The adoption of Big Data Services by Manufacturing firms: An empirical investigation in India

Abstract

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.

Voice assistants in private households: a conceptual framework for future research in an interdisciplinary field

“…In doing so, we followed the advice in recent research that found the classical, purely content-based literature reviews to be time-consuming, lacking rigor, and prone to be affected by the researchers’ biases (Caputo et al, 2018 ; Verma and Gustafsson, 2020 ). Overall, we can confirm that automating literature research through VOSviewer turned out to be a time-saver regarding the actual search across (partly domain-specific) sources and the collection of scientific literature, and it allowed us to relatively quickly identify meaningful research clusters based on keywords in an enormous body of data (Verma, 2017 ; Van Eck and Waltman, 2014 ). However, we also found that several additional steps were necessary to assuring the quality of the review: Despite the careful selection of keywords, the initial literature list contained several irrelevant articles (i.e., not addressing VA-related topics, yet involving the keywords ‘echo’ and ‘home’).…”

A systematic literature review towards a conceptual framework for enablers and barriers of an enterprise data science strategy

“…The enablers in the form of developing dynamic capability, such as experience in ‘dealing with complexity’, ‘high tolerance for complexity’ (Gong and Janssen 2021 ; Walker and Brown 2019 ) and ‘Top-management-Team’ support (Alaskar et al 2020 ; Behl et al 2019 ; Chaurasia and Verma 2020 ; Foshay et al 2015 ; Halaweh and Massry 2015 ; Lai et al 2018 ; Lamba and Singh 2018 ; Lautenbach et al 2017 ; Popovič et al 2018 ; Ransbotham et al 2017 ; Verma and Bhattacharyya 2017 ; Walker and Brown 2019 ; Wang et al 2018c ) are a must to address the barriers to considerable extent. Organizational environment for an individual in communicating the benefits of ‘Data Science’ (Chakravorty 2020 ; Gong and Janssen 2021 ; Verma 2017 ) is also a barrier for ‘Data Science’ project success. Creating opportunities to interact with leadership team and adopting a deliberate storytelling technique (Boldosova 2019 ) would be helpful in overcoming the communication gap barriers.…”

“…Extant literature has studied ‘Data Science Strategy’ in the contexts of dynamic market places, organizational and dynamic capabilities (Knabke & Olbrich, 2018 ), innovations (Mikalef et al 2018 , 2019a ), and new product development processes (Johnson et al 2017 ). Studies have been conducted in the context of but not limited to healthcare (Chen and Banerjee 2020 ; Kamble et al 2019 ; Kemppainen et al 2019 ; Li et al 2021 ; Newlands et al 2020 ; Ramnath et al 2020 ; Yang et al 2015 ; Wang and Hajli 2017 ), B2B (Hallikainen et al 2020 ), construction (Ahmed et al 2018 ; Ram et al 2019 ; Sang et al 2020 ), supply chain management (Ali et al 2020 ; Arunachalam et al 2018 ; Brinch et al 2018 ; Dubey et al 2019a ; Khan 2019 ; Lai et al 2018 ; Lamba and Singh 2018 ; Mandal 2019 ; Singh and Singh 2019 ; Wang et al 2018c ), manufacturing (Popovič et al 2018 ; Verma 2017 ), consumer goods (Rialti et al 2018 ); e-commerce (Behl et al 2019 ; Wamba et al 2017 ), telecommunications (Saldžiūnas and Skyrius 2017 ; Walker and Brown 2019 ), banking and financial services (Lee et al 2017 ; Lautenbach et al 2017 ; Gregory 2011 ), automotive (Dremel et al 2017 ), and airlines (Holland et al 2020 ).…”

The adoption of Big Data Services by Manufacturing firms: An empirical investigation in India.

“…This is inline with the observations of Whitehouse (2014), Harford (2014), Kaisler et al (2013), Lohr (2012), Işık et al (2013), Brown et al (2011) and . As per Gerhardt et al (2012), business leaders should engage in development of talents in the entire big data ecosystem to earn more benefits from analysing data. According to Demirkan and Delen (2013), the big data services is not yet a viable option for firms because costs for decisionsupport systems are high.…”