
5.4
5.7
5.6
1.04
1.25
1.15
1.18
5.5
5.5
5.3
5.5
1.13
1.24
1.40
1.29
5.2
4.9
5.0
5.1
1.35
1.50
1.43
1.34
4.8
5.2
5.5
5.1
1.1
31.1
31.1
71.27
5.2
5.3
5.3
5.0
4.6
5.2
1.2
01.2
11.3
21.2
71.6
21.26
5.3
4.9
4.6
1.2
31.4
61.62
5.4
5.3
5.5
5.1
1.0
31.1
91.1
31.15
5.41
5.31
5.45
5.10
1.0
31.1
91.1
31.15
5.2
5.5
5.7
5.3
5.4
1.1
11.2
11.2
3.2
31.17
5.4
5.5
5.8
5.5
5.5
1.1
71.1
31.1
31.2
01.17
5.2
5.1
5.3
5.3
5.4
1.1
11.3
11.3
11.1
31.30
*-measured based on a seven-point Likert scale ranging from no use, very low use, low use, moderate use, somewhat heavy use, quite heavy use, to very heavy use; #
– dropped after the measurement evaluation.
is 205.9596 Since 316 usable responses were received, this
minimum sample size requirement was met.
4.3. Respondents
Table 2 summarizes the company profile in terms of the
industry, number of employees, and the province in which
the firms were based (out of 34 Chinese provincial-level
Table 2. Company profiles (n = 316).
Industry
Home appliance
Building materials
Clothing/textile
Machinery/equipment
Automobile and accessories
Electronic
Other
Number of
% employees
5.7
<50
14.6
50–249
17.4
250–499
11.1
500–999
17.4 1000–1999
19.6
≥2000
14.2
%
8.5
42.1
23.7
12.4
6.0
7.3
Province
Guangdong
Beijing
Shanghai
Hubei
Henan
Sichuan
Other
%
13%
11%
7%
5%
3%
2%
59%
6
G. CAO ET AL.
administrative units, only the top six provinces with the most
responding firms were listed). Table 3 summarizes the
respondent profile in terms of their organizational positions
and years of experience in the current industry. The reported
positions of the respondents suggest that 85.5% of the respon­
dents were marketing managers while the rest were other
middle and senior managers. Based on their position within
the firm, the respondents were considered to have relevant
knowledge and experience to be able to address the survey
questions.9798
4.4. Common method and non-respondent bias
Both procedural and statistical remedies were used to control
for common method bias. The procedural remedies were used
to improve scale items through defining them clearly and
keeping the questions simple and specific, labeling every
point on the response scale to reduce item ambiguity,99 and
using positively and negatively worded measures to control
for acquiescence and disacquiescence biases.100 The first sta­
tistical approach conducted was to check the correlation
matrix (Table 4) to identify if there were any highly correlated
factors (r >.90) from common method bias.98 The result
indicated that this study was unlikely to suffer from common
method bias. Finally, the partialling out of general factor
suggested by101 was conducted and the result indicated that
common method bias was not a threat in the study.
To evaluate the presence of non-respondent bias, a t-test
and the known value for the population approach102 were
conducted. The results suggested an absence of nonresponse bias102 and significant differences between respon­
dents and non-respondents, respectively.
4.5. Evaluation of the measurement model
As both formative and reflective constructs were used,
a separate set of analyses was used to evaluate the measure­
ment model following the recommendations by.95 The reflec­
tive measurement model was evaluated by considering
Table 3. Respondent profiles (n = 316).
Respondent Positions
%
CEO/President/MD/Partner 1.6
Vice President/Director
0.7
Other C-level Executive
11.5
Chief Marketing Officer
15.9
Director/Head of
69.6
Marketing
Other directors
0.7
Years of Experience (x) in the
industry
x≤5
5 < x ≤ 10
10 < x ≤ 15
15 < x ≤ 20
20 < x ≤ 25
%
8.5
58.5
23.7
7.6
1
x > 25
0.7
Table 4. Descriptive statistics, correlations, and AVE.
