Big data research for the knowledge economy: past, present, and future

Citation

Wang, X., White, L. and Chen, X. (2015), "Big data research for the knowledge economy: past, present, and future", Industrial Management & Data Systems, Vol. 115 No. 9. https://doi.org/10.1108/IMDS-09-2015-0388

Big data research for the knowledge economy: past, present, and future

Article Type: Guest editorial From: Industrial Management & Data Systems, Volume 115, Issue 9.

1. Introduction

Big data has emerged as a new scientific paradigm that has made a tidal wave across various sectors in the knowledge economy ranging from national security to scientific discovery, from economic and business activities to public administration (Chen et al., 2012; McAfee and Brynjolfsson, 2012; Chen and Zhang, 2014). Big data has attracted enormous attention in recent years due to its huge operational and strategical potential. However, it is not straightforward for potential adopters to understand the concept and capture the value of big data. This may be caused by many different definitions of big data highlighting various aspects of the concept. Among these definitions, the notion of “V” is often used by scholars and practitioners to help define big data (McAfee and Brynjolfsson, 2012; Lycett, 2013; Erevelles et al., 2015; Fosso Wamba et al., 2015). The big data definitions have evolved from the classic three “V”s definition: volume, variety, and velocity, to a more recent two additional Vs: value and veracity.

The phenomenon big data is mainly driven by the new technological and methodological development. The extensive application of information communication technologies (ICT) such as the internet, mobile phones, sensor devices, and social media networks in the modern world is generating enormous volume of data in various forms. As the internet has become many people’s preferable means to communicate, game, and shop, each log, search, and browse by millions of internet users on web sites, e.g. Google, Wikipedia, Amazon, eBay, and YouTube generate vast amount of data. Millions of mobile phone users also produce core data (e.g. phone calls, internet usage, and messaging) as well as ambient data as by-products of their daily activities. Meanwhile, more and more people use social media platforms such as Facebook and Twitter, to share and exchange information and give their reviews on products, services, and policies. Moreover, the wide implementation of product identification and sensory technologies, e.g. Global Positioning System (GPS), radio frequency identification technology (RFID), and time temperature indicator, provides huge amount of structured, semi-structured, and unstructured real time data across the supply chain of all industries (Lee and Park, 2008; Sarac et al., 2010; Wang and Li, 2012; Fosso Wamba et al., 2013).

On the other hand, the technological and methodological advances also enable many organizations to collect, store, and analyse huge scale of structured and unstructured data. There are countless organizations producing and using big data that are not just traditional IT companies. Many of them believe that big data research will significantly transform the way organizations run their operations. According to McKinsey Global institute (Manyika et al., 2011), the effective use of big data has the underlying benefits to transform economies, and deliver a new wave of productive growth. Companies can get a better understanding of customer needs and the business operations and services provided to their customers, which may be unknown to them or not achievable without big data and advanced analytics (Kiron et al., 2012; Davenport et al., 2013; Erevelles et al., 2015). Boyd and Crawford (2012) stated that big data has created a radical shift in how we define knowledge, how we think about research, and how we should engage with information. They believe this profound change has stoke out new terrains of objects, the constitution of knowledge, the processes of research, and definitions of social life. George et al. (2014) claimed that big data will fundamentally change the landscape of social and economic research and policy over the next decade. Through a comprehensive literature review, Fosso Wamba et al. (2015) summarized five types of business value creation from big data including: create transparency; enable experimentation to discover needs, expose variability, and improve performance; segment populations to customized actions; support decision making with automated algorithms; and innovate products, services, and business models.

While there is increasing enthusiasm for exploiting big data, i.e. large, fast-moving, and complex data sets, and making better use of quantitative and qualitative data from a range of “open” and administrative sources, there is nonetheless a large gap between big data and impact. For most organizations, they have more data than they know how to use them effectively (LaValle et al., 2011). One key pathway to the impact is to create valuable knowledge from big data, which will become a winning factor in today’s competitive world. Business managers, researchers, and government policy makers have to realize the strategic importance of harvesting big data in the knowledge economy. Previous studies, however, have focused heavily on data mining algorithms, and associated applications on the available data, and so on. Research regarding big data leading to insights for knowledge economy is under-studied. Many disciplines such as information management, operations management, finance, innovation management, etc., have contributed to vast knowledge generation, and yet the links between big data and this proliferation of knowledge is not well understood. The main purpose of this special issue is to reflect the recent developments made in this respect and it is our aim to link the past to the future by both looking back to what has been done and by looking forward to what needs to be done in big data research for the knowledge economy.

