For WizardKim-DPT1

Does big data mean big knowledge? Integration of big data analysis and conceptual model for social commerce research

Xuemei Tian1 • Libo Liu1

Published online: 1 October 2016

� Springer Science+Business Media New York 2016

Abstract The Big Data era has descended on many communities, from govern- ments and e-commerce to health organizations. Information systems designers face

great opportunities and challenges in developing a holistic big data research

approach for the new analytics savvy generation. In addition business intelligence is

largely utilized in the business community and thus can leverage the opportunities

from the abundant data and domain-specific analytics in many critical areas. The

aim of this paper is to assess the relevance of these trends in the current business

context through evidence-based documentation of current and emerging applica-

tions as well as their wider business implications. In this paper, we use BigML to

examine how the two social information channels (i.e., friends-based opinion

leaders-based social information) influence consumer purchase decisions on social

commerce sites. We undertake an empirical study in which we integrate a frame-

work and a theoretical model for big data analysis. We conduct an empirical study

to demonstrate that big data analytics can be successfully combined with a theo-

retical model to produce more robust and effective consumer purchase decisions.

The results offer important and interesting insights into IS research and practice.

Keywords Big data � Business intelligence � Customer knowledge management � Customer purchase behavior

& Xuemei Tian [email protected]

Libo Liu

[email protected]

1 Department of Business Technology and Entrepreneurship, Faculty of Business and Law,

Swinburne University of Technology, Melbourne, Australia

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Electron Commer Res (2017) 17:169–183

DOI 10.1007/s10660-016-9242-7

1 Introduction

Since the 1960s, the interplay between hardware, software, and communications has

led to previously unforeseen advances in information systems. These developments

have been accompanied by shifts in the relationship between ‘carrier and content’,

and between manifestations of the latter as various data, information, knowledge,

and indeed wisdom [1]. During this time, concerns over data and information

overload increased, along with those focused on related organizational and

managerial challenges [2].

One response to these concerns saw the emergence of sub-disciplines, such as

competitive and business intelligence, and data, information, and knowledge

management [3–5]. Others came from management and organizational theory,

notably the resource-based theory, which emphasized the importance of internal

capabilities and competencies [6, 7], and later, knowledge-based views [8, 9], which

focused on the heterogeneous bundles of intangible resources (i.e., imperfectly

mobile, imperfectly imitable and non-substitutable) as a basis for competitive

advantage and organizational success.

As understanding of the nexus between information technology, information

systems, and business has increased, another core lesson has been learned—that

technology on its own is unlikely to provide the answer to the questions continually

posed by the management [10]. Whether it is relational databases, data mining, or

enterprise systems, the latest ‘big thing’ usually turns out to be much less of a

transformative force when introduced to the market. This is as likely to be true for

the latest such breakthrough, namely Big Data [11]. How to better utilize big data

assets, in addition to business assets, and human capital, to create value has become

a fertile ground for corporate competitive advantage. As big data analysis becomes

the next frontier for advancement of knowledge, innovation, and enhanced decision-

making process, the significance of its impact on society as a whole should not be

underestimated [12].

The aim of this paper is to assess the relevance of these trends in the current

business context through evidence-based documentation of current and emerging

applications as well as their wider business implications. In this paper, we use the

BigML software package to investigate how two social information channels (i.e.,

friends-based and opinion leaders-based social information) influence consumer

purchase decisions on social commerce sites. We undertake an empirical study by

combining a theoretical model with the analysis of big data using BigML. This

methodology demonstrates that big data analytics can be successfully combined

with a theoretical model to produce more robust and effective consumer purchase

decisions. However, for this to happen, big data technologies must be further

developed to cope with the proposed big data analysis framework.

The rest of the paper is organized as follows. In Sect. 2, we provide a review of

the literature related to big data, business intelligence, social information processing

theory and online consumer reviews. In Sect. 3, we identify the research gap based

on the literature review. In Sect, 4, we conduct an empirical study to develop and

explain our methodology and compare the results from different models. In Sect. 5,

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we provide robust checks for our final model and conclude by discussing the wider

implications of our study.

