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

Justice at the Forefront: Cultivating felt accountability towards Artificial Intelligence among healthcare professionals

The advent of AI has ushered in a new era of patient care, but with it emerges a contentious debate surrounding accountability for algorithmic medical decisions. Within this discourse, a spectrum of views prevails, ranging from placing accountability on AI solution providers to laying it squarely on the shoulders of healthcare professionals. In response to this debate, this study, grounded in the mutualistic partner choice (MPC) model of the evolution of morality, seeks to establish a configurational framework for cultivating felt accountability towards AI among healthcare professionals. This framework underscores two pivotal conditions: AI ethics enactment and trusting belief in AI and considers the influence of organizational complexity in the implementation of this framework. Drawing on Fuzzy-set Qualitative Comparative Analysis (fsQCA) of a sample of 401 healthcare professionals, this study reveals that a) focusing justice and autonomy in AI ethics enactment along with building trusting belief in AI reliability and functionality reinforces healthcare professionals' sense of felt accountability towards AI, b) fostering felt accountability towards AI necessitates ensuring the establishment of trust in its functionality for high complexity hospitals, and c) prioritizing justice in AI ethics enactment and trust in AI reliability is essential for low complexity hospitals.

Influencing mechanism of the intellectual capability of big data analytics on the operational performance of enterprises

In the era of big data, data processing capability is key to gaining a competitive advantage for businesses. With appropriate technical and organizational resources in place, enterprises can extract considerable value from the vast amount of available data, thereby increasing their competitive advantage. Therefore, to utilize big data resources effectively, enterprises should focus on improving the intellectual abilities of big data analysts. Big data analytics intellectual capability (BDAIC) refers to the specialized skills and knowledge that employees of the enterprise possess, including technical, technical management, business, and relational knowledge, that would enable them to use analytics tools to accomplish organizational tasks and shape the core competitiveness of an enterprise. This study constructs a theoretical model that focuses on the mediating role of person-tool fit and examines the mechanisms by which BDAIC affects an enterprise's operational performance. The results show that BDAIC, which contains four basic categories of knowledge, positively influences an enterprise's operational efficiency. Additionally, person-tool matching mediates BDAIC's effect on an enterprise's operational performance. These findings explore the latest avenues of exploration in the research paradigm of big data analytics. Furthermore, this study has important implications for practitioners trying to use big data to improve business performance.

Digitalization and artificial knowledge for accountability in SCM: a systematic literature review

Purpose

In this study, the authors examine artificial knowledge as a fundamental stream of knowledge management for sustainable and resilient business models in supply chain management (SCM). The study aims to provide a comprehensive overview of artificial knowledge and digitalization as key enablers of the improvement of SCM accountability and sustainable performance towards the UN 2030 Agenda.

Design/methodology/approach

Using the SCOPUS database and Google Scholar, the authors analyzed 135 English-language publications from 1990 to 2022 to chart the pattern of knowledge production and dissemination in the literature. The data were collected, reviewed and peer-reviewed before conducting bibliometric analysis and a systematic literature review to support future research agenda.

Findings

The results highlight that artificial knowledge and digitalization are linked to the UN 2030 Agenda. The analysis further identifies the main issues in achieving sustainable and resilient SCM business models. Based on the results, the authors develop a conceptual framework for artificial knowledge and digitalization in SCM to increase accountability and sustainable performance, especially in times of sudden crises when business resilience is imperative.

Research limitations/implications

The study results add to the extant literature by examining artificial knowledge and digitalization from the resilience theory perspective. The authors suggest that different strategic perspectives significantly promote resilience for SCM digitization and sustainable development. Notably, fostering diverse peer exchange relationships can help stimulate peer knowledge and act as a palliative mechanism that builds digital knowledge to strengthen and drive future possibilities.

Practical implications

This research offers valuable guidance to supply chain practitioners, managers and policymakers in re-thinking, re-formulating and re-shaping organizational processes to meet the UN 2030 Agenda, mainly by introducing artificial knowledge in digital transformation training and education programs. In doing so, firms should focus not simply on digital transformation but also on cultural transformation to enhance SCM accountability and sustainable performance in resilient business models.