2. Development of big data research for the knowledge economy

There has been a growing academic and practitioner interest in big data, which is reflected in an increasing number of publications on big data, analytics, and applications in the past few years. These published articles cover a wide range of sectors including big data research in technology (Bradbury, 2011; Allen et al., 2012; Highfield, 2012), healthcare (Groves et al., 2013; Jee and Kim, 2013; Roski et al., 2014), public administration (Criado et al., 2013; Clarke and Margetts, 2014; Kim et al., 2014; O’Malley, 2014), and many other sectors. This section mainly focuses on the knowledge generation and value creation from big data in various functional disciplines of business and management.

2.1. Big data in manufacturing. Manufacturing is one of the five domains that McKinsey Institute claimed to have transformative potentials (Manyika et al., 2011). While the manufacturing processes generate vast data, it is also critical to process and manage large amounts of complex data due to the increasing demand on higher visibility and vertical integration with other enterprise systems (Li et al., 2015). Big data can play a significant role in extracting useful manufacturing knowledge, supporting progressive decision makings, and enhancing the productivity and competitiveness of manufacturing enterprises. Product quality monitoring in manufacturing is one area that is closely related to big data research. For instance, with the help of big data analytic, huge amount of RFID and sensor data can be used to monitor and track the product quality in real time (Wang et al., 2009). In addition, Abrahams et al. (2014) developed an integrated text analytic framework to use social media data for product defect discovery. Through the two case applications of the automotive and the consumer electronics industries, they demonstrate the effectiveness of using social media data as user-generated content to perform product defect discovery. Big data research has been employed in other manufacturing aspects to enhance the intelligence and productivities. For example, Zhong et al. (2015) proposed a visualization approach to reconstruct the RFID-enabled raw data given the production logics and time stamps. Through such an approach, they attempt to integrate Internet of Things and cloud manufacturing in order to transform and upgrade the traditional manufacturing to an more intelligent and efficient future (Zhong et al., 2015). To illustrate the impact of big data on world class sustainable manufacturing, Dubey et al. (2015) conducted an extensive literature on key factors that enable to achieve world class sustainable manufacturing through big data and developed a conceptual framework that denotes the role of big data analytics in world class sustainable manufacturing. In the big data environment, how to use big data to manage and improve manufacturing process will become the basis of competition and future growth for most manufacturers. To achieve its full potential, it is essential for the manufacturing sector to systematically integrate, manage, and analyse machinery and/or process data throughout the manufacturing life cycle in order to improve their efficiency and productivity (Lee et al., 2013).

2.2. Big data in marketing. With the data-rich market environments, big data research can help to deepen our understanding of consumer behaviour and improve marketing practices. For most companies, understanding who their customers are and what their needs are is essential for market analysis. Now, companies can make use of big data techniques to analyse historical transaction data, market research data, and other new forms of data such as users browsing record on internet web site to identify the most matched customers (Li et al., 2015). Among the existing literature on big data research in marketing, Erevelles et al. (2015) proposed a conceptual framework based on resource-based theory to examine the effect of big data on various marketing activities. Despite the significant potential of big data in the transformation of marketing activities, the authors also acknowledged that it is a complicated process of converting big data into a sustainable competitive advantage. Tirunillai and Tellis (2014) proposed a unified framework to extract latent dimensions from a sample of user-generated product reviews on an online chatter across 15 firms over four years period. Their findings show that dynamic analysis of this new form of data enables firms to track dimensions’ importance over time and dynamically map competitive brand positions on these dimensions over time. Homburg et al. (2015) used a supervised approach for sentiment analysis to examine how consumers react to active firm engagement in an online community environment. Their findings provide some important insights for marketing performance measurement and resource allocation on social media platforms. Despite the commercial promise of big data generated by social media networks and mobile devices in marketing research, the consequential ethical challenges of the emergence of big data has to be considered for market research (Boyd and Crawford, 2012; Nunan and Di Domenico, 2013).

