Big Data Investment, Skills, and Firm Value
TL;DR: Analysis of how labor market factors have shaped early returns on big data investment using a new data source---the LinkedIn skills database--- underscores the importance of geography, corporate investment, and skill acquisition channels for explaining productivity growth differences during the spread of new information technology innovations.
Abstract: This paper considers how labor market factors have shaped early returns to investment in big data technologies. It tests the hypothesis that returns to early investments in Hadoop — a key big data infrastructure technology — have been concentrated in select labor markets due to the importance of aggregate corporate investment levels within a labor market for producing a supply of complementary technical skills during the early stages of technology diffusion. The analysis uses a new data source — the LinkedIn skills database — enabling direct measurement of firms’ investments into emerging technical skills such as Hadoop, Map/Reduce, and Apache Pig. Productivity estimates indicate that from 2006 to 2011, firms’ Hadoop investments were associated with 3% faster productivity growth, but only for firms a) with significant existing data assets and b) in labor networks characterized by significant aggregate Hadoop investment. Evidence for the importance of labor market concentration disappears for investments in mature data technologies, such as SQL-based databases, for which the skills are diffused and readily available through universities and other channels. These findings underscore the importance of geography, corporate investment, and channels for technical skill acquisition for explaining differences in productivity growth rates across labor markets during the spread of new IT innovations.
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TL;DR: Results empirically validate the proposed theoretical framework of this study and provide evidence that BDA capability leads to superior firm performance.
677 citations
TL;DR: A first step toward an inclusive big data research agenda for IS is offered by focusing on the interplay between big data’s characteristics, the information value chain encompassing people-process-technology, and the three dominant IS research traditions (behavioral, design, and economics of IS).
Abstract: Big data has received considerable attention from the information systems (IS) discipline over the past few years, with several recent commentaries, editorials, and special issue introductions on the topic appearing in leading IS outlets. These papers present varying perspectives on promising big data research topics and highlight some of the challenges that big data poses. In this editorial, we synthesize and contribute further to this discourse. We offer a first step toward an inclusive big data research agenda for IS by focusing on the interplay between big data’s characteristics, the information value chain encompassing people-process-technology, and the three dominant IS research traditions (behavioral, design, and economics of IS). We view big data as a disruption to the value chain that has widespread impacts, which include but are not limited to changing the way academics conduct scholarly work. Importantly, we critically discuss the opportunities and challenges for behavioral, design science, and economics of IS research and the emerging implications for theory and methodology arising due to big data’s disruptive effects.
543 citations
Cites background from "Big Data Investment, Skills, and Fi..."
...As one answer to this question, Tambe (2014) found that firms with significant existing data sets who invested in Hadoop were associated with 3 percent faster productivity growth....
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TL;DR: It is argued that, in practice, organizations need to continuously realign work practices, organizational models, and stakeholder interests in order to reap the benefits from big data, and identifies two socio-technical features of big data that influence value realization: portability and interconnectivity.
Abstract: Big data has been considered to be a breakthrough technological development over recent years. Notwithstanding, we have as yet limited understanding of how organizations translate its potential into actual social and economic value. We conduct an in-depth systematic review of IS literature on the topic and identify six debates central to how organizations realize value from big data, at different levels of analysis. Based on this review, we identify two socio-technical features of big data that influence value realization: portability and interconnectivity. We argue that, in practice, organizations need to continuously realign work practices, organizational models, and stakeholder interests in order to reap the benefits from big data. We synthesize the findings by means of an integrated model.
519 citations
Cites background from "Big Data Investment, Skills, and Fi..."
...Specifically, organizations face questions regarding not only how to acquire or develop technical and human resources (Brinkhues et al., 2015; Tambe, 2014), but also how to structure them in teams or departments....
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TL;DR: The present paper aims to provide a systematic literature review that can help to explain the mechanisms through which big data analytics (BDA) lead to competitive performance gains and identifies gaps in the extant literature and proposes six future research themes.
Abstract: With big data growing rapidly in importance over the past few years, academics and practitioners have been considering the means through which they can incorporate the shifts these technologies bring into their competitive strategies. To date, emphasis has been on the technical aspects of big data, with limited attention paid to the organizational changes they entail and how they should be leveraged strategically. As with any novel technology, it is important to understand the mechanisms and processes through which big data can add business value to companies, and to have a clear picture of the different elements and their interdependencies. To this end, the present paper aims to provide a systematic literature review that can help to explain the mechanisms through which big data analytics (BDA) lead to competitive performance gains. The research framework is grounded on past empirical work on IT business value research, and builds on the resource-based view and dynamic capabilities view of the firm. By identifying the main areas of focus for BDA and explaining the mechanisms through which they should be leveraged, this paper attempts to add to literature on how big data should be examined as a source of competitive advantage. To this end, we identify gaps in the extant literature and propose six future research themes.
431 citations
Cites background from "Big Data Investment, Skills, and Fi..."
...This lack of personnel with the appropriate skills is also noted in numerous studies, and constitutes a major constraint in realizing the full potential of these technologies (Tambe, 2014)....
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TL;DR: In this paper, the effects of big data analytics on supply chain agility, supply chain adaptability, and operational performance were investigated using 281 surveys, gathered using a pre-tested questionnaire.
