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The rising costs of recruiting and hiring workers and the seismic shift of age demographics in the United States workforce has created much stir around the concept of generational cohorts. Although much has been done by researchers and practitioners alike to attempt a better understanding of each generational group’s leadership preferences, confusing and contradictory results has attracted much criticism. This critique has inspired efforts to look at the concept of leadership and followership preference through an alternative lifespan developmental lens. Because leadership influences are inherently social influences, a person’s overall lifespan development level may potentially provide a deeper perspicacity of the phenomenon than examining it from the more conventional generational cohort perspective. However, specific research into this area is lacking. This paper adds to the literature by uncovering what we are missing in research and practice when we look at age-related leadership phenomena solely from a generational cohort perspective. A review of the contradicting literature on generational cohorts and leadership is offered. Next, specific lifespan developmental theories are examined, and propositions and implications of such research are extended.

Mentoring is an essential tool for teacher education, not least because it allows knowledge and experience sharing. Research findings suggest a close relationship between mentoring and the well-being of those involved. However, little is known about this relationship or the mechanisms involved. This paper aims to discuss the aforementioned issues.

Through an integrative literature review, important links between mentor and mentee well-being and certain aspects of mentoring are explored. The results of the review are then synthesised into a theoretical framework.

The framework developed here posits that the well-being of teachers relates to the quality of mentoring. This includes teachers' perceptions of mentoring and their decision to get involved, their contributions to functional mentor–mentee relationships and the selection and use of mentoring styles that support both mentor and mentee well-being. Moreover, the framework hypothesises that the quality of mentoring received by mentees is related to their well-being, which is in turn connected to their professional development. Most of these relationships appear to be bidirectional, meaning that mentee well-being also contributes to mentoring quality and mentor well-being. Finally, it is hypothesised that the well-being of both mentors and mentees is connected to the school context in which the mentoring takes place.

This work provides a novel, comprehensive framework for future examinations of the connections between well-being and mentoring. It synthesises the prior work on this topic by integrating findings from both the mentor and mentee perspectives into a single framework.

The purpose of this paper is to examine, among the possible causes, whether trust in the leader is one of the most relevant factors on the success of a virtual work team by analyzing different antecedents of the trust and its consequences.

The influence that certain physical and behavioral characteristics of the leader (attractiveness, empathy and justice) exert on the degree of trust is evaluated. On the other hand, the influence of trust on the efficiency of the team, in terms of organizational citizenship behavior and commitment, is analyzed. To test the model, a survey was conducted on real work teams and the data were analyzed through a model of structural equations.

The results support the hypotheses and consequently, the relevance of trust in the leader. Specifically, the leader’s physical and behavioral characteristics have a significant effect on the trust in the leader. This trust results in greater organizational efficiency.

Despite the undisputable growth in the number of companies using virtual teams, it is also true that many of these teams fail to perform. In this sense, this paper analyzes if certain factors related to leadership can be relevant when influencing the efficiency of a virtual work team. This paper contributes to a better understanding of the internal processes within a virtual team in order to maximize the chances of success in this type of organizations.

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.

The purpose of this paper is to make the case for bringing compassion to students in educational settings, preschool through graduate school (PK-20).

First, the author defines what is meant by “compassion” and differentiates it from the related constructs. Next, the author discusses the importance of bringing compassion into education, thinking specifically about preschool, K-12 (elementary and middle school/junior high/high school), college students, and graduate students (e.g. law, medical, nurses, counselors and therapists-in-training). The author then reviews the scant empirical literature on compassion in education and makes recommendations for future research. In the final section, the author makes specific and practical recommendations for the classroom (e.g. how to teach and evaluate compassion in PK-20).

While there is a fair amount of research on compassion with college students, and specifically regarding compassion for oneself, as the author reviews in this paper, the field is wide open in terms of empirical research with other students and examining other forms of compassion.

This is not a formal review or meta-analysis.

This paper will be a useful resource for teachers and those interested in PK-20 education.

This paper highlights the problems and opportunities for bringing compassion into education settings.

To date, no review of compassion in PK-20 exists.

The transition to omnichannel retail is the recognized future of retail, which uses digital technologies (e.g. augmented reality shopping assistants) to enhance the customer shopping experience. However, retailers struggle with the implementation of such technologies in brick-and-mortar stores. Against this background, the present study investigates the impact of a smartphone-based augmented reality shopping assistant application, which uses personalized recommendations and explainable artificial intelligence features on customer shopping experiences.

The authors follow a design science research approach to develop a shopping assistant application artifact, evaluated by means of an online experiment (n = 252), providing both qualitative and quantitative data.

Results indicate a positive impact of the augmented reality shopping assistant application on customers' perception of brick-and-mortar shopping experiences. Based on the empirical insights this study also identifies possible improvements of the artifact.