2 Literature review

2.1 Big data and business intelligence

Big data has been labelled as the fifth wave in the technology revolution, after the

mainframe, the PC, the Internet, and Web 1.0 eras, and more recently, mobile and

Web 2.0 eras [13]. It combines an architectural paradigm shift in data movement in

which instead of bringing data for centralized computation, the aim is to push the

computation to distributed locations [14]. Big data analytics have been used to

describe data sets and analytical techniques in applications that are so large (from

terabytes to exabytes) and complex (from sensor to social media data) that they

require advanced and unique data storage, management, analysis, and visualization

technologies [12]. Considering the seemingly limitless spectrum, big data can

utilized for social networks, web server logs, traffic flow sensors, satellite imagery,

broadcast audio streams, banking transactions, MP3 s of rock music, the content of

web pages, scans of government documents, GPS trails, telemetry from automo-

biles, financial markets and so on [15]. Dumbill (2012) explains that although any

data can be regarded as ‘Big’ when size is too large to be handled by conventional

systems, it is also the case that the dramatically increased volume (there is too much

of it), velocity (it is moving too fast), and variety (it is not structured in a useable

way) do not longer fit in the structures of database architectures [12, 15]. More

recently, three more characteristics have been mentioned by other researchers, such

as Daniel [16]: (1) value (a source of competitive advantage), (2) veracity (the

biases, noise and abnormality in data), and (3) verification (refers to data verification

and security). Daniel [16] argues that these three attributes also represent Big Data’s

fundamental properties as they are linked to data accuracy, a concept associated

with the longevity of data and their relevance to analysis outcomes, as well as the

length required to store data in a useful form for appropriate value-added analysis.

Clearly, today more than ever, data is a key resource and corporations are striving

to generate it at innumerable data points to create value and obtain competitive

advantage [17]. As will be seen below, further iterations of the concept are ongoing,

as through a combination of technological change, regulatory processes, and

managerial decision making, businesses are collecting and storing data at an

astonishing rate. In turn this is enabling organizations to employ business

intelligence to make faster and more reliable information-based business decisions

[18].

It is claimed that the advent of big-data technologies has rendered obsolete the

separation of information-centric systems, such as data warehouses (once reserved

for strategic decision support) and those, like enterprise resource systems

(supporting daily operations) [19]. The integrated management of transactional

and operational data, especially in new post-bureaucratic structures promises

improved business value through better-informed decisions, the discovery of hidden

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insights and automation of business processes [20]. In addition, the resultant

decisions can be made on the basis of data and rigor rather than gut-feeling [12],

albeit increasingly in situations where big data is placing transformative data

discovery and advanced analytics tools into the hands of customers [21] and where

concerns have already been expressed about risks to the security of proprietary

knowledge [22].

Furthermore, while there is plenty of enthusiasm in business circles for big data,

there are those who argue that there is little special about it, seeing it as merely a

magnification of the features found in knowledge management, and others

predicting that its popularity will eventually wane owing to its complexity and a

shortage of qualified workers [23]. It is also important to balance matters of data

size against the context of its application. A survey conducted jointly by IBM and

Oxford University Business School revealed that 30 % of respondents did not know

what ‘big’ meant for their organizations [24]. Elsewhere it has been observed that

considerations of adequacy and relevance might require ‘not so big data’ [25],

something that might apply to smaller businesses, who are also expected to adopt

these technologies, for example through SaaS or as a component of ERP systems

[26].

An important component of the ongoing case for big data is calls for education

and training of a new breed of statisticians and analysts for the proper use of BI

applications [27], big data scientists operating as multifunctional problem solvers

communicating between different departments [28, 29]. Likewise, the professional

development and career progression of in-house analysts—already familiar with the

organization’s unique business processes and challenges—has also been identified

as a top priority for business executives [24]. This might well extend to the

operational personnel, to reflect their needs for analytical and decision making

competencies consequent upon ongoing systems integration and the breaking down

of traditional organizational hierarchies [19].