2.3. Big data in accounting and finance. Accounting and finance are regarded as the discipline that is intrinsically data intensive. Naturally, they are also the functions that organizations tend to explore the potentials of big data to gain competitive advantages. For instances, internet users’ searches on the web sites like Google and Wikipedia itself generate vast volume of new data, and the data of finance-related searches can be analysed to predict stock market (Moat et al., 2013; Preis et al., 2013). Vasarhelyi et al. (2015) provided an overview of big data in accounting focusing on the sources, usages, and challenges of big data in accounting and auditing. While indicating the new opportunities and the significant potential benefits of big data, they also acknowledged the challenges and obstacles of incorporating non-traditional sources of data into the accounting and auditing domains. Colgren (2014) believed that the convergence of social media, big data, and cloud technologies can create a new channel of funding, crowdfunding, and it will significantly revolutionize the way small- and medium-sized enterprises and start-up companies accessing capital and impact the way management accountants around the world serve these clients. Fanning and Drogt (2014) pointed out that many firms use big data, e.g. social media data to analyse the target firm’s customers and markets in order to make more informed merger and acquisition decisions. In relation to the financial function and management accounting information provision, Bhimani and Willcocks (2014) recognized the potential benefits of big data as well as the complexities it could bring to organizations in order to deliver these potentials. They also stated that although big data and advanced analytics enable managers to take actions on structured and unstructured data but it also requires fundamental rethinking to integrate corporate strategy, firm structure, and information systems design.

2.4. Big data in product and service innovation. Product and service innovations are regarded by many people as one of the main value creations from big data. Manyika et al. (2011) claimed that big data is the next frontier for innovation which may give originations competitive advantages over their rival competitors. The wide adoption of the internet and mobile technology generates new forms of data in an astronomical scale. These forms of data such as mobile data, consumer web logs, and posts from social media platform, provide great opportunities for product and service innovations. With respect to product innovation, Li et al. (2015) claimed that employing big data techniques in product design enables companies to engage customers in the product design, and, therefore, enhances the innovation and quality of product design and achieve socialization design. Chan et al. (2015) proposed a new product development evaluation model by analysing social media data through a mixed method approach. Their research provides a novel way to engage users in the new product development through extracting value of enormous unstructured social media data. With respect to service innovation, Demirkan and Delen (2013) pointed that a service-orientation of big data research bring the capabilities extending from data/text mining to large-scale optimization, distributed simulation models, and highly complex multiple criteria decision problems. Opresnik and Taisch (2015) investigated how manufacturers can exploit the value of big data in servitization. Their finding shows that new revenue streams can be generated through the two data exploitation strategies: data reuse and data resell. These strategies can help manufacturers to differentiate other already servitized competitors.

3. Big data in supply chain management

Characterized by its three follows (material, finance, and information), supply chain management is the area where big data could make a more profound impact than any other business functions discussed earlier. With large volume of material flows and complex supply chain processes, more data than ever before is being generated and collected by ICT such as Point of Sales systems, Enterprise Resource Planning systems, and GPS across many industry sectors. In addition, other forms of data are also being generated exponentially from sources such as readings of sensors, consumer web logs, and footages of traffic control. This provides a significant opportunity to impact supply chain practices through original research. Waller and Fawcett (2013) claimed that big data, data science, and predictive analytics have brought a new revolution that will transform the way how supply chains are managed. Christopher and Ryals (2014) argued that new manufacturing technologies and enhanced information flows through big data enable supply chains to operate with quick customer response and lower inventory. Wang and Alexander (2015) indicated four main benefits of big data in supply chain management: improved supply chain visibility and product quality; higher operational efficiencies; personalized service and improved service quality; and new business models and better prediction.

