Streams of digital data and competitive advantage: The mediation effects of process efficiency and product effectiveness

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Abstract

Firms can achieve a competitive advantage by leveraging real-time Digital Data Streams (DDSs). The ability to profit from DDSs is emerging as a critical competency for firms and a novel area for Information Technology (IT) investments. We examine the relationship between DDS readiness and competitive advantage by studying the mediation effect of product effectiveness and process efficiency. The research model is tested with data obtained from 302 companies, and the results confirm the existence of the mediation effects. Interestingly, we confirm that competitive advantage is more significantly impacted by IT investments affecting product effectiveness than those affecting process efficiency.

Introduction

The study of the impacts of big data on organizational outcomes has emerged as a central topic for both practitioners and academics [[1], [2], [3], [4], [5]]. The relevance of digital transformation, analytics, and big data projects is reflected in the agendas of chief information officers (CIOs), and other senior executives, for whom big data strategic initiatives remain a priority. Indeed, CIOs expect to deliver tangible business outcomes and produce revenue and market growth [6]. While early evidence supports the positive link between big data investments and organizational performance [7,8], firms benefit to various degrees from big data investments, and the organizational conditions affecting their performance are underexplored [4].

As we summarize in the theoretical background and in the Appendix—Table A1, while the study of big data has received tremendous attention in recent years in all major Information Systems (IS) journals, the research has focused mostly on the investigation of big data adoption and use [9,10] or has remained oriented more toward the study of organizational transformational outcomes than the assessment of its business value [5]. Moreover, even the studies that have established the link between big data and business value have investigated the relationship mainly through the lens of the resource-based view and its declinations, such as dynamic capabilities, resource orchestration, or absorptive capacity [4,5,8,[11], [12], [13], [14], [15]]. Only a limited number of studies have proposed an assessment of business value in terms of financial performance [[16], [17], [18]] or competitive advantage [4,5]. In addition, these studies have analyzed big data in a broad sense, and instanced big data in terms of business intelligence and analytics [18], big data analytics infrastructure [11], and big data analytics capability [4], limiting the possibility of deciphering the operational aspects of the phenomenon.

This lack of specificity in the analysis of big data is probably imputable to the polysemy of the term big data itself, which is used to represent a profusion of technologies, techniques, phenomena, and initiatives. For example, we observe that most definitions of big data [8,19] emphasize the dimension of the volume of the managed datasets and the capabilities to extract business insights from large datasets. When such a definition is applied to a company context, it may simultaneously refer to initiatives related to data management, data processing, data analysis, and data visualization, with very different operational aspects and consequences. Hence, we decided to concentrate our study on the organizational impact of one specific manifestation of big data, namely, Digital Data Streams (DDSs), following the path traced by previous studies [[20], [21], [22], [23]]. A DDS is defined as the “continuous digital encoding and transmission of data describing a related class of events” [21]. Therefore, DDSs result from human behavior (e.g., a tweet) or from activity by machines (e.g., a temperature reading from a connected thermostat). The DDS channels the digital representation of these events at their inception and makes them available for harvesting and later exploitation by organizations [24]. DDSs thus represent the natural unfolding of computing, which has progressed from batch processing to online transaction processing and has now reached the continuous processing of streaming data [[25], [26], [27]]. DDSs represent a promising level of analysis for understanding the organizational impacts of strategic big data–enabled initiatives [21], representing a key organizational resource that firms may leverage for creating competitive advantage [25]. By directly investigating the strategic initiatives stemming from DDSs, we may gain a more precise view of how a big data investment contributes to competitive advantage.

Since organizational conditions are critical barriers to the success of big data initiatives [28] and their study is still an open question [5], to assess how these conditions affect the performance of DDSs initiatives, we base our investigation on the concept of organizational readiness [29,30]. The organizational readiness has been shown to play a relevant role in organizational change [31] and implementation success [32], being a necessary condition for firms to manifest a competitive advantage. We investigate this link by looking at the organizational readiness in DDS, the DDS readiness [33].

From there, and inspired by the literature on Information Technology (IT) implementation success [[34], [35], [36]] and the ongoing discourse on success’ mediating variables (e.g. [8,17,37]), we advance that the implementation of DDS initiatives improves the competitive advantage of firms through the mediating effect of two other success dimensions of IT implementations: product success, in terms of product effectiveness, and process success, in terms of process efficiency [38]. We posit that taken together, these dimensions of IT implementation success yield a more comprehensive view of DDS impacts and may shed light on the complex relationship between big data and performance, both still open research questions [5]. We answer the following research questions: “To what extent does digital data stream readiness impact companies’ competitive advantage?” and “To what extent do product effectiveness and process efficiency mediate the relationship between digital data stream readiness and competitive advantage?”.

This paper represents one of the first attempts to measure the mediating effect of product effectiveness and process efficiency on the relationship between DDS readiness and competitive advantage. We contribute levering empirical data from a sample of 302 companies that participated in a questionnaire-based survey. The rest of the paper is structured as follows. First, we present the theoretical background and formulate our hypotheses. We then detail our methodology and present our results. We continue with a discussion of the findings, our conclusions, and guidelines for future studies.