As with other such concepts, the meaning and interpretation of business

intelligence (BI) has shifted over the years. At its simplest, BI refers to the ability to

use information to gain a competitive advantage. This includes data on education,

skills, and the past performance of employees to help businesses identify the critical

talent within their organizations and ensure its development and retention [29]. BI is

often referred to as the techniques, technologies, systems, practices, methodologies,

and applications that analyze critical business data to help an organization better

understand its business and respond to the market through timely decisions [30].

Moving beyond BI to the development of more advanced apps and an enhanced

ability to drill down and answer the questions asked by BI engines [31], BI and Big

Data initiatives must advance together, rather than separately on parallel tracks. The

real value lies in integrating the existing BI and analytics capabilities with the new

big data technologies and techniques to focus on how these new capabilities can

augment and extend the existing environment [31].

It is clear that much work remains to be done for the anticipated benefits of big

data to be realized. The remaining issues of clarification around the specific content

of these large datasets (primitive data elements, information, knowledge or meta-

knowledge) and the relationship between terms like data, information, and

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knowledge [25]. There are also risks to do with the misuse of data or with ‘false

discoveries’ in trying to find meaningful needles in massive haystacks of data [11].

Hence, this calls for improvement in such areas as data analysis, acquisition,

extraction and cleaning, integration, representation, and interpretation thorough

integration of systems. More generally, there are matters of the benefits and risks of

big data, both in organizational terms and as regards society as a whole, whether

these relate to potential problems in the areas of privacy, security and ethics, or to

emergence of a new version of the Digital Divide, a digital network divide [1, 32].

Critically, this ‘softer’ dimension to developments in big data extends to behavioral

and cultural issues at organizational level, and would extend to a much wider set of

norms and behaviors.

The promises of Big Data are real, however, there is currently a wide gap

between its potential and realization. Many leading researchers, such as Agrawal

et al. [33] and Zicari [34], emphasize the opportunities and challenges in big data

research and argue that the challenges include not just the obvious issues of scale,

but also heterogeneity, lack of structure, error-handling privacy, timeliness,

provenance, and visualization. These problems impede the progress at all phases

in the pipeline (acquisition/recording, extraction/cleaning annotation, integration/

aggregation/representation, analysis/modeling, and interpretation). Better under-

standing and management of these problems is the key of creating business value

from data [33]. The task of creating real value from data cannot be achieved by

simply by focusing on development of new technologies; it also requires us to

fundamentally rethink how we manage data analysis. The purpose of this paper is to

introduce a concept on how to manage data analysis.

2.2 Social information processing theory and online consumer reviews

The growing availability and popularity of social commerce sites have increased the

importance of online social information as a market force [35]. Previous studies

have revealed an association between eWOM (electronic word-of-mouth) and

product sales/revenue, which is mostly explained through either the awareness or

persuasive effects [36]. Alshibly and Chiong [37] showed that customer empow-

erment can increase e-government success. Opinion-based social information in the

form of eWOM communication can take place through several channels. For

example, consumers can post their opinions, and reviews of products on weblogs,

review websites, and social networking sites. Other studies have found that the

volume of consumer reviews is significantly related with product sales [38, 39].

Nowadays, online consumer reviews are becoming increasingly prevalent in the

majority of online shopping sites. Consumers use them either to find products that

match their preferences or to find information useful for offline purchases.

Typically, online consumer reviews can be deconstructed into several dimensions.

Scholars focusing on these have reported the different influences of such reviews on

the choice and sale of products. For instance, Kostyra et al. [40], focusing on

valence (i.e., average rating), volume (i.e., number of customer ratings) and

variance (i.e., variation in ratings) of online consumer reviews (also see [41]), found

that volume and variance only serve as moderators of the effect of valence. As well

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it was revealed that they do not have a direct effect on consumers’ product choice.