251 citations
References
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Posted Content•
TL;DR: In this article, the authors examined the linkages between systems of high performance work practices and firm performance and found that these practices have an economically and statistically significant impact on both intermediate outcomes (turnover and productivity) and short and long-term measures of corporate financial performance.
Abstract: This paper comprehensively examined the linkages between systems of High Performance Work Practices and firm performance. Results based on a national sample of nearly one thousand firms indicate that these practices have an economically and statistically significant impact on both intermediate outcomes (turnover and productivity) and short- and long-term measures of corporate financial performance. Support for the predictions that the impact of High Performance Work Practices is in part contingent on their interrelationships and links with competitive strategy was limited.
8,131 citations
"Big Data Investment, Skills, and Fi..." refers background in this paper
...…Milgrom and Roberts (1990, 1994), has been the basis of an influential literature on organizational complementarities (Arora and Gambardella 1990, Huselid 1995, Milgrom and Roberts 1994, Ichniowski et al. 1997, Athey and Stern 1998, Bresnahan et al. 2002, Bloom et al. 2012) as well as a…...
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Book•
13 May 2011
TL;DR: The amount of data in the authors' world has been exploding, and analyzing large data sets will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus, according to research by MGI and McKinsey.
Abstract: The amount of data in our world has been exploding, and analyzing large data sets—so-called big data— will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus, according to research by MGI and McKinsey's Business Technology Office. Leaders in every sector will have to grapple with the implications of big data, not just a few data-oriented managers. The increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet of Things will fuel exponential growth in data for the foreseeable future.
4,700 citations
Posted Content•
TL;DR: In this paper, the spatial distribution of innovation activity and the geographic concentration of production are examined, using three sources of economic knowledge: industry R&D, skilled labor, and the size of the pool of basic science for a specific industry.
Abstract: Previous research has indicated that investment in R&D by private firms and universities can lead to knowledge spillover, which can lead to exploitation from other third-party firms. If the ability of these third-party firms to acquire knowledge spillovers is influenced by their proximity to the knowledge source, then geographic clustering should be observable, especially in industries where access to knowledge spillovers is vital. The spatial distribution of innovation activity and the geographic concentration of production are examined, using three sources of economic knowledge: industry R&D, skilled labor, and the size of the pool of basic science for a specific industry. Results show that the propensity for innovative activity to cluster spatially is more attributable to the influence of knowledge spillovers and not merely the geographic concentration of production. (SFL)
4,252 citations
Posted Content•
TL;DR: In this paper, the authors compare the organization of regional economies, focusing on Silicon Valley's thriving regional network-based system and Route 128's declining independent firm-based systems, and conclude that innovation should be a collective process, most successful when institutional and social boundaries dividing firms are broken down.
Abstract: Compares the organization of regional economies, focusing on Silicon Valley's thriving regional network-based system and Route 128's declining independent firm-based system. The history of California's Silicon Valley and Massachusetts' Route 128 as centers of innovation in the electronics indistry is traced since the 1970s to show how their network organization contributed to their ability to adapt to international competition. Both regions faced crises in the 1980s, when the minicomputers produced in Route 128 were replaced by personal computers, and Japanese competitors took over Silicon Valley's market for semiconductor memory. However, while corporations in the Route 128 region operated by internalization, using policies of secrecy and company loyalty to guard innovation, Silicon Valley fully utilized horizontal communication and open labor markets in addition to policies of fierce competition among firms. As a result, and despite mounting competition, Silicon Valley generated triple the number of new jobs between 1975 and 1990, and the market value of its firms increased $25 billion from 1986 to 1990 while Route 128 firms increased only $1 billion for the same time period. From analysis of these regions, it is clear that innovation should be a collective process, most successful when institutional and social boundaries dividing firms are broken down. A thriving regional economy depends not just on the initiative of individual entrepreneurs, but on an embedded network of social, technical, and commercial relationships between firms and external organizations. With increasingly fragmented markets, regional interdependencies rely on consistently renewed formal and informal relationships, as well as public funding for education, research, and training. Local industrial systems built on regional networks tend to be more flexible and technologically dynamic than do hierarchical, independent firm-based systems in which innovation is isolated within the boundaries of corporations. (CJC)
4,036 citations
TL;DR: A model of IT business value is developed based on the resource-based view of the firm that integrates the various strands of research into a single framework and provides a blueprint to guide future research and facilitate knowledge accumulation and creation concerning the organizational performance impacts of information technology.
Abstract: Despite the importance to researchers, managers, and policy makers of how information technology (IT) contributes to organizational performance, there is uncertainty and debate about what we know and don't know. A review of the literature reveals that studies examining the association between information technology and organizational performance are divergent in how they conceptualize key constructs and their interrelationships. We develop a model of IT business value based on the resource-based view of the firm that integrates the various strands of research into a single framework. We apply the integrative model to synthesize what is known about IT business value and guide future research by developing propositions and suggesting a research agenda. A principal finding is that IT is valuable, but the extent and dimensions are dependent upon internal and external factors, including complementary organizational resources of the firm and its trading partners, as well as the competitive and macro environment. Our analysis provides a blueprint to guide future research and facilitate knowledge accumulation and creation concerning the organizational performance impacts of information technology.
3,318 citations
"Big Data Investment, Skills, and Fi..." refers background in this paper
...Prior research focuses on the effects of organizational factors in explaining variation in IT returns (Melville et al. 2004 provide a review)....
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