Section snippets

Theoretical background

The study of the effects of IT investments and performance has a long tradition in the IS field, as the study of the strategic role of IT in creating and sustaining a competitive advantage [4,[39], [40], [41]]. Similarly, the literature investigating big data effects on performance is rapidly growing (see Appendix—Table A1). Until now, a large part of big data studies analyzed business value creation by looking at the role of analytics almost exclusively [5,8,42,43]. Therefore, the investigated

Research model and hypotheses development

Based on the conceptual model, we advance a research model (Fig. 2) and five testable hypotheses to assess the role of DDS readiness in affecting competitive advantage through both process efficiency and product effectiveness. We expect higher levels of DDS readiness to be positively associated with competitive advantage, indirectly, and through the mediating effects of the efficiency of processes and the effectiveness of the products and services delivered [73]. We separate process efficiency

Measurement development

Instead than opting for new scale development, measurement items were adapted from existing scales identified in literature and we considered the DDS initiatives as our unit of analysis. We operationalized DDS readiness as specified in previous research [22], with its four components: mindset, skillset, toolset, and dataset. Product effectiveness and process efficiency were adapted from existing and validated self-assessment performance measures, where process efficiency is about time to market

Sample and data collection

To test our hypotheses, we administered a questionnaire to a sample of firms with the support of Qualtrics (http://www.qualtrics.com), a company that specializes in data collection and analysis. We expressly targeted CIOs, CTOs, and senior IT managers in North American companies with more than 10 employees and annual revenues higher than 1 million dollars. Of the 732 responses received, 320 (43.7 %) qualified based on those criteria. We considered targeting CIOs, CTOs, and senior IT managers

Theoretical contributions

These results contribute to the enrichment of the body of research investigating the impacts of big data investment on firm performance, in several directions.

This study integrates the organizational readiness perspective, complementary to the resource-based view and dynamic capability theories [95], and the conceptualization of DDS initiatives to investigate the link between big data and performance. The advantages of this choice were twofold. On the one hand, it enabled us to look beyond the

Limitations and future research

This study enriches the literature on the business value of DDS and the competitive advantage of firms. While we provide evidence of the relationship between DDS readiness and competitive advantage through the mediation of product effectiveness and process efficiency, we cannot exclude the influence of other mediation effects. For example, the absorptive capacity [104] of companies, namely, the contextual ability of companies to translate change into performance, could play a mediating role, as

Conclusion

This study represents one of the first attempts to measure the mediating effect of product effectiveness and process efficiency on the relationship between DDS readiness and competitive advantage. To fill the gap in the literature on big data readiness, with this paper, we answered the following research questions: “To what extent does digital data stream readiness impact the competitive advantage of companies?” and “To what extent do product effectiveness and process efficiency mediate the

Data availability statement

Data cannot be shared since related to an FP7 European project whose data cannot be publicly available.

CRediT authorship contribution statement

Elisabetta Raguseo: Conceptualization, Methodology, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review & editing, Supervision, Project administration. Federico Pigni: Conceptualization, Validation, Investigation, Resources, Writing - original draft, Writing - review & editing, Supervision, Project administration, Funding acquisition. Claudio Vitari: Validation, Writing - original draft, Writing - review & editing, Supervision, Project

Acknowledgements

The authors acknowledge the support of the European Community through a Marie Curie Intra-European Fellowship that provided funds to one author of the paper. They also acknowledge the Advanced Practices Council (APC) of the Society for Information Management, a leading forum for senior IT executives that initiated and made this research possible.

Elisabetta Raguseo is Associate Professor at Politecnico di Torino (Turin, Italy). She is part of the Group of Experts for the Observatory on the Online Platform Economy of the European Commission, member of the Entrepreneurship and Innovation Centre at the Politecnico di Torino, and of European Industrial Engineering and Management Cluster. She was formerly Marie Curie Fellow at the business school Grenoble Ecole de Management (France). She has been involved in several national and

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  • Cited by (0)

    Elisabetta Raguseo is Associate Professor at Politecnico di Torino (Turin, Italy). She is part of the Group of Experts for the Observatory on the Online Platform Economy of the European Commission, member of the Entrepreneurship and Innovation Centre at the Politecnico di Torino, and of European Industrial Engineering and Management Cluster. She was formerly Marie Curie Fellow at the business school Grenoble Ecole de Management (France). She has been involved in several national and international research projects, and as a consultant for public and private institutions. Her research and teaching expertise is in strategic information systems, big data, smart work, tourism economics, redesign of value chains, and digital transformation. Her research was published in highly ranked international journals including International Journal of Production Research, Journal of Global Information Management, International Journal of Electronic Commerce, Information & Management, and International Journal of Information Management.

    Federico Pigni is Full Professor and director of the Global Tech program at the Grenoble Ecole de Management in France (GEM) and fellow at the Digital Data Stream Lab at Lousiana State University (LSU). He holds a PhD in Management Information Systems and Supply Chain Management. Prior to joining GEM, he taught at Carlo Cattaneo University - LIUC, Università Commerciale Luigi Bocconi and the Catholic University in Milan. He was Senior Researcher at Carlo Cattaneo University’s Lab#ID RFId (Radio Frequency Identification) laboratory and post-doctorate at France Télécom R&D—Pole Service Sciences in Sophia Antipolis (France). He participated in research projects funded by Italian, regional, and EU agencies, private industry, and government partners. He teaches in the area of Information Systems and he is currently researching value creation and appropriation opportunities stemming from big data and digital data streams and their impacts at the organizational and industry levels.

    Claudio Vitari is Full Professor at Aix-Marseille University (Marseille, France). His research interests include Information Systems, Strategic Management, and Ecological Economics. His publications encompass several articles in journals including Ecological Economics, Systèmes d'Information et Management, European Journal of Information Systems, Communications of the Association for Information Systems, International Journal of Knowledge Management, Knowledge Management Research & Practice, and Journal of Information Technologies: Cases and Applications. He has over 15 years’ experience in teaching, research, management, and consulting. He is member of the endowed chair “Mutations Anticipations Innovations”. He got his PhD from Montpellier University (Montpellier, France) and the Carlo Cattaneo University (Castellanza, Italy). He received his French accreditation to supervise research (HDR) from Montpellier University (Montpellier, France).

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