Focusing on the volume and valence of online movie reviews, Liu [42] documented

that review volume, rather than review valence, increases both aggregate and

weekly box office revenue. By taking this finding a step further, Duan et al. [43]

proposed a dynamic simultaneous equation system in which the two dimensions

could be considered as both a precursor to, and an outcome of, retail sales. That is,

both a movie’s box office revenue and review valence significantly increase review

volume, which in turn leads to better box office performance. Moreover, as noted

earlier, previous studies concerning the effects of online consumer reviews have

investigated the issues either from the perspective retailers or consumers. In our

study, we focus on the effect valence of review on consumer purchase decision.

3 Research framework

In the foregoing literature review a number of commonalities emerged between

sources. Firstly, researchers examined knowledge contribution in general, with a

special focus on knowledge sharing. Although some studies noted that social

relational activities influence knowledge sharing [44], few studies explicitly

investigated social relational activities. Secondly, most studies relied on subjective

data collected through surveys, case studies, and focus groups to explore how and

why customers participate in online social communities. Straub et al. [45] argued

that actual usage and perceived usage are not always congruent. Instead of using

self-reported contributions, we used field data to measure the actual contributions.

Thirdly, we worked on the premise that extracting useful knowledge from big data

requires not only scalable analytics services, but also theoretical support.

Boyer et al. [46] identified the obstacles to successful BI projects as not as much

technological, as lying within the organization itself. They thus advocated strategic

enterprise BI programs that would enable business users to make informed decisions

based on the collaborative leveraging of enterprise-wide information. This

prediction seems relevant today and to the circumstances of big data, whose

analytics-driven insights must be closely linked to business strategy, easy for end

users to understand, and embedded into organizational processes to enable action at

the right time [47]. The strategic significance of such activities is emphasized by

Elbashir et al. [48] who recognize the critical importance of collaborative

knowledge synergies between senior management, CIOs and IT managers in

facilitating improved decision making across the organization’s value chain. For

present purposes this is a reminder of the importance of the knowledge component,

both in relation to current expectations of big data, and in acknowledgement of the

fact that many organizations are still failing to use available data and knowledge

effectively, let alone being in a position to accommodate the demands of big data.

Admittedly big data really could turn out to be different, but another lesson from

experience is that the more data and information are available, the greater the need

for human judgement in decision making. Even in reporting big data-related

advances in cognitive augmentation and job automation, McGovern [49] concedes

that there are still many tasks where the combination of humans and machines

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produce superior results. As shown in Fig. 1, the availability of data alone does not

equate to value creation [25], and what is often missing is the knowledge to extract

wisdom from it, and to take decisions about what data to keep and what to discard,

and how to store what is kept reliably with the right metadata [50]. Processing data

requires not only the right technologies and analytic tools/techniques, but also

consideration of the wider organizational dimension. This will mark the transfor-

mation from big/massive data to knowledge which can be used for making business

predictions and decisions. This raises questions not only as to the relative value of

big data predictions and knowledge-based decisions, but also as to future

relationships between big data and knowledge management.

In this paper, to support the above arguments, we use two empirical studies to

investigate the influence of social relational activities on customer purchase

decisions. We also compare the findings from two studies which indicate that big

data analysis may not provide the best results without consideration of the

organizational dimension.

4 Empirical study

We conducted an empirical study by combining a big data tool (BigML) and a

conceptual model. We used BigML to examine how the two social information

channels (i.e., friends-based social information and opinion leaders-based social

information) influence consumer purchase decisions on social commerce sites.

Fig. 1 Big data analysis framework

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4.1 Data collection

The data for this study was collected from a popular social commerce site in Asia,

which provides a platform for consumers to share their experience related to the

purchased cosmetics, and to interact with other consumers. A consumer in the social

commerce site can rate, for example, a cosmetics product (of a particular brand)

while sharing her or his experience about this product. The consumer can also

follow other consumers whose posts or ratings are useful. Figure 2 shows the social

network structure of ego consumer network in this community. As the provision of

ratings is optional, some consumers choose not to provide ratings about their

products or a particular brand.

In total, the website includes 10,097 products from 60 different brands. All of

brands were used for analysis in this study. Moreover, there are 163,845 consumers

within sixty brands. We crawled panel data from this site in Nov 2012. Based on the

customers ID, we crawled the network data for each customer from the community.