Construct Mean S.D.
1
2
3
4
5
6
7
1 BRC
5.50 0.93 0.76
2 CRM
5.47 0.86 0.57** 0.74
3 MIC
5.42 0.93 0.58** 0.62** 0.78
4 MPC
5.33 0.84 0.55** 0.58** 0.62** 0.76
5 PDM
5.22 0.90 0.57** 0.52** 0.53** 0.55** 0.75
6 UBD
5.41 0.76 0.44** 0.44** 0.49** 0.47** 0.46** 0.83
7 UMA
5.17 0.67 0.39** 0.40** 0.43** 0.41** 0.43** 0.50** #
**p < 0.01, #-formative
internal consistency (composite reliability), indicator reliabil­
ity, convergent validity and discriminant validity (Table 4);
they were satisfactory. The formative measurement model was
evaluated by assessing multicollinearity, the indicator weights,
significance of the weights, and the indicator loadings.95 The
evaluation results indicated all were satisfactory.
4.6. Hypothesis testing
In order to test the hypotheses, SmartPLS3 was used, includ­
ing a two-stage approach and a bootstrapping procedure
(5,000 samples), as suggested by.95 The result is summarized
in Figure 2.
All hypotheses are supported. H1 proposes that use of big
data (UBD) positively relates to use of marketing analytics
(UMA), which is supported as UBD’s effect on UMA is 0.50
(p < .001). H2 assumes that UMA is positively related to
marketing planning capability (MPC), marketing implemen­
tation capability (MIC), brand management capability (BMC),
customer relationship management (CRM), and production
management (PDM), which is confirmed by UMA’s effects of
0.39 (p < .001) on MPC, 0.39 (p < .001) on MIC, 0.36
(p < .001) on BMC, 0.36 (p < .001) on CRM, and 0.4
(p < .001) on PDM, respectively.
The results also indicate that industry type has
a statistically significant effect on all firm marketing capabil­
ities except for marketing planning; job tenure has
a statistically significant effect on product development man­
agement only; and both firm size and job title have no statis­
tically significant effect on the marketing capabilities.
5. Discussion and implications
This study drew on the dynamic capability view to examine
how marketing capabilities can be enhanced through devel­
oping and testing a research framework linking the use of big
data, marketing analytics and firm marketing capabilities.
First, the findings provide valuable theoretical understand­
ing and empirical evidence of how firm marketing capabilities
can be enhanced by the use of big data and marketing analy­
tics. This fills an important gap between the link–and the need
for a tighter connection–between big data, marketing analy­
tics, and marketing capabilities. In fact, the capabilityenhancing mechanisms are rather different from the known
and fragmented approaches to studying marketing capabil­
ities, the use of big data and marketing analytics demonstrated
by prior studies. Integrating and broadening the applicability
of the relationships between big data, marketing analytics, and
firm marketing capabilities in this research demonstrate gen­
eralizability of previous findings from analytics and marketing
studies, and indicates that firms should be able to improve
their firm marketing capabilities through the use of big data
and marketing analytics.
Second, the study’s outcomes suggest that the use of big data
significantly and positively affects the use of marketing analy­
tics. While prior research suggests that an enhanced relation­
ship between two resources in a firm103 is value enhancing,2,76
this study’s analysis provides empirical evidence in support of
this view and the idea that both big data and analytics are parts
JOURNAL OF COMPUTER INFORMATION SYSTEMS
7
Figure 2. Hypothesis test results.
of a whole ‘big data chain’ [e.g.53,55,56] By empirically and
conceptually demonstrating the value of and the need for
using big data to enhance marketing analytics, this study con­
tributes to analytics literature by challenging the way in which
existing analytics studies examine the effects of big data and
analytics separately, thereby directing the attention to how the
use of big data and analytics together could create firm value.