Originality/value

This study is, to the authors' best knowledge, among the first to conceptualize artificial knowledge and digitalization issues in SCM. It further integrates resilience theory with institutional theory, legitimacy theory and stakeholder theory as the theoretical foundations of artificial knowledge in SCM, based on firms' responsibility to fulfill the sustainable development goals under the UN's 2030 Agenda.

Effect of business intelligence on operational performance: the mediating role of supply chain ambidexterity

Purpose

This study aims to examined the impact of business intelligence (BI) and supply chain ambidexterity (SCA) on operational performance (OP), contributing to dwarf knowledge in small- and medium-sized enterprises (SMEs) in the context of emerging economies. The mediating role of SCA was considered in the proposed model.

Design/methodology/approach

The study used the quantitative method to investigate the interdependencies between variables. As a result, 216 senior and middle managers/owners of SMEs in Ghana were surveyed using a purposive and convenient sampling method. SPSS version 23 and Smart PLS version 3 were used to conduct the research.

Findings

While the direct link among BI, SCA and OP was confirmed. The outcome also showed that SCA plays a significant mediating role between BI and OP among SMEs.

Practical implications

The outcome of the study indicates that SCA encourages the use of BI to generate superior OP among SMEs. This knowledge will improve the performance of SMEs and their ability to withstand the competition in the global market.

Originality/value

With the discovery of this study, the theory of a resource-based view now has some empirical evidence behind it. As a result, SMEs prioritize aspects that could improve their operations and implement tactics that would nurture better performance and competitive advantages.

Customer experiences in the era of artificial intelligence (AI) in context to FinTech: a fuzzy AHP approach

Purpose

The financial technology (FinTech) era has brought a revolutionary change in the financial sector’s customer experiences at the national and global levels. The importance of artificial intelligence (AI) in the context of FinTech services for enriching customer experiences has become a new norm in this modern era of technological advancement. So, it becomes crucial to understand the customer’s perspective. The current research ranks the factors and sub-factors influencing customers’ perceptions of AI-based FinTech services.

Design/methodology/approach

The sample size for this study was decided to be 970 respondents from four Indian cities: Mumbai, Delhi, Kolkata and Chennai. The Fuzzy-AHP technique was used to identify the primary factors and sub-factors influencing customers’ experiences with AI-enabled finance services. The factors considered in the study were service quality, trust commitment, personalization, perceived convenience, relationship commitment, perceived sacrifice, subjective norms, perceived usefulness, attitude and vulnerability. The current research is both empirical and descriptive.

Findings

The study’s three top factors are service quality, perceived usefulness and perceived convenience, all of which have a significant impact on customers’ experience with AI-enabled FinTech services discussing sub-criteria three primary criteria for customers’ experience for FinTech services include: “Using FinTech would increase my effectiveness in managing a portfolio (A2)”, “My peer groups and friends have an impact on using FinTech services (SN3)” and “Using FinTech would increase my efficacy in administering portfolio (PU2)”.

Research limitations/implications

The current study is limited to four Indian cities, with 10 factors to understand customers’ preferences in FinTech. Further research can focus on other dimensions like perceived ease of use, familiarity, etc. Future studies can have a broader view of different geographical locations and consider new tech to understand customer perceptions better.

Practical implications

The study’s findings will significantly assist businesses in determining the primary aspects influencing customers’ experiences with AI-enabled financial services. As a result, they will develop strategies and policies to entice clients to use AI-powered FinTech services.

Originality/value

Existing AI research investigated several vital topics in the context of FinTech services. On the other hand, the current study ranked the criteria in understanding customer experiences. The research will substantially assist marketers, business houses, academicians and practitioners in understanding essential facets influencing customer experience and contribute significantly to the literature.

