Perceived strategic value-based adoption of Big Data Analytics in emerging economy

Verma, Surabhi; Bhattacharyya, Som Sekhar

doi:10.1108/jeim-10-2015-0099

Cited by 143 publications

(163 citation statements)

References 81 publications

(172 reference statements)

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“…With regard to the strategic orientation, our results are consistent with those of Li et al (2018) and Verma and Bhattacharyya (2017)'s findings that the major reason for the non-adoption of BDA was that companies did not realise the strategic value of BDA, and they were not ready to make changes due to technological, organisational and environmental difficulties. Therefore, we conclude that a company's strategic orientation and structure may also be important influential factors concerning the use of BDA.…”

Section: Comparison and Discussion Of The Resultssupporting

confidence: 89%

“…While prior studies (for example, Li et al, 2018) have focused on internal auditing, this paper addresses BDA and external auditors in particular. In addition, Verma and Bhattacharyya (2017) found that complexity and perceived costs were the inhibitors that prevented the adoption of BDA in business companies, while our research results indicated that the factors mentioned above were not critical. Secondly, our study examines the phenomenon of Big Data and BDA in the context of auditing.…”

Section: Contributionmentioning

confidence: 52%

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Motivation to use big data and big data analytics in external auditing

Dagilienė

Klovienė

2019

MAJ

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Purpose This paper aims to explore organisational intentions to use Big Data and Big Data Analytics (BDA) in external auditing. This study conceptualises different contingent motivating factors based on prior literature and the views of auditors, business clients and regulators regarding the external auditing practices and BDA. Design/methodology/approach Using the contingency theory approach, a literature review and 21 in-depth interviews with three different types of respondents, the authors explore factors motivating the use of BDA in external auditing. Findings The study presents a few key findings regarding the use of BD and BDA in external auditing. By disclosing a comprehensive view of current practices, the authors identify two groups of motivating factors (company-related and institutional) and the circumstances in which to use BDA, which will lead to the desired outcomes of audit companies. In addition, the authors emphasise the relationship of audit companies, business clients and regulators. The research indicates a trend whereby external auditors are likely to focus on the procedures not only to satisfy regulatory requirements but also to provide more value for business clients; hence, BDA may be one of the solutions. Research limitations/implications The conclusions of this study are based on interview data collected from 21 participants. There is a limited number of large companies in Lithuania that are open to co-operation. Future studies may investigate the issues addressed in this study further by using different research sites and a broader range of data. Practical implications Current practices and outcomes of using BD and BDA by different types of respondents differ significantly. The authors wish to emphasise the need for audit companies to implement a BD-driven approach and to customise their audit strategy to gain long-term efficiency. Furthermore, the most challenging factors for using BDA emerged, namely, long-term audit agreements and the business clients’ sizes, structures and information systems. Originality/value The original contribution of this study lies in the empirical investigation of the comprehensive state-of-the-art of BDA usage and motivating factors in external auditing. Moreover, the study examines the phenomenon of BD as one of the most recent and praised developments in the external auditing context. Finally, a contingency-based theoretical framework has been proposed. In addition, the research also makes a methodological contribution by using the approach of constructivist grounded theory for the analysis of qualitative data.

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Section: Comparison and Discussion Of The Resultssupporting

confidence: 89%

Section: Contributionmentioning

confidence: 52%

Motivation to use big data and big data analytics in external auditing

Dagilienė

Klovienė

2019

MAJ

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“…Zhu et al (2004) for example studied e-business adoption in the finance industry applying the TOE framework in order to structure survey data from 612 firms in 10 countries [31]. Another more recent study analyzes the value-based adoption of big data analytics using the TOE framework, which is a subject area closely related to the field of AI [30]. The study outlines the main reasons for non-adoption of big data analysis, using a similar approach as our investigation.…”

Section: The Technology-organization-environment Framework (Toe)mentioning

confidence: 95%

Artificial Intelligence for the Financial Services Industry: What Challenges Organizations to Succeed

Kruse¹,

Wunderlich²,

Beck³

2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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As a research field, artificial intelligence (AI) exists for several years. More recently, technological breakthroughs, coupled with the fast availability of data, have brought AI closer to commercial use. Internet giants such as Google, Amazon, Apple or Facebook invest significantly into AI, thereby underlining its relevance for business models worldwide. For the highly data driven finance industry, AI is of intensive interest within pilot projects, still, few AI applications have been implemented so far. This study analyzes drivers and inhibitors of a successful AI adoption in the finance industry based on panel data comprising 22 semi-structured interviews with experts in AI in finance. As theoretical lens, we structured our results using the TOE framework. Guidelines for applying AI successfully reveal AI-specific role models and process competencies as crucial, before trained algorithms will have reached a quality level on which AI applications will operate without human intervention and moral concerns.

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“…Stepwise research design has been widely used in the investigation of social sciences [60][61][62], as well as in addressing marketing and organizational [63,64] aspects. Following the protocol developed in previous studies that addressed the subject topic [17,63,64], five stages of research were identified:…”

Section: Methodsmentioning

confidence: 99%

Market-Oriented Sustainability of Sjenica Sheep Cheese

Filipović

2019

Sustainability

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This study investigated the nature and level of market-oriented sustainability of Sjenica sheep cheese, a Serbian Protected Designation of Origin (PDO)-registered product. In the analysis of the supply chain market sustainability, three theories were used: Resource dependence theory, upper echelons theory, and signaling theory. The data were collected using a stepwise research design, applying techniques of observation and in-depth and telephone interviewing. The results show that the low recognition of the PDO label among Serbian consumers and all participants in the supply chain and the lack of the presence in the markets and large packages negatively impact domestic consumers in their decisions to purchase higher amounts of Sjenica sheep cheese. Small producers lack bargaining power, whereas their perceptions hinder formation of inter-organizational arrangements. The insufficient education of big producers and distributors impede the achievement of better business results. The main challenges for the export of Sjenica sheep cheese are the inadequate certification of this food product for the European Union market, the lack of standardized production process that results in variations in the product quality, and the unawareness of the regional consumers of Sjenica sheep cheese due to insufficient communication efforts.

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Perceived strategic value-based adoption of Big Data Analytics in emerging economy

Cited by 143 publications

References 81 publications

Motivation to use big data and big data analytics in external auditing

Motivation to use big data and big data analytics in external auditing

Artificial Intelligence for the Financial Services Industry: What Challenges Organizations to Succeed

Market-Oriented Sustainability of Sjenica Sheep Cheese

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