Specifically, we collected the ‘‘following’’ and ‘‘follower’’ of customers’ lists and

built an egocentric network for each customer. Then we crawled friends-based

social information (i.e., friends’ review valence and friends’ purchase) and opinion

leaders-based social information (i.e., opinion leaders’ review valence and opinion

leaders’ purchase). Table 1 presents the summary statistics of the variables.

4.2 Operationalization of variables

Opinion leaders’ review valence is operationalized as the total score of ratings (on

products in a particular brand) provided by individuals who are the members’

reference persons.

Friends’ review valence is operationalized as the total score of ratings provided

by individuals who are friend of a consumer in the social commerce site.

Opinion leaders’ purchase is operationalized as the total number of products in

the buy-lists of a consumer’s reference persons in the social commerce site.

Fig. 2 Social network of ego customers

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Friends’ purchase is operationalized as the total number of products in the buy-

lists of a consumer’s friends in the social commerce site.

Consumer purchase behavior is operationalized as the number of products in a

consumer’s buy-list. Such operationalization was considered because consumers

can add products to their buy-lists to indicate that they have already bought specific

products in the social commerce site.

4.3 Data analysis tool: BigML

BigML (https://bigml.com) is an approach to machine learning. Users can set up

data sources, create, visualize and share prediction models, and use models to

generate predictions. It implements a set of basic data mining procedures. BigML

includes several advanced procedures that support interactive analysis of big data.

For example, data histograms are first rendered on a data sample and then updated

when the entire data set is loaded and processed. Decision trees provide a supervised

machine learning technique that starts with the root classification node, which is

then iteratively refined. Decision trees in BigML are rendered on the client appli-

cation dynamically, and evolve as their nodes are induced on the computational

server. More important, the process of preparing a model in BigML can be divided

into several consecutive steps: (1) data preparation, (2) association model learning,

(3) model selection, (4) model testing, and (5) development of model.

4.4 Phase 1: BigML analysis: base model

To analyze our data in BigML, we set consumer purchase decisions as our objective.

We first used BigML to run the selected dataset with the Base Model (1 Click

Model). The results of the decision tree are shown in Fig. 3. This indicates how

important each factor is in the prediction of consumer purchase decisions. Friends’

purchase is the most important factor, which is followed by opinion leaders’ review

and opinion leaders’ purchase. The result of the BigML analysis ran with the

Default Model is not consistent with the regression analysis. This means that the

Default Model does not fit the business rules well, which are artifacts of the data.

BigML’s Default Model does not fit the business rules, and therefore we should

adjust the training model by integrating a conceptual framework from the existing

literature.

Table 1 Descriptive statistics Mean SD

Friends 6.92 8.37

Opinion leaders 4.07 17.66

Friends’ review valence 5.99 16.50

Friends’ purchase 1.53 8.05

Opinion leaders’ review valence 5.05 29.56

Opinion leaders’ purchase 1.07 14.61

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4.5 Phase 2 BigML analysis: integrating the conceptual model into the base model

4.5.1 Conceptual research model

Based on social information processing theory, we propose a conceptual research

model shown in Fig. 4, which should be integrated with the default model of

BigML.

Consequently we developed the following hypotheses:

H1a Friends’ review valence will positively influence ego-consumer purchase behavior.

Fig. 3 Results from training data with the default model

Fig. 4 Research model

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H1b Friends’ purchase will positively influence ego-consumer purchase behavior.

H2a Opinion leaders’ review valence will positively influence ego-consumer purchase behavior.

H2b Opinion leaders’ purchase will positively influence ego-consumer purchase behavior.

Based on the conceptual framework, we conducted the following steps to adjust

the model in BigML: (1) association model learning; (2) model testing; and (3)

development of model of purchase decision marking. The results from running the

data set with the adjusted model are shown in Fig. 5. The results indicate that the

adjusted model fits the theoretical model better and therefore provides more

accurate results concerning the antecedents of consumers’ purchase decisions.

