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How to embed artificial intelligence (AI) in human resource management (HRM) is one of the core challenges of digital HRM. Despite regulations demanding humans in the loop to ensure human oversight of AI-based decisions, it is still unknown how much decision-makers rely on information provided by AI and how this affects (personnel) selection quality.

This paper presents an experimental study using vignettes of dashboard prototypes to investigate the effect of AI on decision-makers’ overreliance in personnel selection, particularly the impact of decision-makers’ information search behavior on selection quality.

Our study revealed decision-makers’ tendency towards status quo bias when using an AI-based ranking system, meaning that they paid more attention to applicants that were ranked higher than those ranked lower. We identified three information search strategies that have different effects on selection quality: (1) homogeneous search coverage, (2) heterogeneous search coverage, and (3) no information search. The more applicants were searched equally often (i.e. homogeneous) as when certain applicants received more search views than others (i.e. heterogeneous) the higher the search intensity was, resulting in higher selection quality. No information search is characterized by low search intensity and low selection quality. Priming decision-makers towards carrying responsibility for their decisions or explaining potential AI shortcomings had no moderating effect on the relationship between search coverage and selection quality.

Our study highlights the presence of status quo bias in personnel selection given AI-based applicant rankings, emphasizing the danger that decision-makers over-rely on AI-based recommendations.

Although it is commonly agreed that prescriptive analytics can benefit organizations by enabling better decision-making, the deployment of prescriptive analytics tools can be challenging. Previous studies have primarily focused on methodological issues rather than the organizational deployment of analytics. However, successful deployment is key to achieving the intended benefits of prescriptive analytics tools. Therefore, this study aims to identify the enablers of successful deployment of prescriptive analytics.

The authors examine the enablers for the successful deployment of prescriptive analytics through five organizational case studies. To provide a comprehensive view of the deployment process, each case includes interviews with users, managers and top management.

The findings suggest the key enablers for successful analytics deployment are strong leadership and management support, sufficient resources, user participation in development and a common dialogue between users, managers and top management. However, contrary to the existing literature, the authors found little evidence of external pressures to develop and deploy analytics. Importantly, the success of deployment in each case was related to the similarity with which different actors within the organization viewed the deployment process. Furthermore, end users tended to highlight user participation, skills and training, whereas managers and top management placed greater emphasis on the importance of organizational changes.

The results will help practitioners ensure that key enablers are in place to increase the likelihood of the successful deployment of prescriptive analytics.

This study aims to present state-of-the-art research on circular economy (CE) implementation in construction and demolition waste management (CDWM) within the construction sector.

A mixed-method (scientometric and critical analysis) review strategy was adopted, involving scientometric and critical analysis to uncover the evolutionary progress within the research area, investigate key research themes in the field, and explore ten issues of CE in CDWM. Moreover, avenues for future research are provided for researchers, practitioners, decision-makers, and planners to bring innovative and new knowledge to this field.

A total of 212 articles were analyzed, and scientometric analysis was performed. The critical analysis findings reveal extensive use of surveys, interviews, case studies, or mixed-method approaches as study methodologies. Furthermore, there is limited focus on the application of modern technologies, modeling approaches, decision support systems, and monitoring and traceability tools of CE in the CDWM field. Additionally, no structured framework to implement CE in CDWM areas has been found, as existing frameworks are based on traditional linear models. Moreover, none of the studies discuss readiness factors, knowledge management systems, performance measurement systems, and life cycle assessment indicators.

The outcomes of this study can be utilized by construction and demolition sector managers, researchers, practitioners, decision-makers, and policymakers to comprehend the state-of-the-art, explore current research topics, and gain detailed insights into future research areas. Additionally, the study offers suggestions on addressing these areas effectively.

This study employs a universal approach to provide the current research progress and holistic knowledge about various important issues of CE in CDWM, offering opportunities for future research directions in the area.

Heritage building management serves as a potent catalyst for sustainability, yet it poses a distinctive set of challenges. Achieving a harmonious balance between conserving the building's historical and cultural value and ensuring modern functionality and safety remains a primary concern. The present work proposes a socio-technical approach to the development and use of a digital twin (DT) that will integrate social data related to the use of heritage buildings with building and environmental data.

The paper presents a logical and systematic joined-up management framework to the targeted heritage buildings, according to a “Whole Building” approach. Our approach is informed by the underpinning assumption that a heritage building and even more a heritage neighborhood is a socio-technical, complex and dynamic system, the change of which depends on the dynamic interconnections of materials, competences, resources, values, space/environment, senses and time.

A heritage dynamics approach is adopted to unfold the dynamic nature of heritage and to better inform decisions that can be made in the present and future, achieving people-centered and place-based heritage management. This proposition underlines the heritage transformation as a complex systemic process that consists of nonlinear interconnections of multiple heterogeneous factors (values, senses, attitudes, spaces and resources).

