Understanding dark side of artificial intelligence (AI) integrated business analytics: assessing firm’s operational inefficiency and competitiveness

Rana, Nripendra P.; Chatterjee, Sheshadri; Dwivedi, Yogesh K.; Akter, Shahriar

doi:10.1080/0960085x.2021.1955628

Cited by 191 publications

(85 citation statements)

References 121 publications

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“…The proposed research model contains higher-order variables ( Ashaari et al, 2021 ) testing by the PLS-SEM ( Bag et al, 2021a ). In addition, PLS-SEM techniques in business performance ( Chatterjee et al, 2021a ; Chaudhuri et al, 2021 ; Pinheiro et al, 2021 ; Shao et al, 2021 ) and artificial intelligence research ( Khalid, 2020 ; Bag et al, 2021b ; Mikalef and Gupta, 2021 ; Rana et al, 2021 ) have been involved for a long time. Ultimately, we determined to use the PLS-SEM analysis in Smart PLS 3 software to test the hypotheses and theoretical models ( Chen and Siau, 2020 ; Hair et al, 2022 ).…”

Section: Methodsmentioning

confidence: 99%

The Impact of Artificial Intelligence on Firm Performance: An Application of the Resource-Based View to e-Commerce Firms

2022

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The application of artificial intelligence (AI) technology has evolved into an influential endeavor to improve firm performance, but little research considers the relationship among artificial intelligence capability (AIC), management (AIM), driven decision making (AIDDM), and firm performance. Based on the resource-based view (RBV) and existing findings, this paper constructs a higher-order model of AIC and suggests a research model of e-commerce firm AIC and firm performance. We collected 394 valid questionnaires and conducted data analysis using partial least squares structural equation modeling (PLS-SEM). As a second-order variable, AIC was formed by three first-order variables: basic, proclivity, and skills. AIC indirectly affects firm performance through creativity, AIM, and AI-driven decision making. Firm creativity, AIM, and AIDDM are essential variables between AIC and firm performance. Innovation culture (IC) positive moderates the relationship between firm creativity and AIDDM as well as the relationship between AIDDM and firm performance. Environmental dynamism (ED) positive mediates the connection between AIM and AIDDM. Among the control variables, firm age negatively affects firm performance, and employee size does not. This study helps enterprises leverage AI to improve firm performance, achieve a competitive advantage, and contribute to theory and management practice.

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Section: Methodsmentioning

confidence: 99%

The Impact of Artificial Intelligence on Firm Performance: An Application of the Resource-Based View to e-Commerce Firms

2022

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“…Traditional machine learning system is a set of algorithms which initially breaks up the problem into several divisions, which are solved separately. After the process learns from those, it recombines the divisions to make the final decision (Halim et al, 2021 ; Rana et al, 2021 ; Rezaei et al, 2021 ; Sreenivasulu, & Chatterjee, 2019 ). On the other hand, deep learning system simultaneously tackles the problem and accurately decides (Heavey & Simsek, 2013 ; Jafari-Sadeghi et al, 2021 ; Schmidhuber, 2015 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Examining the impact of deep learning technology capability on manufacturing firms: moderating roles of technology turbulence and top management support

et al. 2022

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Data science can create value by extracting structured and unstructured data using an appropriate algorithm. Data science operations have undergone drastic changes because of accelerated deep learning progress. Deep learning is an advanced process of machine learning algorithm. Its simple process of presenting data to the system is sharply different from other machine learning processes. Deep learning uses advanced analytics to solve complex problems for accurate business decisions. Deep leaning is considered a promising area for creating additional value in firms’ productivity and sustainability as they develop their smart manufacturing activities. Deep learning capability can help a manufacturing firm’s predictive maintenance, quality control, and anomaly detection. The impact of deep learning technology capability on manufacturing firms is an underexplored area in the literature. With this background, the purpose of this study is to examine the impact of deep learning technology capability on manufacturing firms with moderating roles of deep learning related technology turbulence and top management support of the manufacturing firms. With the help of literature review and theories, a conceptual model has been prepared, which is then validated with the PLS-SEM technique analyzing 473 responses from employees of manufacturing firms. The study shows the significance of deep learning technology capability on smart manufacturing systems. Also, the study highlights the moderating impacts of top management team (TMT) support as well as the moderating impacts of deep learning related technology turbulence on smart manufacturing systems.

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“…In terms of methods, the present quantitative cross-sectional study can be strengthened through longitudinal or qualitative research, adding perception and depth to our understanding of the topic; or through the inclusion of other boundary conditions, like "new product meaningfulness" (Duan et al, 2018) to investigate potential improvements to the predictive power of the model. Also, algorithmic bias and suboptimal decision consequent upon inappropriate business analytics applications could bring in competitive disadvantage to a digitalized organization (Akter et al, 2021;Rana et al, 2021). These issues are needed to be kept in mind by the future researchers for further studies in this context.…”

Section: Limitations and Direction For Future Researchmentioning

confidence: 99%

Adoption of robust business analytics for product innovation and organizational performance: the mediating role of organizational data-driven culture

et al. 2021

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In the present digital environment, a data-driven organizational culture has become a vital emerging driver of organizational growth. This data-driven culture has assumed an advanced shape due to adoption of artificial intelligence (AI) integrated business analytics tools in the organization. Data-driven culture in the organization could considerably impact product innovation strategy as well as organizational process alteration. In this context, the aim of this study is to investigate how an organization's data-driven culture impacts process performance and product innovation that led to enhanced organizational overall performance and higher business value. Methodologically, supported by relevant extant literature and inputs from the resource-based view and dynamic capability theories (organizational context), a conceptual model and a set of hypotheses are initially developed. These are subsequently statistically validated through a survey involving 513 usable responses from employees of different organizations using business analytics tools embedded with AI capability. The findings demonstrate that an organizational data-driven culture has considerable moderating impact on product innovation and process improvement, which ultimately enhance business value through improved organizational overall performance.

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Understanding dark side of artificial intelligence (AI) integrated business analytics: assessing firm’s operational inefficiency and competitiveness

Cited by 191 publications

References 121 publications

The Impact of Artificial Intelligence on Firm Performance: An Application of the Resource-Based View to e-Commerce Firms

The Impact of Artificial Intelligence on Firm Performance: An Application of the Resource-Based View to e-Commerce Firms

Examining the impact of deep learning technology capability on manufacturing firms: moderating roles of technology turbulence and top management support

Adoption of robust business analytics for product innovation and organizational performance: the mediating role of organizational data-driven culture

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