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The key factor for the success of total quality management programs in an organization is focusing on the customer. The purpose of this paper is to assess customer focus level following implementation of a quality improvement model in social security hospitals in Tehran Province.

This research was descriptive‐comparative in nature. The study population consisted of the implementers of quality improvement model in four Tehran social security hospitals. The data were gathered through a checklist addressing customer knowledge and customer satisfaction.

The research findings indicated that the average scores on customer knowledge in Shahriar, Alborz, Milad, and Varamin hospitals were 64.1, 61.2, 54.1, and 46.6, respectively. The average scores on customer satisfaction in Shahriar, Alborz, Milad, and Varamin hospitals were 67.7, 65, 59.4, and 50, respectively. The customer focus average scores in Shahriar, Alborz, Milad, and Varamin hospitals were 66.3, 63.3, 57.3, and 48.6, respectively. The total average scores on customer knowledge, satisfaction and customer focus in the investigated hospitals proved to be 56.4, 60.5, and 58.9, respectively.

The paper is of value in showing that implementation of the quality improvement model could considerably improve customer focus level.

This research aims to examine how decision-makers’ demographic traits affect the integration of foreign workforce into Tirolean (Austria) companies. With continuous world migrations, Tirol experiences a great inflow of foreign workforce. While integrating into the workforce, the foreign workers interact with various decision-makers whose demographic traits (e.g., age, gender, nationality) potentially influence the success of the integration process. To gather data on the integration levels of a foreign workforce, the author conducted a questionnaire. Furthermore, several statistical analyses were run to determine if the relationship between demographic characteristics and integration success exists. The study reveals that demographic characteristics influence decision-makers’ acceptance of expatriates, as well as their recruitment, integration, and training and development outcomes. The empirical results indicate the strength of relationships identified through analyses. The study is limited to geographical, as well as the scope of the sample size, as the data are obtained from Tirol only. In addition, the results from the study serve as a basis for future discussions and research.

This chapter explores the role of artificial intelligence (AI), particularly its subfield of machine learning (ML) methods, as a core technology of the fintech revolution in the financial services industry. It simplifies some of the complex concepts related to AI by introducing the main ML paradigms and related techno-methodic aspects. This chapter uses real-world examples to illustrate how next-generation AI powered by ML is transforming the financial services industry. Next, in illustrating the risks associated with AI adoption, this chapter discusses the need for regulation to address the essential facets of AI governance, including transparency, accountability, ethics, and responsible use. Lastly, it looks at emerging regulatory approaches across leading global jurisdictions. The primary goal is to give readers an initial understanding of AI's profound impact on the financial sector.

The purpose of this paper is to explore parallels between scientific management and the new scientific management to gain insight into applications of machine learning and artificial intelligence (AI) to human resource management and employee assessment.

Analysis of Taylor’s work and its interpretation by scholars is contrasted with modern analysis of human resource analytics to demonstrate conceptual and methodological commonalities between the old and the new forms of scientific management.

The analysis demonstrates how the epistemology, ethos and cultural trajectory of scientific management has resulted in a mindset that has influenced the implementation and objectives of the new scientific management with respect to human resources analytics.

This paper offers an alternative to the view that machine learning and AI as applied to work and employees are beneficial and points out why important challenges have been overlooked and how they can be addressed.

Commonalties between Taylorism and the new scientific management have been overlooked so that attempts to gain an understanding of how machine learning is likely to influence work, employees and work organizations are incomplete. This paper provides a new perspective that can be used to address challenges associated with applications of machine learning to work design and employee rights.

In spite of the merits of artificial intelligence (AI) in marketing and social media, harm to consumers has prompted calls for AI auditing/certification. Understanding consumers’ approval of AI certification entities is vital for its effectiveness and companies’ choice of certification. This study aims to generate important insights into the consumer perspective of AI certifications and stimulate future research.

A literature and status-quo-driven search of the AI certification landscape identifies entities and related concepts. This study empirically explores consumer approval of the most discussed entities in four AI decision domains using an online experiment and outline a research agenda for AI certification in marketing/social media.

Trust in AI certification is complex. The empirical findings show that consumers seem to approve more of non-profit entities than for-profit entities, with the government approving the most.

The introduction of AI certification to marketing/social media contributes to work on consumer trust and AI acceptance and structures AI certification research from outside marketing to facilitate future research on AI certification for marketing/social media scholars.

For businesses, the authors provide a first insight into consumer preferences for AI-certifying entities, guiding the choice of which entity to use. For policymakers, this work guides their ongoing discussion on “who should certify AI” from a consumer perspective.

To the best of the authors’ knowledge, this work is the first to introduce the topic of AI certification to the marketing/social media literature, provide a novel guideline to scholars and offer the first set of empirical studies examining consumer approval of AI certifications.

Machine learning (ML) techniques are increasingly important in enabling business-to-business (B2B) companies to offer personalized services to business customers. On the other hand, humans play a critical role in dealing with uncertain situations and the relationship-building aspects of a B2B business. Most existing studies advocating human-ML augmentation simply posit the concept without providing a detailed view of augmentation. Therefore, the purpose of this paper is to investigate how human involvement can practically augment ML capabilities to develop a personalized information system (PIS) for business customers.