Third, the findings show that the use of marketing
analytics relates to firm marketing capabilities significantly
and positively. This is consistent with the marketing litera­
ture previously discussed in that firm marketing capabilities
are built upon marketing knowledge [e.g.5,6,38,57] The posi­
tive relationship between the use of marketing analytics and
firm marketing capabilities demonstrated in this study
implies that the firms in this study can use the marketing
knowledge and insights uncovered from big data and mar­
keting analytics to enhance its firm marketing capabilities,
which are the manifestations of seizing and transforming
capabilities9 or dynamic marketing capabilities.8,39 The
results contribute to the marketing literature and practice
by empirically demonstrating how firms could use market­
ing analytics to develop their marketing capabilities, which
has
so
far
remained
largely
under-researched
[e.g.9,31,33,40,42,44]
The findings from this study also have interesting implica­
tions for managers. Firms interested in investing in big data
and marketing analytics should use them together to max­
imize their potential business value. Firms wishing to develop
their marketing capabilities should utilize the knowledge and
insights gained from big data and marketing analytics as
a foundation. While this relationship was not hypothesized
in this study, it was seen to be supported indirectly as the
firms in this study that used marketing analytics saw signifi­
cant improvements in their firm marketing capabilities.
Although it was beyond the remit of this study to examine
the effect of the model on competitive advantage, taken
together, higher levels of firm marketing capabilities would
enhance competitive advantage.64
6. Limitations and directions for future research
This study has several limitations. First, the study’s outcomes were
based on data collected from a survey. Future research could
complement this study’s findings by utilizing longitudinal and
time-series research designs that will provide additional causal
evidence. Employing a qualitative approach to develop more indepth insights and knowledge on how big data and marketing
analytics create firm value is important for future research.
Second, the survey questionnaire was distributed to a single
key informant by a market research firm using mass e-mails,
without providing a meaningful response rate, which raises
concerns regarding non-respondent bias. Although there was
no evidence of non-respondent bias, the risk of bias could still
not be completely absent. Future research should use multiple
informants to enhance confidence in the findings.
Third, the current research results are based on and limited
to Chinese firms. It would be worthwhile to extend and
replicate this work to firms in other countries. Finally, the
present study focuses on developing an understanding of the
ways in which big data and marketing analytics can be used to
develop firm marketing capabilities thereby attaining compe­
titive advantage. However, since the latter was not examined
in this study, future research could explore the impact of the
model on competitive advantage.
Funding
This work was supported by the Ajman University Research Grant
[CRG- 2019-CBA-03].
References
1. Helfat CE, Peteraf MA. Understanding dynamic capabilities: pro­
gress along a developmental path. Strateg Organ. 2009;7:91–102.
doi:10.1177/1476127008100133.
2. Teece DJ. Explicating dynamic capabilities: the nature and micro­
foundations of (sustainable) enterprise performance. Strat Manage
J. 2007;28:1319–50.
8
G. CAO ET AL.
3. Teece DJ. The foundations of enterprise performance: dynamic
and ordinary capabilities in an (economic) theory of firms. Acad
Manag Perspect. 2014;28(4):328–52. doi:10.5465/amp.2013.0116.
4. Teece DJ, Pisano G, Shuen A. Dynamic capabilities and strategic
management. Strat Manage J. 1997;18(7):509–33. doi:10.1002/
(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z.
5. Bruni DS, Verona G. Dynamic marketing capabilities in sciencebased firms: an exploratory investigation of the pharmaceutical
industry. British J Manage. 2009;20:S101–S117. doi:10.1111/
j.1467-8551.2008.00615.x.
6. Kachouie R, Mavondo F, Sands S. Dynamic marketing capabilities
view on creating market change. Eur J Mark. 2018;52:1007–36.
doi:10.1108/EJM-10-2016-0588.
7. Wang ET, Hu H-F, Hu PJ-H. Examining the role of information
technology in cultivating firms’ dynamic marketing capabilities.
Inf Manag. 2013;50(6):336–43. doi:10.1016/j.im.2013.04.007.
