Conceptualization, operationalization, and validation of the digital data stream Readiness Index

This article describes how in their search for value creation, companies are investing considerable resources in so-called "Big Data" initiatives. A peculiar aspect of these initiatives is the increasing availability of real-time streams of data. Successfully leveraging these streams to extract value is emerging as a critical competence for the modern firm. Despite the significant attention received, scholarly research on Digital Data Stream (DDS) remains insufficient. More importantly, there are no specialized definitions and measurement instruments that can move the field forward by initiating a cumulative research tradition. This article can provide clarification on key definitions, differentiating DDS from Big Data. Drawing on the organizational readiness concept, the DDS readiness index develops as a measure of organizational readiness to exploit real-time digital data. This article will conceptualize, define, operationalize and validate the index. By identifying the four dimensions of mindset, skillset, dataset and toolset as the elements of the DDS readiness index and discussing its managerial and research implications

Understanding adoption of big data analytics in China: from organizational users perspective

Big data is a recent technology employed by companies to gain a competitive advantage. The investment of big data technologies in the USA was estimated at more than 30 billion USD in 2016. However, the investment of big data technologies in China was relatively small in 2016. Grounded in the Technology-Organization-Environment (TOE) framework, this study identifies the main factors affecting the organizational adoption of big data in China. The results can provide useful indicators for industries to utilize big data for a more productive business

THE ADOPTION OF BIG DATA SERVICES BY MANUFACTURING FIRMS: AN EMPIRICAL INVESTIGATION IN INDIA

Although some leading companies are actively adopting Big data services (BDS) to strengthen market competition , many manufacturing firms are still in the early stage of the adoption curve due to lack of understanding of and experience with BDS. Hence, it is interesting and timely to understand issues relevant to BDS adoption. The empirical investigation reveals that a firm’s intention to adopt BDS can be positively affected by the quality and benefits of BDS. Surprisingly, a firm’s absorptive capacity in utilizing big data and risks and costs associated with implementation and maintenance does not impact the adoption intention of BDS

Understanding Organizations’ Artificial Intelligence Journey: A Qualitative Approach

Background: With growth in Artificial Intelligence (AI) adoption, challenges and hurdles are also becoming evident. Organizations implementing AI are challenged to find ways to leverage AI to produce optimum results and benefits for the organization. Understanding other organizations’ AI implementation journeys will help them start and implement AI. By understanding the different facets of AI implementation, they can strategize AI to gain business value. Though several studies have examined AI adoption, there are few studies on how firms implement it. We close this gap by studying AI adoption and implementations in various firms. Method: Using a qualitative approach of semi-structured interviews, we studied twenty global organizations of various sizes that have implemented AI. Results: The study categorizes the results into four major themes – facilitators, barriers, trends, and strategies for implementing AI. Our study reinforces the relevance of the TOE framework and Roger’s DOI theory in studying AI adoption. Organizational factors such as top management support, strategic roadmap, availability of skilled resources, and corporate culture influenced AI adoption. Their lack of data or poor data quality is a primary challenge. The privacy laws concerning data, as well as regulatory bottlenecks, further exacerbate this problem. We also identified and mapped the standard AI implementations to their AI technologies. We found that most of them exploit AI’s image and natural language processing capabilities to automate their processes. Regarding implementation, firms work with partners to obtain customer data and use federated learning. Conclusion: Understanding firms’ AI implementation journey will help us promote further adoption and experimentation. Organizations can identify areas where they can leverage AI to enhance value, prepare themselves for the future, start and proceed with AI implementation efforts and overcome barriers they might encounter

The role of the social and technical factors in creating business value from big data analytics: A meta-analysis

Big data analytics (BDA) has recently gained importance as an emerging technology for handling big data. The use of advanced techniques with differing levels of intelligence, such as descriptive, predictive, prescriptive, and autonomous analytics, is expected to create value for firms. By viewing BDA as a sociotechnical system, we conduct a meta-analysis of 107 individual studies to integrate prior evidence on the role of the technical and social factors of BDA in creating BDA business value. The findings underline the predominant role of the social components in enhancing firm performance, such as the BDA system’s human factors and a nurturing organizational structure, in contrast to the minor role of the technological factors. However, both the technical and social factors are found to be strong determinants of BDA business value. Through the combined lens of sociotechnical theory and the IS business value framework, we contribute to research and practice by enhancing the understanding of the main technical and social determinants of BDA business value at the firm level

Industry 4.0 and Business Policy Development: Strategic Imperatives for SME Performance