This study's assessment is limited to an online evaluation approach. Therefore, future studies should test actual usage of the technology in brick-and-mortar stores. Contrary to the suggestions of established theories (i.e. technology acceptance model, uses and gratification theory), this study shows that an increase of shopping experience does not always convert into an increase in the intention to purchase or to visit a brick-and-mortar store. Additionally, this study provides novel design principles and ideas for crafting augmented reality shopping assistant applications that can be used by future researchers to create advanced versions of such applications.

This paper demonstrates that a shopping assistant artifact provides a good opportunity to enhance users' shopping experience on their path-to-purchase, as it can support customers by providing rich information (e.g. explainable recommendations) for decision-making along the customer shopping journey.

This paper shows that smartphone-based augmented reality shopping assistant applications have the potential to increase the competitive power of brick-and-mortar retailers.

Research into the interpretability and explainability of data analytics and artificial intelligence (AI) systems is on the rise. However, most recent studies either solely promote the benefits of explainability or criticize it due to its counterproductive effects. This study addresses this polarized space and aims to identify opposing effects of the explainability of AI and the tensions between them and propose how to manage this tension to optimize AI system performance and trustworthiness.

The author systematically reviews the literature and synthesizes it using a contingency theory lens to develop a framework for managing the opposing effects of AI explainability.

The author finds five opposing effects of explainability: comprehensibility, conduct, confidentiality, completeness and confidence in AI (5Cs). The author also proposes six perspectives on managing the tensions between the 5Cs: pragmatism in explanation, contextualization of the explanation, cohabitation of human agency and AI agency, metrics and standardization, regulatory and ethical principles, and other emerging solutions (i.e. AI enveloping, blockchain and AI fuzzy systems).

As in other systematic literature review studies, the results are limited by the content of the selected papers.

The findings show how AI owners and developers can manage tensions between profitability, prediction accuracy and system performance via visibility, accountability and maintaining the “social goodness” of AI. The results guide practitioners in developing metrics and standards for AI explainability, with the context of AI operation as the focus.

This study addresses polarized beliefs amongst scholars and practitioners about the benefits of AI explainability versus its counterproductive effects. It poses that there is no single best way to maximize AI explainability. Instead, the co-existence of enabling and constraining effects must be managed.

The article makes an empirical and conceptual contribution to understanding the temporalities of information literacies. The paper aims to identify different ways in which anticipation of certain outcomes shapes strategies and tactics for engagement with algorithmic information intermediaries. The paper suggests that, given the dominance of predictive algorithms in society, information literacies need to be understood as sites of anticipation.

The article explores the ways in which the invisible algorithms of information intermediaries are conceptualised, made sense of and challenged by young people in their everyday lives. This is couched in a conceptual discussion of the role of anticipation in understanding expressions of information literacies in algorithmic cultures. The empirical material drawn on consists of semi-structured, pair interviews with 61 17–19 year olds, carried out in Sweden and Denmark. The analysis is carried out by means of a qualitative thematic analysis in three steps and along two sensitising concepts – agency and temporality.

The results are presented through three themes, anticipating personalisation, divergences and interventions. These highlight how articulating an anticipatory stance works towards connecting individual responsibilities, collective responsibilities and corporate interests and thus potentially facilitating an understanding of information as co-constituted by the socio-material conditions that enable it. This has clear implications for the framing of information literacies in relation to algorithmic systems.

The notion of algo-rhythm awareness constitutes a novel contribution to the field. By centring the role of anticipation in the emergence of information literacies, the article advances understanding of the temporalities of information.

This study aims to examine the way in which elderly people, men and women, with a terminal illness use language to construct a narrative about their “living-with-dying” experience.

This investigation is a secondary analysis based on a corpus of health and illness narratives collected by the Health Experiences Research Group at the University of Oxford and published by the DIPEx charity (available at: http://healthtalk.org/home).

This study shows that there are qualitative differences in the way in which not only elderly people but also men and women report their experience with terminal illness and their relation to death.

Understanding the different perspectives from which elderly people narrate their experiences of how they live while dying from terminal illness can help health professionals to develop more effective all-inclusive health policies and practices in end-of-life care.

Recommendation systems are a fundamental tool for hotels to adopt a differentiating competitive strategy. The main purpose of this work is to use machine learning techniques to treat imbalanced data sets, not applied until now in the tourism field. These techniques have allowed the authors to analyse the influence of imbalance data on hotel recommendation models and how this phenomenon affects client dissatisfaction.

An opinion survey was conducted among hotel customers of different categories in 120 different countries. A total of 135.102 surveys were collected over eleven quarters. A longitudinal design was conducted during this period. A binary logistic model was applied using the function generalized lineal model (GLM).

Through the analysis of a representative amount of data, the authors empirically demonstrate that the imbalance phenomenon is systematically present in hotel recommendation surveys. In addition, the authors show that the imbalance exists independently of the period in which the survey is done, which means that it is intrinsic to recommendation surveys on this topic. The authors demonstrate the improvement of recommendation systems highlighting the presence of imbalance data and consequences for marketing strategies.

The main contribution of the current work is to apply to the tourism sector the framework for imbalanced data, typically used in the machine learning, improving predictive models.