8. Barrales-Molina V, Martínez-López FJ, Gázquez-Abad JC.
Dynamic marketing capabilities: toward an integrative
framework. Int J Manag Rev. 2014;16(4):397–416. doi:10.1111/
ijmr.12026.
9. Cao G, Duan Y, El Banna A. A dynamic capability view of
marketing analytics: evidence from UK firms. Ind Mark Manag.
2019;76:72–83. doi:10.1016/j.indmarman.2018.08.002.
10. Guo H, Xu H, Tang C, Liu-Thompkins Y, Guo Z, Dong B.
Comparing the impact of different marketing capabilities: empiri­
cal evidence from B2B firms in China. J Bus Res. 2018;93:79–89.
doi:10.1016/j.jbusres.2018.04.010.
11. Wang Z, Kim HG. Can social media marketing improve customer
relationship capabilities and firm performance? Dynamic capabil­
ity perspective. J Interact Mark. 2017;39:15–26. doi:10.1016/j.
intmar.2017.02.004.
12. Erevelles S, Fukawa N, Swayne L. Big data consumer analytics and
the transformation of marketing. J Bus Res. 2016;69(2):897–904.
doi:10.1016/j.jbusres.2015.07.001.
13. Hajli N, Tajvidi M, Gbadamosi A, Nadeem W. Understanding
market agility for new product success with big data analytics. Ind
Mark Manag. 2020;86:135–43. doi:10.1016/j.indmarman.2019.
09.010.
14. George G, Osinga EC, Lavie D, Scott BA. Big data and data
science methods for management research. Acad Manag J.
2016;59(5):1493–507. doi:10.5465/amj.2016.4005.
15. Goes PB. Big data and IS research. MIS Q. 2014;38:iii–viii.
16. Watson HJ. Tutorial: big data analytics: concepts, technologies,
and applications. Commun Assoc Inf Syst. 2014;34(1):1247–68.
doi:10.17705/1CAIS.03465.
17. Cohen MC. Big data and service operations. Prod Oper Manage.
2018;27:1709–23. doi:10.1111/poms.12832.
18. Li J, Xu L, Tang L, Wang S, Li L. Big data in tourism research:
A literature review. Tour Manag. 2018;68:301–23. doi:10.1016/j.
tourman.2018.03.009.
19. Manyika J, Chui M, Brown B, Bughin J, Dobbs R, Roxburgh C,
Byers AH. Big data: the next frontier for innovation, competition,
and productivity. 2011 [cited 2017 30, April]; Available from:
https://bigdatawg.nist.gov/pdf/MGI_big_data_full_report.pdf.
20. De Luca LM, Herhausen D, Troilo G, & Rossi A. How and when
do big data investments pay off? The role of marketing affor­
dances and service innovation. J Acad Mark Sci. 2020;1–21.
21. Merendino A, Dibb S, Meadows M, Quinn L, Wilson D, Simkin L,
Canhoto A. Big data, big decisions: the impact of big data on
board level decision-making. J Bus Res. 2018;93:67–78.
doi:10.1016/j.jbusres.2018.08.029.
22. Johnson JS, Friend SB, Lee HS. Big data facilitation, utilization,
and monetization: exploring the 3Vs in a new product develop­
ment process. J Prod Innov Manag. 2017;34(5):640–58.
doi:10.1111/jpim.12397.
23. Zhan Y, Tan KH, Li Y, & Tse YK. Unlocking the power of big
data in new product development. Ann Oper Res. 2018;270(1/
2):577–95.
24. Jabbar A, Akhtar P, Dani S. Real-time big data processing for
instantaneous marketing decisions: A problematization approach.
Ind Mark Manag. 2019;90:558–569.
25. Ransbotham S, Kiron D, Prentice PK. Beyond the hype: the hard
work behind analytics success. MIT Sloan Manag Rev.
2016;57:1–18.