This paper presents a multi-level framework of DTs that interact hierarchically to comprehensively understand, assimilate and seamlessly integrate intricate contexts, even when faced with conflicting conditions from diverse cultural heritage entities. This paper outlines the importance of the iterative system dynamics (SD) approach, which enables adaptive management and ensures the resilience of cultural heritage over time.

Information technology as a source of information and decision-making has wider acceptance in contemporary times. Studies have identified the importance of electronic word of mouth (eWOM) and its impact on decision-making. The primary objective of this research is to investigate the relationship between eWOM (pre-travel), destination image (post-visit), tourist satisfaction and eWOM intentions post the COVID-19 crisis. This study is important as it is anticipated that in the post-pandemic world, tourists would seek well-being-enhancing experiences more often than any other form of tourism.

Data were collected through an online questionnaire circulated over a period of six months from November 2020 to April 2021. Non-probability purposive sampling technique was used.

The results depicted that wellness destination’s image has a significant influence on wellness tourists’ satisfaction level and their eWOM intentions. Furthermore, it also came to light that the satisfaction level of wellness tourist satisfaction was found to be significantly influencing their eWOM intentions. The mediating role of wellness tourists’ satisfaction was found to be significant from destination image (post-visit) to eWOM intentions. COVID-19 pandemic perceived health risk was also found to be significantly moderating the relationship between eWOM (pre-travel) and eWOM intentions.

Pre-travel online information about a wellness destination is an important determinant of travel decisions, especially during the COVID-19 crisis. This empirical study proves that effective use of this information can advance a destination’s marketing efforts and ensure future demand.

The purpose of this paper is to identify the influence of homophily and social identity in eWOM adoption and influence on music consumption in streaming platforms. The study also proposes a framework for eWOM influence on product consumption.

Data were collected through an electronic questionnaire with a total of 524 valid responses. A partial least square methodology was used to approach the data analysis.

The findings indicate that both homophily and social identity have an influence on eWOM effect. Additionally, homophily shows influence on social identity being an important value to the total influence. It is also confirmed how eWOM has direct influence on music consumption.

Social information of consumers becomes more relevant than ever, as the results contribute to highlight its importance as a message adoption influencer and product consumption driver.

This study contributes to social communication theory by identifying homophily and social identity as valuable assets during eWOM generation. The research brings a new angle to social identity theory in the social network environment and influences eWOM adoption in music streaming consumption.

El propósito de este estudio es identificar la influencia de la homofilia y la identidad social en la adopción de eWOM y su influencia en el consumo de música en plataformas de streaming. El estudio también propone un marco para la influencia de la eWOM en el consumo de productos.

Los datos se recopilaron a través de un cuestionario electrónico con un total de 524 respuestas válidas. Se utilizó una metodología de Mínimos Cuadrados Parciales para abordar el análisis de datos.

Los hallazgos indican que tanto la homofilia como la identidad social tienen una influencia en el efecto de la eWOM. Además, la homofilia muestra influencia en la identidad social siendo un valor importante para la influencia total. También se confirma cómo la eWOM tiene influencia directa en el consumo de música.

Este estudio contribuye a la teoría de la comunicación social identificando la homofilia y la identidad social como activos valiosos durante la generación de eWOM. La investigación aporta un nuevo enfoque a la teoría de la identidad social en el entorno de las redes sociales e influye en la adopción de eWOM en el consumo de música en streaming.

La información social de los consumidores se vuelve más relevante que nunca, ya que los resultados contribuyen a resaltar su importancia como influenciador en la adopción de mensajes y como impulsor del consumo de productos.

本文旨在确定同质性和社会认同对电子口碑在音乐流媒体平台上的采纳的影响。研究还提出了一个关于eWOM对产品消费影响的框架。

通过电子问卷收集了524份有效回复, 并采用了偏最小二乘法对数据进行分析。

研究结果显示, 同质性和社会认同对eWOM效果产生影响。此外, 同质性对社会认同产生影响, 对总体影响具有重要价值。另外, 研究还确认了eWOM对音乐消费的直接影响。

本研究通过确认同质性和社会认同在eWOM生成过程中的重要价值, 为社交传播理论做出了重要贡献。研究为社交网络环境下的社会认同理论提供了新的视角,并对音乐流媒体消费中的eWOM采纳产生了影响。

在当前社会环境下, 消费者的社会信息比以往任何时候都更加重要。本研究的结果有助于凸显社会信息在信息采纳和产品消费方面的重要性, 进一步强调了其作为影响因素和驱动力的重要性。

This paper aims to analyse the determinants of the intention to share a post about an environmental issue posted by a tourism destination on Facebook. The authors use the stimulus-organism-response (SOR) model as a theoretical framework and consider cognitive variables (destination social responsibility, tourist social responsibility and three types of congruence) as antecedents of emotions and of the tourists’ response (intention to share). Specific factors related to the social platform (attachment and active use of social media) are also included.