The authors developed a research framework to create an integrated human-ML PIS for business customers. The PIS was then implemented in the energy sector. Next, the accuracy of the PIS was evaluated using customer feedback. To this end, precision, recall and F1 evaluation metrics were used.

The computed figures of precision, recall and F1 (respectively, 0.73, 0.72 and 0.72) were all above 0.5; thus, the accuracy of the model was confirmed. Finally, the study presents the research model that illustrates how human involvement can augment ML capabilities in different stages of creating the PIS including the business/market understanding, data understanding, data collection and preparation, model creation and deployment and model evaluation phases.

This paper offers novel insight into the less-known phenomenon of human-ML augmentation for marketing purposes. Furthermore, the study contributes to the B2B personalization literature by elaborating on how human experts can augment ML computing power to create a PIS for business customers.

This paper aims to analyze the morphology transformation on the Cu₃Sn grains during the formation of full Cu₃Sn solder joints in electronic packaging.

Because of the infeasibility of analyzing the morphology transformation intuitively, a novel equivalent method is used. The morphology transformation on the Cu₃Sn grains, during the formation of full Cu₃Sn solder joints, is regarded as equivalent to the morphology transformation on the Cu₃Sn grains derived from the Cu/Sn structures with different Sn thickness.

During soldering, the Cu₃Sn grains first grew in the fine equiaxial shape in a ripening process until the critical size. Under the critical size, the Cu₃Sn grains were changed from the equiaxial shape to the columnar shape. Moreover, the columnar Cu₃Sn grains could be divided into different clusters with different growth directions. With the proceeding of soldering, the columnar Cu₃Sn grains continued to grow in a feather of the width growing at a greater extent than the length. With the growth of the columnar Cu₃Sn grains, adjacent Cu₃Sn grains, within each cluster, merged with each other. Next, the merged columnar Cu₃Sn grains, within each cluster, continued to merge with each other. Finally, the columnar Cu₃Sn grains, within each cluster, merged into one coarse columnar Cu₃Sn grain with the formation of full Cu₃Sn solder joints. The detailed mechanism, for the very interesting morphology transformation, has been proposed.

Few researchers focused on the morphology transformation of interfacial phases during the formation of full intermetallic compounds joints. To bridge the research gap, the morphology transformation on the Cu₃Sn grains during the formation of full Cu₃Sn solder joints has been studied for the first time.

Recommender systems (RS) are designed to communicate with users and drive consumers' engagement with the platform. However, little is known about the strength of this relationship and how RS can create stronger consumer engagement (CE) with the platform brand. Addressing this gap, this paper examines the role of RS in converting consumers' short-term engagement with the RS to their longer-term platform engagement.

To explore these issues, the authors review key literature in the areas of CE and RS, from which they develop a conceptual framework.

The proposed framework suggests RS design as an important precursor to consumers' RS use, which is expected to affect their platform engagement/disengagement, in turn impacting the firm's long-term outcomes. The authors also identify key managerial tactics, strategies and challenges to aid the conversion of consumers' RS to CE.

This research raises pertinent implications for research on the RS/CE interface, as synthesized in a proposed research agenda.

Based on the attained insight, authors outline implications for managing, facilitating and leveraging the proposed RS to CE conversion process. Correspondingly, authors argue that, to optimize RS effectiveness, RS designers should understand the nature of CE.

By exploring the effect of consumers' RS on their longer-term CE with the platform, the analyses offer pioneering managerial insight into RS effectiveness from a CE perspective.

This study aimed to investigate the perception and acceptance of artificial intelligence (AI) technologies among the Israeli workforce. More specifically, it examined how age, income, and education level are related to employees’ fears of being replaced by AI technologies and their willingness to adopt these technologies in their personal and professional lives.

Data were collected by surveying 502 adults from the Jewish population of Israel in February 2023 via an Internet panel. Stratified sampling was performed to ensure a representative cross-section of the population.

Contrary to the expectations from a technologically advanced society, the findings indicated varied levels of enthusiasm and apprehension. Age was found to be negatively correlated with the fear of being replaced by AI technologies and the willingness to adopt these technologies. Income was negatively correlated with the fear of being replaced by AI technologies. Education level was negatively correlated with the fear of being replaced and positively correlated with the willingness to adopt.

The findings provide valuable guidance for policymakers, educators, and business leaders in shaping AI integration strategies. They emphasize the need for targeted educational and policy initiatives to bridge the gap in AI readiness.

This study offers unique insights into the perceptions toward AI in a leading technological hub, contributing to the understanding of how advanced societies are adapting to rapid AI integration.

The digital revolution has changed consumer–service provider interaction, spawning a new generation of FinTech. This paper analyzes consumers' reactions to artificial intelligence (AI) (vs human) decisions.

The authors tested their predictions by conducting two experimental studies with FinTech consumers (n = 503).

The results reveal that consumers' responses to AI (vs human) credit decisions depend on the type of credit product. For personal loans, the rejection by an AI provider triggers higher levels of satisfaction compared to a credit analyst. This effect is explained via the perceived role congruity. In addition, the findings reveal that consumers’ rejection sensitivity determines how they perceive financial services role congruity.

To the best of the authors' knowledge, this research is the first to jointly examine AI (vs human) credit decisions in FinTech and role congruity, extending prior research in the field.