26. Richards G, Yeoh W, Chong AYL, Popovič A. Business intelli­
gence effectiveness and corporate performance management: an
empirical analysis. J Comput Inf Syst. 2019;59(2):188–96.
doi:10.1080/08874417.2017.1334244.
27. Germann F, Lilien GL, Rangaswamy A. Performance implications
of deploying marketing analytics. Int J Res Mark. 2013;30
(2):114–28. doi:10.1016/j.ijresmar.2012.10.001.
28. Hanssens DM, Pauwels KH. Demonstrating the value of
marketing. J Mark. 2016;80(6):173–90. doi:10.1509/jm.15.0417.
29. Wedel M, Kannan PK. Marketing analytics for data-rich
environments. J Mark. 2016;80(6):97–121. doi:10.1509/jm.15.0413.
30. Wetzels M, Odekerken-Schröder G, van Oppen C. Using PLS path
modeling for assessing hierarchical construct models: guidelines
and empirical illustration. MIS Q. 2009;33(1):177–95. doi:10.2307/
20650284.
31. Dremel C, Herterich MM, Wulf J, Vom Brocke J. Actualizing big
data analytics affordances: A revelatory case study. Inf Manag.
2020;57(1):103121. doi:10.1016/j.im.2018.10.007.
32. Xu Z, Frankwick GL, Ramirez E. Effects of big data analytics and
traditional marketing analytics on new product success:
A knowledge fusion perspective. J Bus Res. 2016;69(5):1562–66.
doi:10.1016/j.jbusres.2015.10.017.
33. Zeng J, Glaister KW. Value creation from big data: looking inside
the black box. Strateg Organ. 2018;16(2):105–40. doi:10.1177/
1476127017697510.
34. Günther WA, Rezazade Mehrizi MH, Huysman M, Feldberg F.
Debating big data: A literature review on realizing value from big data.
J Strat Inf Syst. 2017;26(3):191–209. doi:10.1016/j.jsis.2017.07.003.
35. Wamba SF, Gunasekaran A, Akter S, Ren SJF, Dubey R, Childe SJ.
Big data analytics and firm performance: effects of dynamic
capabilities. J Bus Res. 2017;70:356–65. doi:10.1016/j.jbusres.
2016.08.009.
36. Vorhies DW, Orr LM, Bush VD. Improving customer-focused
marketing capabilities and firm financial performance via market­
ing exploration and exploitation. J Acad Mark Sci. 2011;39
(5):736–56. doi:10.1007/s11747-010-0228-z.
37. Xu H, Guo H, Zhang J, Dang A. Facilitating dynamic marketing
capabilities development for domestic and foreign firms in an
emerging economy. J Bus Res. 2018;86:141–52. doi:10.1016/j.
jbusres.2018.01.038.
38. Merrilees B, Rundle-Thiele S, Lye A. Marketing capabilities: ante­
cedents and implications for B2B SME performance. Ind Mark
Manag. 2011;40(3):368–75. doi:10.1016/j.indmarman.2010.08.005.
39. Quach S, et al. Toward a theory of outside-in marketing: past,
present, and future. Ind Mark Manag. 2019.
40. Wilden R, Gudergan S. The impact of dynamic capabilities on
operational marketing and technological capabilities: investigating
the role of environmental turbulence. J Acad Mark Sci. 2015;
43:181–99.
41. Conboy K, Mikalef P, Dennehy D, Krogstie J. Using business
analytics to enhance dynamic capabilities in operations research:
A case analysis and research agenda. Eur J Oper Res. 2020;281
(3):656–72. doi:10.1016/j.ejor.2019.06.051.
42. Mikalef P, Krogstie J, Pappas IO, Pavlou P. Exploring the relation­
ship between big data analytics capability and competitive perfor­
mance: the mediating roles of dynamic and operational
capabilities. Inf Manag. 2020;57(2):103169. doi:10.1016/j.
im.2019.05.004.
43. Torres R, Sidorova A, Jones MC. Enabling firm performance
through business intelligence and analytics: a dynamic capabilities
perspective. Inf Manag. 2018;55(7):822–39. doi:10.1016/j.im.20
18.03.010.