Despite its promising potential, research on big data leading to new insights and knowledge for supply chain management is under-studied. Among them, Hofmann (2015) found in the simulation of a system dynamics model that the three characteristics of big data has potential to mitigate the supply chain bullwhip effect and among the three characteristics, “velocity” has the greatest potential to enhance performance. Ittmann (2015) used several industry examples where big data analytics have already been embraced, used, and implemented successfully, to illustrate the value of applying big data to supply chain management. Others use the supply side big data, e.g. RFID and sensor data to support supply chain decisions and innovations. For example, Wang et al. (2009) illustrated in the two case studies of food companies how the traceability data facilitated by RFID and sensor technologies can be employed to manage and improve supply chain processes. Li and Wang (2015) proposed a networked sensor data-driven dynamic pricing model in the chilled food supply chain. In their research, the pricing decision is based on the remaining product shelf-life, which is predicted through the real time sensor data when perishable food products go through supply chain processes. Zhong et al. (2015) developed a holistic big data approach to excavate the invaluable trajectory knowledge from enormous RFID-enabled production data in order to support decision makings on production logistics. Although RFID and sensory technologies were mainly adopted by many industry sectors for tracking and tracing purposes, with the rapid development of big data analytics, companies can gain more insights from the huge amount of available data and create new knowledge to improve supply chain management. In addition, supply chain researchers also start looking at other forms of data such as social media data to extract insights for supply chain decision making. For instance, Chae (2015) proposed an analytical framework to analyse supply chain-related tweets on social media platform, Twitter, for understanding the current trends in supply chain management. The research findings provide insights into the potential role of social media data for supply chain practices such as new product/service development, stakeholder engagement, demand management, and supply chain risk management. With respect to new business models and better prediction, Tan et al. (2015) developed an analytic infrastructure based on the deduction graph technique to help companies to capture the potential of big data in supporting supply chain operations. Their case study shows that the proposed approach enables the case company to obtain ideas from different sources of data such as existing customers’ characteristics and preferences, videos and images of available products, and social media data for new product development and manufacture new products in a more cost efficient way.

The emergence of big data concept provides a significant opportunity to the academics and practitioners to impact supply chain practices through original research on how big data research can be employed to improve supply chain management. However, there are challenges to extract the meaning of the information from enormous volumes of structured and unstructured data and provide important insights to support supply chain decisions. While acknowledging its significant implications in supply chain management, Waller and Fawcett (2013) also pointed that big data presents a great opportunity as well as challenge. They call for more research on supply chain management data scientist to bring clarity to the relevant of big data within the supply chain domain. Hazen et al. (2014) argued that supply chain professionals are overwhelmed with data, which motivates organizations to adopt and advance data analytic functions (e.g. big data, data science, and predictive analytics) in order to improve supply chain processes and their performance. The research team also points the importance of addressing data quality in supply chain research and calls for interdisciplinary collaboration to address the issue.

4. Scanning the issue

Seven good quality articles were selected among 30 submissions for publication in this special issue. A wide range of research topics are covered in the research presented in these articles including, literature review-based conceptual models, big data analytical approaches, and empirical studies on big applications. The articles were submitted by research teams from North America, Europe, and Asia. Contributions from the selected articles are briefly discussed in this section.

Base on a comprehensive literature review, Bhat and Quadri (2015) examined the current hardware technology trends in computation, networking, and storage that are available for big data analysis. The research highlights the challenges faced and solutions found by each hardware technology to cope with the general demands of big data problems. While the evolution of hardware technology considerably and significantly supports the generation, transportation, and analysis of big data, the research also pointed some important shortcomings and challenges of the current hardware technology trends. Beyond the cornerstone of business and management research, Amankwah-Amoah (2015) investigated different capacities of government in big data utilization through a comprehensive synopsis of the literature on big data and the role of governments in utilizing and harnessing big data. The study also proposes a data collection-data analysis matrix to characterise the role of governments in collection and analysis of big data, and provides an array of explanations to account for the difference between countries in using big data to solve social problems.

Again, social media data research is proved to be another popular area of big data research for the knowledge economy. Among them, Gaikar et al. (2015) proposed a social media analytics approach to use Twitter data to predict movie office collection. The research employs sentiment analysis and prediction algorithms to analyse the data extracted from Twitter web site for predicting Bollywood movies box office collection. The research findings show that pre-release sentiment and hype factor have a significant impact on the performance of movies’ box office sales. The insights gained from this research can be used by the movie industry to support their business decisions. He et al. (2015) proposed a novel framework for social media competitive intelligence to enhance market intelligence and increase business value. The analysis results of nearly half million tweets related to two large US retail chains (Walmart and Costco) show some useful patterns, which enable firms to gain market insights and enhance business intelligence. The obtained business knowledge and fostered innovations through social media analytics will lead to an increase in economic activities and, therefore, make significant impact on today’s knowledge economy.