Dependable modulation classifier explainer with measurable explainability

The Internet of Things (IoT) plays a significant role in building smart cities worldwide. Smart cities use IoT devices to collect and analyze data to provide better services and solutions. These IoT devices are heavily dependent on the network for communication. These new-age networks use artificial intelligence (AI) that plays a crucial role in reducing network roll-out and operation costs, improving entire system performance, enhancing customer services, and generating possibilities to embed a wide range of telecom services and applications. For IoT devices, it is essential to have a robust and trustable network for reliable communication among devices and service points. The signals sent between the devices or service points use modulation to send a password over a bandpass frequency range. Our study focuses on modulation classification performed using deep learning method(s), adaptive modulation classification (AMC), which has now become an integral part of a communication system. We propose a dependable modulation classifier explainer (DMCE) that focuses on the explainability of modulation classification. Our study demonstrates how we can visualize and understand a particular prediction made by seeing highlighted data points crucial for modulation class prediction. We also demonstrate a numeric explainability measurable metric (EMM) to interpret the prediction. In the end, we present a comparative analysis with existing state-of-the-art methods.

Role of fake news and misinformation in supply chain disruption: impact of technology competency as moderator

Studies show that COVID-19 has increased the effects of misinformation and fake news that proliferated during the continued crisis and related turbulent environment. Fake news and misinformation can come from various sources such as social media, print media, as well as from electronic media such as instant messaging services and other apps. There is a growing interest among researchers and practitioners on how fake news and misinformation impacts on supply chain disruption. But the limited research in this area leaves a gap. With this background, the purpose of this study is to determine the role of fake news and misinformation in supply chain disruption and the consequences to a firm's operational performance. This study also investigates the moderating role of technology competency in supply chain disruption and operational performance of the firm. With the help of theories and literature, a theoretical model has been developed. Later, the conceptual model has been validated using partial least squares structural equation modeling. The study finds that there is a significant impact of misinformation and fake news on supply chain disruption, which in turn negatively impacts firms' operational performance. The study also highlights that firms' technology competency can improve the supply chain situation that has been disrupted by misinformation and fake news.

Examining the role of gender on family business entrepreneurial intention: influence of government support and technology usage

Purpose

The purpose of this paper is to examine the influence of government support and technology usage on family business entrepreneurial intention. The paper also investigates the moderating impacts of gender on the family business entrepreneurial intention with its two predictors.

Design/methodology/approach

This paper has used both resource-based view and dynamic capability view theory as well as literature on family business entrepreneurship to develop the theoretical model. Later, the theoretical model has been validated using structural equation modelling (CB-SEM) with respondents from Indian family business enterprises. This study has used a purposeful and convenience sampling approach.

Findings

This study has shown the significance of technology usage as well as government support to improve the family business enterprise. The study highlights that there is a moderating impact of gender on the relationship between government support and technology usage with entrepreneurial intention in family business.

Research limitations/implications

This study adds value towards body of literature in entrepreneurship, gender, and business, as well as family business literature. The study shows how gender acts as a moderator in case of family business entrepreneurship. The study is cross sectional in nature and has limited number of respondents from India. Thus, the findings cannot be generalizable.

Originality/value

This study is a unique study as it investigates the influence of both government support as well as technology usage by the family business firms for entrepreneurial intention. The proposed theoretical model has a high predictive power which makes the model effective. Moreover, this study also examines the moderating impacts of gender on entrepreneurial intention in the family business which adds value to the existing body of knowledge.

Adoption of Digital Technologies by SMEs for Sustainability and Value Creation: Moderating Role of Entrepreneurial Orientation

Digital business transformation is considered an effective business strategy that appears to have gained attention since the enterprises are challenged to continuously improve their business practices as well as capabilities. The use of digital technologies could reduce the influence of external crises and could introduce massive changes in business operations by providing better business models. Moreover, adopting digital technology can influence both economic sustainability and social value of enterprises and can improve regional socio-economic conditions. There are few recent studies on how technology can empower enterprises at different phases of growth and sustainability; furthermore, very few studies are available that determine how adopting different modern digital technologies can create value for small and medium enterprises (SMEs). Therefore, this study aims to close this gap and investigate the moderating role of entrepreneurial orientation. With the support of resource-based view (RBV) and dynamic capability view (DCV) theories, along with a literature review, a theoretical model has been developed. It was then validated using the PLS-SEM technique considering 319 respondents who are SME employees in India. The findings show that adopting digital technologies has a significant impact on the creation of economic sustainability and social value for SMEs. The study also found a significant moderating impact of entrepreneurial orientation on the relationship between social and economic value creation and SME performance.