Industry 4.0 presents companies with new prospects to renovate industrial manufacturing processes and increase value creation, has promised several optimizing strategies for improved business performance. The purpose of this research is to examine the relationship between innovation capability and employee capability on organizational performance among Small and Medium Scale industries entrepreneurs. Following a positivist research philosophy with a quantitative, cross-sectional descriptive study design, the study addressed three direct and two indirect relationships in the model. The research followed the expectation Resource-Based View Theory to test the theoretical model. Following stratified random sampling, this research using 384 SME entrepreneurs from the Selangor state of Malaysia. The study applied Smart PLS-SEM to analyze the data. The results show that SME firms' innovation capability and employee capability positively correlate with business performance. The study also shows the partial mediation effect of technology change on innovation capability and business performance and employee capability and business performance. Research extends practical and theoretical implications to the stakeholders of SMEs and businesses.JEL Classification: L25, L26, L29How to Cite:Govindarajo, N. S., Kumar M, D., Shaikh, E., Kumar, M., & Kumar, P. (2021). Industry 4.0 and Business Policy Development: Strategic Imperatives for SME Performance. Etikonomi, 20(2), 213 – xx. https://doi.org/10.15408/etk.v20i2.20143

Drivers of Big Data Analytics’ Adoption and Implications of Management Decision-Making on Big Data Adoption and Firms’ Financial and Non-Financial Performance: Evidence from Nigeria’s Manufacturing and Service Industries

Despite advances in Big Data Analytics, its utilitarian discourse is yet to move beyond early capture to focus on its post adoption impacts on firms’ financial and non-financial performance, especially in Nigeria’s manufacturing and service industries. This study advances BDA beyond organizational readiness for change by empirically and analytically focusing on the reality of 261 Nigerian professionals by drawing on business-to-business marketing, dynamic capabilities, and Technology-Organization-Environment theoretical frameworks to contribute a conceptual model (Figure 1) on factors which really impact on organizations' readiness to adopt BDA. Consequently, our study’s findings were used to develop Figure 2, showing the direct and moderating nature of interactions between BDA and TOE variables on BDA adoption. However, whereas hypotheses three and four confirm top management’s support and overall organizational readiness, paradoxically, this study’s hypotheses five and seven contribute to existing BDA discourse by highlighting that environmental, competitive pressure, including regulation do not support the adoption of BDA. Additionally, while external support (H6) was found conducive for BDA adoption, interestingly, hypotheses eight, nine and 10a were also found supportive of not only financial but also non-financial performance. However, contrary to current theorisation, hypotheses 10b was not supportive of non-financial performance. Our results contribute to BDA’s business competitiveness and regulation

Contemporary Research on Business and Management

This book contains selected papers presented at the 4th International Seminar of Contemporary Research on Business and Management (ISCRBM 2020), which was organized by the Alliance of Indonesian Master of Management Program (APMMI) and held in Surubaya, Indonesia, 25-27 November 2020. It was hosted by the Master of Management Program Indonesia University and co-hosts Airlangga University, Sriwijaya University, Trunojoyo University of Madura, and Telkom University, and supported by Telkom Indonesia and Triputra. The seminar aimed to provide a forum for leading scholars, academics, researchers, and practitioners in business and management area to reflect on current issues, challenges and opportunities, and to share the latest innovative research and best practice. This seminar brought together participants to exchange ideas on the future development of management disciplines: human resources, marketing, operations, finance, strategic management and entrepreneurship

From Dataveillance to Data Economy: Firm View on Data Protection

The increasing availability of electronic records and the expanded reliance on online communications and services have made available a huge amount of data about people’s behaviours, characteristics, and preferences. Advancements in data processing technology, known as big data, offer opportunities to increase organisational efficiency and competitiveness. Analytically sophisticated companies excel in their ability to extract value from the analysis of digital data. However, in order to exploit the potential economic benefits produced by big data and analytics, issues of data privacy and information security need to be addressed. In Europe, organisations processing personal data are being required to implement basic data protection principles, which are considered difficult to implement in big data environments. Little is known in the privacy studies literature about how companies manage the trade-off between data usage and data protection. This study contributes to explore the corporate data privacy environment, by focusing on the interrelationship between the data protection legal regime, the application of big data analytics to achieve corporate objectives, and the creation of an organisational privacy culture. It also draws insights from surveillance studies, particularly the idea of dataveillance, to identify potential limitations of the current legal privacy regime. The findings from the analysis of survey data show that big data and data protection support each other, but also that some frictions can emerge around data collection and data fusion. The demand for the integration of different data sources poses challenges to the implementation of data protection principles. However, this study finds no evidence that data protection laws prevent data gathering. Implications relevant for the debate on the reform of European data protection law are also drawn from these findings

Time is money: foster engagement through online compensation and gamification mechanisms

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