JOURNAL OF COMPUTER INFORMATION SYSTEMS
44. van Rijmenam M, Erekhinskaya T, Schweitzer J, Williams M-A.
Avoid being the Turkey: how big data analytics changes the game
of strategy in times of ambiguity and uncertainty. Long Range
Plann. 2019;52(5):101841. doi:10.1016/j.lrp.2018.05.007.
45. Benoit DF, Lessmann S, Verbeke W. On realising the utopian
potential of big data analytics for maximising return on marketing
investments. J Market Manage. 2020;36(3/4):233–47. doi:10.1080/
0267257X.2020.1739446.
46. Day GS. The capabilities of market-driven organizations.
J Market. 1994;58:37–52. doi:10.1177/002224299405800404.
47. Morgan NA, Vorhies DW, Mason CH. Market orientation, mar­
keting capabilities, and firm performance. Strat Manage J. 2009;30
(8):909–20. doi:10.1002/smj.764.
48. Ngo LV, O’Cass A. Performance implications of market orientation,
marketing resources, and marketing capabilities. J Market Manage.
2012;28(1–2):173–87. doi:10.1080/0267257X.2011.621443.
49. Helfat CE, et al. Dynamic capabilities: Understanding strategic
change in organizations. Blackwell: London; 2007.
50. Sun S, Cegielski CG, Jia L, Hall DJ. Understanding the factors
affecting the organizational adoption of big data. J Comput Inf
Syst. 2018;58(3):193–203. doi:10.1080/08874417.2016.1222891.
51. Janssen M, van der Voort H, Wahyudi A. Factors influencing big
data decision-making quality. J Bus Res. 2017;70:338–45.
doi:10.1016/j.jbusres.2016.08.007.
52. Holsapple C, Lee-Post A, Pakath R. A unified foundation for
business analytics. Decis Support Syst. 2014;64:130–41.
doi:10.1016/j.dss.2014.05.013.
53. Sanders NR. How to use big data to drive your supply chain. Calif
Manage Rev. 2016;58(3):26–48. doi:10.1525/cmr.2016.58.3.26.
54. Sivarajah U, Kamal MM, Irani Z, Weerakkody V. Critical analysis
of big data challenges and analytical methods. J Bus Res.
2017;70:263–86. doi:10.1016/j.jbusres.2016.08.001.
55. Day GS. Closing the marketing capabilities gap. J Mark. 2011;75
(4):183–95. doi:10.1509/jmkg.75.4.183.
56. Collis DJ. Research note: how valuable are organizational
capabilities? Strat Manage J. 1994;15:143–52. doi:10.1002/
smj.4250150910.
57. Stadler C, Helfat CE, Verona G. The impact of dynamic capabil­
ities on resource access and development. Organ Sci. 2013;24
(6):1782–804. doi:10.1287/orsc.1120.0810.
58. Peteraf M, Di Stefano G, Verona G. The elephant in the room of
dynamic capabilities: bringing two diverging conversations
together. Strat Manage J. 2013;34:1389–410.
59. Petter S, Straub D, Rai A. Specifying formative constructs in
information systems research. MIS Q. 2007;31(4):623–56.
doi:10.2307/25148814.
60. Ambrosini V, Bowman C. What are dynamic capabilities and are
they a useful construct in strategic management? Int J Manag Rev.
2009;11(1):29–49. doi:10.1111/j.1468-2370.2008.00251.x.
61. Vorhies DW. An investigation of the factors leading to the devel­
opment of marketing capabilities and organizational effectiveness.
J Strat Market. 1998;6(1):3–23. doi:10.1080/096525498346676.
62. Vorhies DW, Morgan NA. Benchmarking marketing capabilities
for sustainable competitive advantage. J Mark. 2005;69(1):80–94.
doi:10.1509/jmkg.69.1.80.55505.