The model was tested for two destinations with different positioning (green tourism versus sun and beach). For the sampling strategy, the authors conducted an online poll targeting Facebook users who had undertaken at least one trip in the previous year. The sample consisted of 1,001 individuals.

The empirical evidence obtained indicates that consumer–cause congruence is the most important variable for explaining the intention to share the post for both destinations, with the destination–cause congruence being non-significant. The authors also observed that active participation on the social network stimulated the intention to share this specific content.

Unlike prior research, this paper examined consumer motivators for engaging with online corporate social responsibility content for tourism destinations, specifically focusing on destination social responsibility in sustainable tourism. The model also incorporates three types of congruence, revealing variations in their impact on explaining the intention to share sustainability-related posts.

This study presents a systematic literature review (SLR) of the interdisciplinary literature on drones in last-mile delivery (LMD) to extrapolate pertinent insights from and into the logistics management field.

Rooting their analytical categories in the LMD literature, the authors performed a deductive, theory refinement SLR on 307 interdisciplinary journal articles published during 2015–2022 to integrate this emergent phenomenon into the field.

The authors derived the potentials, challenges and solutions of drone deliveries in relation to 12 LMD criteria dispersed across four stakeholder groups: senders, receivers, regulators and societies. Relationships between these criteria were also identified.

This review contributes to logistics management by offering a current, nuanced and multifaceted discussion of drones' potential to improve the LMD process together with the challenges and solutions involved.

The authors provide logistics managers with a holistic roadmap to help them make informed decisions about adopting drones in their delivery systems. Regulators and society members also gain insights into the prospects, requirements and repercussions of drone deliveries.

This is one of the first SLRs on drone applications in LMD from a logistics management perspective.

In today’s highly competitive world, the purpose of this research is to emphasize the increasing significance of management education and advocate for the adoption of innovative teaching approaches, specifically focusing on artificial intelligence (AI)-driven personalized learning (PL). This study aims to explore the integration of self-determination theory (SDT) principles into management education, with a primary focus on enhancing student motivation, engagement and academic performance (AP).

This interdisciplinary research adopts a multifaceted approach, combining perspectives from AI, education and psychology. The design and methodology involve a thorough exploration of the theoretical foundations of both AI-driven education and SDT. The research demonstrates how these two elements can synergize to create a holistic educational experience. To substantiate the theoretical claims, empirical data-driven analyses are employed, showcasing the effectiveness of AI-enabled personalized learning (AIPL). The study integrates principles from SDT, such as autonomy, competence and relatedness, to create an environment where students are intrinsically motivated, receiving tailored instruction for optimal outcomes.

The study, rooted in SDT, demonstrates AIPL’s transformative impact on management education. It positively influences students’ autonomy, competence and relatedness, fostering engagement. Autonomy is a key driver, strongly linked to improved AP. The path analysis model validates these relationships, highlighting AI’s pivotal role in reshaping educational experiences and intrinsically motivating students.

This study holds substantial significance for educators, policymakers and researchers. The findings indicate that the AIPL model is effective in increasing student interest and improving AP. Furthermore, this study offers practical guidance for implementing AI in management education to empower students, enhance engagement and align with SDT principles.

Contribute original insights through an interdisciplinary lens. Synthesize AI and SDT principles, providing a roadmap for a more effective educational experience. Empirical data-driven analyses enhance credibility, offering valuable contributions for educators and policymakers in the technology-influenced education landscape.

Applications of artificial intelligence (AI), such as virtual and physical service robots, generative AI, large language models and decision support systems, alter the nature of services. Most service research centers on the division between human and AI resources. Less attention has been paid to analyzing the entangled resource relations and interactions between humans and AI entities. Thus, the purpose of this paper is to extend our metatheoretical understanding of resource integration and value cocreation by analyzing different human–AI resource relations in service ecosystems.

The conceptual paper adapts a novel framework from postphenomenology, specifically cyborg intentionality. This framework is used to analyze what kinds of human–AI resource relations enable resource integration and value cocreation in service ecosystems.

We conceptualize seven different human–AI resource relations, namely background, embodiment, hermeneutic, alterity, cyborg, immersion and composite relation. The sociotechnical entangled perspective on human–AI resource relations challenges and reframes our understanding of interactions between humans and nonhumans in resource integration and value cocreation and the distinction between operant and operand resources in service research.

Our primary contribution to researchers and service providers is dissolving the distinction between operant and operand resources. We present two foundational propositions. 1. Humans and AI become entangled value cocreating resources in inherently sociotechnical service ecosystems; and 2. Human and AI entanglements in value cocreation manifest through seven resource relations in inherently sociotechnical service ecosystems. Understanding the combinatorial potential of different human–AI resource relations enables service providers to make informed choices in service ecosystems.