Other papers published in this special issue also cover several topics including evaluation of uncertain in product design, design and construction of big data analytics systems, business failure prediction of supply chain finance clients. For instance, Afshari and Peng (2015) proposed a novel application of big data analytics to evaluate the external and internal uncertainty in product design process. A big data analytics-based competence method is developed to overcome the shortcomings of the agent-based model that is also applied in the study. Through the case study of a smartphone product, the research findings demonstrate that the proposed big data analytics method can help to identify product change during its product life cycle. The obtained early knowledge will enable firms to improve the efficiency of decision making in product design and minimize the operations cost. Chen et al. (2015) proposed an integrated collective intelligence model to address the variety, uncertainty, and complexity challenges faced by big data analytics systems. In their model instantiation, multi-agent paradigm and Petri Net are used as the specific model and the general model, respectively. Through the simulation study, it demonstrates that multi-agent paradigm is beneficial for reuse and integration of big data analytics modules, and the Petri Net models provide effective simulation results in the system design period. In the big data environment, in which, decision making requires the integration of multiple data analysis outputs. This research contributes to generate, organize, reuse, and integrate knowledge by collaborative agents in consensus decision. Zhao et al. (2015) examined the potential use of external financial big data set to improve of predictability of financial institutions on the business failure of new supply chain finance clients. Based on the evolutionary theory and resource-based theory, the authors developed a logistic regression model to predict the probability of the business failure of new supply chain finance model. The research findings illustrate how the external big data sets can be effectively leveraged through “unconventional” predictor variables by financial institutions in order to reduce the potential credit risks associated with business failure of supply chain finance clients.

5. Concluding remarks

Big data has become a buzzword in the academic and business world. It has been regarded by many scholars as the golden key to change the present and future (McAfee and Brynjolfsson, 2012; Sanders, 2014). Nevertheless, there are still some gaps that need to be addressed in this emerging and important field. It provides great opportunity for more studies that develop new theories, analytics, and applications to exploit full potential of big data and make real impact on the knowledge economy.

Despite the fact that big data research has gained rapid growth in recent years, there is a lack of grounded theories and acceptable conceptual frameworks around big data theme that enable researchers and organizations to capture the value of big data in a systematic manner. One important research avenue is to develop explanatory and predictive theories that could systematize the research with solid theoretical foundations. Furthermore, limited access to big data sources creates an uneven distribution among the existing big data research. For instance, social media-related big data research seems to be popular among the academics due to easy accessibility of the data sources on social media platforms, e.g. Facebook and Twitter (Rapp et al., 2013; Abrahams et al., 2014; Chae, 2015; Chan et al., 2015). In fact, other types of big data are also being generated and collected across various sectors offering the potential of new insights and knowledge into areas that are important to the knowledge economy. There is a crucial need for close collaboration between the academics and the industries to share technologies and data sources and work together in order to advance big data research and applications. Another avenue for research is to go beyond the knowledge economy and to consider more broadly where and how the field of industrial management and data systems can contribute to societal improvements through big data research. Fulfilling this potential will require the research inputs from interdisciplinary teams including data scientists, IT specialists, economists, mathematicians, strategists, sociologists, and other scholars.

Dr Xiaojun Wang - School of Economics, Finance and Management, University of Bristol, Bristol, UK

Professor Leroy White - Warwick Business School, University of Warwick, Coventry, UK

Professor Xu Chen - School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, China

Acknowledgement

The guest editors would like to thank the Editors in Chief of Industrial Management & Data Systems, Hing Kai Chan, and Alain Yee Loong Chong, who assisted us to initiate the special issue and supported its completion. The guest editors have been in a privileged position to evaluate 30 manuscripts addressing many important big data-related topics for this special issue. The seven papers selected in this special issue were shaped and refined by their authors with the support of many reviewers. The guest editors would like to thank the reviewers who dedicated their time to reviewing the manuscripts submitted to this special issue.

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Corresponding author

Professor Xu Chen can be contacted at: xchenxchen@263.net