4.6 Robust check

Consumer purchase decision is represented by using a count variable summing up

all the products that are added to a buy-list. Negative binomial regression is often

used to analyze count data. Thus we use negative binomial regression to analyze the

dataset. To test the robustness of the adjusted model from BigML, we ran a negative

binomial regression analysis for the model.

Consumer Purchase Decision ¼ b0 þ b1Opinionleader Review þ b2Friend Review þ b3Opinionleader Purchase þ b4Friend Purchaseþ w ð1Þ

The regression analysis results are summarized in Table 2. The results indicate

that friends’ reviews valence have a significant positive effect (b = 0.012, p\ 0.001) on consumers’ purchase decisions. The regression results suggest that

Fig. 5 BigML analysis model 2

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opinion review also has a significant positive effect (b = 0.051, p\ 0.001) on consumers’ purchase decisions. Table 2 reveals that opinion leaders’ review valence

exerts a significant positive moderating effect on consumers’ purchase decisions

(b = 0.006, p\ 0.001). The positive coefficient (b = 0.035, p\ 0.001) indicates that opinion leaders’ purchase exerts a significant positive effect on consumers’

purchase decisions. Furthermore, the results are consistent with the adjusted model

of BigML. Therefore, our model developed in BigML is robust.

5 Discussion and conclusion

5.1 Research implications

Recently, the Big Data era has descended on many communities, from governments

to e-commerce to health organizations. To avoid being data driven, the big data

analysis process should be adopted to leverage the opportunities presented by the

abundant data and domain-specific analytics. In addition, while machine learning

algorithms exist, most of them produce ‘‘black box’’ models, which are difficult to

understand. This study presents a complete implementation of a big data analysis

process by integrating machine learning with a theory based on business rules from

the existing literature. Instead of developing machine learning models solely on

specific training datasets, researchers should integrate theory based business rules to

adjust the machine learning models and use test datasets to test the adjusted machine

learning models in order to attain comprehensive models that are more relevant and

better reflect the reality (see Fig. 6).

The literature of big data emphasises the application of algorithms to pattern

analysis and prediction. We have created a big data processing model to emphasise

that processing data requires not only the right technologies and analytic tools/

techniques, but also consideration of the wider organizational dimension. This will

drive the transformation from big/massive data to knowledge to business value

processes (as shown in Fig. 5 below). This raises questions not only as to the

relative value of big data predictions and knowledge-based decisions, but also as to

future relationships between big data and knowledge management. Big Data is still

in its infancy and is quintessentially a technology or set of still developing

Table 2 Results of regression analysis

Model Unstandardized coefficients Sig.

B Std. error

(Constant) .364 .004 .000

Friends’ review valence .012 .000 .000

Friends’ purchase .051 .001 .000

Opinion leaders’ review valence .006 .000 .000

Opinion leaders’ purchase .035 .001 .000

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technologies. Experience has shown time and again that the contribution of new

technologies is dependant very much on organizational and especially, human

factors.

5.2 Conclusion

Making sound business decisions based on accurate and current information and

knowledge requires more than simple intuition, and BI has become indispensable to

organizational success in the global economy. We need not only to be skilled

engineers, but also domain experts. Big data will become big knowledge through the

combination of data mining of association rules and the conceptual model of

business rules. Data mining and domain experts will be able to use this for

extending the possibilities of the model not only for user-defined business rules, but

also for models generated from association rules gained from machine learning.

This paper started from the perception that big data and business intelligence,

while facing challenges in developing systems for a new data savvy generation, had

the potential to leverage opportunities presented by the abundant data and domain-

specific analytics needed in many critical areas.

This study assessed the relevance of these trends in the current business context

through evidence-based documentation of current and emerging applications as well

as their wider business implications. We used the comprehensive model after

verifying that the machine learning model was inaccurate and thus unsuitable to the

task in hand. The proposed framework was tested using in an empirical study the

results of which suggest that big data can become big knowledge by the

combination of data mining of association rules and a conceptual model of business

rules. This big data analysis framework and theoretical model contributes to the

existing literature and offers important and interesting insights to IS research and

practice.

Fig. 6 Big data analysis process

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