Adoption of AI-Enabled Tools in Social Development Organizations in India: An Extension of UTAUT Model

Social development organizations increasingly employ artificial intelligence (AI)-enabled tools to help team members collaborate effectively and efficiently. These tools are used in various team management tasks and activities. Based on the unified theory of acceptance and use of technology (UTAUT), this study explores various factors influencing employees' use of AI-enabled tools. The study extends the model in two ways: a) by evaluating the impact of these tools on the employees' collaboration and b) by exploring the moderating role of AI aversion. Data were collected through an online survey of employees working with AI-enabled tools. The analysis of the research model was conducted using partial least squares (PLS), with a two-step model - measurement and structural models of assessment. The results revealed that the antecedent variables, such as effort expectancy, performance expectancy, social influence, and facilitating conditions, are positively associated with using AI-enabled tools, which have a positive relationship with collaboration. It also concluded a significant effect of AI aversion in the relationship between performance expectancy and use of technology. These findings imply that organizations should focus on building an environment to adopt AI-enabled tools while also addressing employees' concerns about AI.

Investigating the impacts of microlevel CSR activities on firm sustainability: mediating role of CSR performance and moderating role of top management support

Purpose

The purpose of this study is to investigate the impacts of microlevel corporate social responsibility (CSR) activities on firm sustainability. The study also examines the mediating roles of CSR performance (CSRP) and top management support on firm sustainability.

Design/methodology/approach

With the help of existing literature and theories, a theoretical model has been developed, which is then validated using structural equation modeling technique considering 327 respondents who perform CSR activities at respondents' firms in India. The research methods include moderator analysis to understand the role of top management support for firm sustainability.

Findings

This research found that microlevel CSR activities have a significant positive impact on CSR performance. The study also found that there is a significant moderating impact of top management support on the relationship between CSR performance and firm sustainability.

Research limitations/implications

This study proposed a theoretical model which has established the relationships between the microlevel CSR activities and CSRP along with firm sustainability. This is an effective model and provides vital inputs to the firms on how to succeed with CSR activities. Limitations to this study's generalizability include use of cross-sectional data and that the data were collected from one country.

Originality/value

The proposed theoretical model is unique and can be applied by firms to enhance firms' CSR performance. There is no other study which has investigated the moderating role of firm leadership team impacting the relationship between CSR performance and firm sustainability. Thus, this study is a unique attempt and adds value to the extant literature on CSR as well as firm sustainability.

The future is yesterday: Use of AI-driven facial recognition to enhance value in the travel and tourism industry

This study aims to investigate the role of artificial intelligence (AI) driven facial recognition to enhance a value proposition by influencing different areas of services in the travel and tourism industry. We adopted semi-structured interviews to derive insights from 26 respondents. Thematic analysis reveals the development of four main themes (personalization, data-driven service offering, security and safety, and seamless payments). Further, we mapped the impact of AI- driven facial recognition to enhance value and experience for corporate guests. Findings indicate that AI-based facial recognition can facilitate the travel and tourism industry in understanding travelers' needs, optimization of service offers, and value-based services, whereas data-driven services can be realized in the form of customized trip planning, email, and calendar integration, and quick bill summarization. This contributes to strengthening the tourism literature through the lens of organizational information processing theory.

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

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.

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

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.

Facilitating the Creativity of Governmental Employees via High-Involvement Human Resource Management Practices

The study examines some of the facilitators of governmental employees’ creativity by developing and testing a model linking perceived high-involvement human resource practices (HIHRPs) to an employee’s creativity (EC), via the mediating mechanism of an employee’s felt trust (FT). Online questionnaires were used to collect data from 241 staff members at a main governmental university in Jordan. Structural-equation modeling (SEM) via AMOS and the PROCESS macro in SPSS were used for data analysis. The study found: 1-HIHRPs as a bundle had a positive significant influence on EC, however, as discrete practices, only empowerment and competence development had significant relations with EC. 2-HIHRPs (as a bundle and as discrete practices) had positive significant influences on FT. 3-FT was positively and significantly related to EC. 4-FT mediated the relations between HIHRPs (as a bundle and as discrete practices) and EC.