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 util… Show more
“…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’).…”
The present study identifies, organizes, and structures the available scientific knowledge on the recent use and the prospects of Voice Assistants (VA) in private households. The systematic review of the 207 articles from the Computer, Social, and Business and Management research domains combines bibliometric with qualitative content analysis. The study contributes to earlier research by consolidating the as yet dispersed insights from scholarly research, and by conceptualizing linkages between research domains around common themes. We find that, despite advances in the technological development of VA, research largely lacks cross-fertilization between findings from the Social and Business and Management Sciences. This is needed for developing and monetizing meaningful VA use cases and solutions that match the needs of private households. Few articles show that future research is well-advised to make interdisciplinary efforts to create a common understanding from complementary findings—e.g., what necessary social, legal, functional, and technological extensions could integrate social, behavioral, and business aspects with technological development. We identify future VA-based business opportunities and propose integrated future research avenues for aligning the different disciplines’ scholarly efforts.
“…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’).…”
The present study identifies, organizes, and structures the available scientific knowledge on the recent use and the prospects of Voice Assistants (VA) in private households. The systematic review of the 207 articles from the Computer, Social, and Business and Management research domains combines bibliometric with qualitative content analysis. The study contributes to earlier research by consolidating the as yet dispersed insights from scholarly research, and by conceptualizing linkages between research domains around common themes. We find that, despite advances in the technological development of VA, research largely lacks cross-fertilization between findings from the Social and Business and Management Sciences. This is needed for developing and monetizing meaningful VA use cases and solutions that match the needs of private households. Few articles show that future research is well-advised to make interdisciplinary efforts to create a common understanding from complementary findings—e.g., what necessary social, legal, functional, and technological extensions could integrate social, behavioral, and business aspects with technological development. We identify future VA-based business opportunities and propose integrated future research avenues for aligning the different disciplines’ scholarly efforts.
“…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.…”
Section: Resultsmentioning
confidence: 99%
“…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 ).…”
While embracing digitalization that is further accentuated by the Covid-19 pandemic, the real business outcome is achieved through a robust and well-crafted ‘Data Science Strategy’ (DSS), as significant constituent of Enterprise Digital Strategy. Extant literature has studied the challenges in adoption of components of ‘Data Science’ in discrete for various industry sectors and domains. There is dearth of studies on comprehensive ‘Data Science’ adoption as an umbrella constituting all of its components. The study conducts a “Systematic Literature Review (SLR)” on enablers and barriers affecting the implementation and success of DSS in enterprises. The SLR comprised of 113 published articles during the period 1998 and 2021. In this SLR, we address the gap by synthesizing and proposing a novel framework of ‘Enablers and Barriers’ influencing the success of DSS in enterprises. The proposed framework of ‘Data Science Strategy’ can help organizations taking the right steps towards successful implementation of ‘Data Science’ projects.
Supplementary Information
The online version contains supplementary material available at 10.1007/s10257-022-00550-x.
“…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.…”
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.
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