63. Fang E, Zou S. Antecedents and consequences of marketing
dynamic capabilities in international joint ventures. J Int Bus
Stud. 2009;40(5):742–61. doi:10.1057/jibs.2008.96.
64. Kohli AK, Jaworski BJ. Market orientation: the construct, research
propositions, and managerial implications. J Market.
1990;54:1–18. doi:10.1177/002224299005400201.
65. Martin SL, Javalgi RG. Entrepreneurial orientation, marketing
capabilities and performance: the moderating role of competitive
intensity on Latin American international new ventures. J Bus
Res. 2016;69:2040–51.
66. Asseraf Y, Luis Filipe L, Shoham A. Assessing the drivers and
impact of international marketing agility. Int Mark Rev. 2019;36
(2):289–315. doi:10.1108/IMR-12-2017-0267.
67. Paul J. Masstige model and measure for brand management. Eur
Manag J. 2019;37(3):299–312. doi:10.1016/j.emj.2018.07.003.
9
68. Rust RT. The future of marketing. Int J Res Mark. 2020;37
(1):15–26. doi:10.1016/j.ijresmar.2019.08.002.
69. Veloutsou C, Guzman F. The evolution of brand management
thinking over the last 25 years as recorded in the journal of
product and brand management. J Prod Brand Manag. 2017;26
(1):2–12. doi:10.1108/JPBM-01-2017-1398.
70. Sun Z, Huo Y. The spectrum of big data analytics. J Comput Inf
Syst. 2019;1–9. doi:10.1080/08874417.2019.1571456.
71. Kitchens B, Dobolyi D, Li J, Abbasi A. Advanced customer analy­
tics: strategic value through integration of relationship-oriented
big data. J Manag Inf Syst. 2018;35(2):540–74. doi:10.1080/
07421222.2018.1451957.
72. Gandomi A, Haider M. Beyond the hype: big data concepts,
methods, and analytics. Int J Inf Manage. 2015;35(2):137–44.
doi:10.1016/j.ijinfomgt.2014.10.007.
73. Gefen D, Rigdon EE, Straub D. An update and extension to SEM
guidelines for administrative and social science research. MIS Q.
2011;35(2):iii–A7. doi:10.2307/23044042.
74. Eisenhardt KM, Martin JA. Dynamic capabilities: what are they?
Strat Manage J. 2000;21(10–11):1105–21. doi:10.1002/1097-0266
(200010/11)21:10/11<1105::AID-SMJ133>3.0.CO;2-E.
75. Krishnamoorthi S, Mathew SK. Business analytics and business
value: A comparative case study. Inf Manag. 2018;55(5):643–66.
doi:10.1016/j.im.2018.01.005.
76. Oztekin A. Creating a marketing strategy in healthcare industry:
a holistic data analytic approach. Ann Oper Res. 2018;270(1/
2):361–82. doi:10.1007/s10479-017-2493-4.
77. McAfee A, Brynjolfsson E. Big data: the management revolution.
Harv Bus Rev. 2012;90:60–68.
78. Morgan NA, Slotegraaf RJ, Vorhies DW. Linking marketing
capabilities with profit growth. Int J Res Mark.
2009;26:284–93.
79. Sun Z, Strang K, Firmin S. Business analytics-based enterprise
information systems. J Comput Inf Syst. 2017;57(2):169–78.
doi:10.1080/08874417.2016.1183977.
80. Foley A, Fahy J. Seeing market orientation through a capabilities
lens.
Eur
J
Mark.
2009;43(1/2):13–20.
doi:10.1108/
03090560910923201.
81. Hulland J, Wade MR, Antia KD. The impact of capabilities and
prior investments on online channel commitment and
performance. J Manag Inf Syst. 2007;23(4):109–42. doi:10.2753/
MIS0742-1222230406.
82. Jaakkola M, Frösén J, Tikkanen H, Aspara J, Vassinen A,
Parvinen P. Is more capability always beneficial for firm perfor­
mance? Market orientation, core business process capabilities and
business environment. J Market Manage. 2016;32(13–14):1359–­
85. doi:10.1080/0267257X.2016.1181098.
83. He W, Tian X, Chen Y, Chong D. Actionable social media
competitive analytics for understanding customer experiences.
J Comput
Inf Syst.
2016;56(2):145–55. doi:10.1080/
08874417.2016.1117377.
84. LaValle S, et al. Big data, analytics and the path from insights to
value. MIT Sloan Manage Rev. 2011;52(2):21–32.
85. Drnevich PL, Kriauciunas AP. Clarifying the conditions and limits
of the contributions of ordinary and dynamic capabilities to
relative firm performance. Strat Manage J. 2011;32(3):254–79.
doi:10.1002/smj.882.
86. Popovič A, Hackney R, Tassabehji R, Castelli M. The impact of
big data analytics on firms’ high value business performance.
Inform Syst Front. 2018;20(2):209–22. doi:10.1007/s10796-0169720-4.
87. Spiess J, T’Joens Y, Dragnea R, Spencer P, Philippart L. Using big
data to improve customer experience and business performance.
Bell Labs Tech J. 2014;18(4):3–17. doi:10.1002/bltj.21642.
88. Pigni F, Piccoli G, Watson R. Digital data streams: creating value
from the real-time flow of big data. Calif Manage Rev. 2016;58
(3):5–25. doi:10.1525/cmr.2016.58.3.5.
89. Abbasi A, Sarker S, Chiang RH. Big data research in information
systems: toward an inclusive research agenda. J Assoc Inf Syst.
2016;17(2):i–xxxii. doi:10.17705/1jais.00423.
10
G. CAO ET AL.
90. Sousa CM, Bradley F. Cultural distance and psychic distance: two
peas in a pod? J Int Market. 2006;14(1):49–70. doi:10.1509/
jimk.14.1.49.
91. Bhalla G, Lin LY. Crops-cultural marketing research: a discussion of
equivalence issues and measurement strategies. Psychol Market.
1987;4:275–85.
92. Couper MP. Review: web surveys: A review of issues and approaches.
Public Opin Q. 2000;64(4):464–94. doi:10.1086/318641.
93. Hair JF, et al. A primer on partial least squares structural equation
modeling (PLS-SEM); Sage:2014. 328.
94. Hair JF, Matthews LM, Matthews RL, Sarstedt M. PLS-SEM or
CB-SEM: updated guidelines on which method to use.
Int J Multivar Data Anal. 2017;1(2):107–23. doi:10.1504/
IJMDA.2017.10008574.
95. Hair JF, Ringle CM, Sarstedt M. Partial least squares structural equa­
tion modeling: rigorous applications, better results and higher
acceptance. Long Range Plann. 2013;46(1):1–12. doi:10.1016/j.
lrp.2013.01.001.
96. Bagozzi RP, Youjae Y, Phillips LW. Assessing construct validity in
organizational research. Adm Sci Q. 1991;36(3):421–58. doi:10.2307/
2393203.
97. Krosnick JA. Survey research. Ann Rev Psychol. 1999;50
(1):537–67. doi:10.1146/annurev.psych.50.1.537.
98. Podsakoff PM, MacKenzie SB, Podsakoff NP. Sources of method
bias in social science research and recommendations on how to
control it. Ann Rev Psychol. 2012;63(1):539–69. doi:10.1146/
annurev-psych-120710-100452.
99. Podsakoff PM, Todor WD. Relationships between leader reward
and punishment behavior and group processes and productivity.
J Manage. 1985;11(1):55–73. doi:10.1177/014920638501100106.
100. Armstrong JS, Overton TS. Estimating nonresponse bias in mail
surveys. J Market Res (JMR). 1977;14(3):396–402. doi:10.1177/
002224377701400320.
101. Black JA, Boal KB. Strategic resources: traits, configurations
and paths to sustainable competitive advantage. Strat Manage
J. 1994;15:131–48. doi:10.1002/smj.4250151009.