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Smoke-free prison policies have been introduced in some countries, in part to address very high levels of tobacco use in people in prison. However, relapse rates post-release remain high. This papers aims to improve understanding of post-release smoking and/or vaping behaviour is necessary to inform support for a priority population.

The authors searched health, social science and criminal justice databases for studies about smoking/vaping behaviours among people released from smoke-free prisons. Studies were included if they reported primary data and were published between January 2017 and March 2024 in English; the population was adults/young people (16 yr+) imprisoned or formerly imprisoned, in prisons with comprehensive smoke-free policies; and at least one of the following was reported: pre-release intention to smoke, vape or remain abstinent post-release; smoking/vaping behaviour post-release and factors influencing smoking/vaping behaviour; attempts to quit again following post-release smoking/vaping relapse.

Nine studies met our criteria. The evidence base is small and mainly from the USA or Australia. Evidence continues to suggest that most people resume smoking after leaving a smoke-free prison. No new interventions have been successful in reducing relapse rates. No studies report on vaping post-release, although two studies report on perceived factors affecting smoking relapse post-release from prisons allowing vaping.

Given very high rates of relapse, there remains a significant need to better understand what approaches are feasible and acceptable for reducing return to smoking post-release.

This review updates the limited evidence on smoking behaviours after leaving a smoke-free prison.

This study aims to analyze the effect of cognitive load and social value orientation on managers’ preferences when they face with two types of restructuring choices in financially distressed firms: the first belonging to the family of organizational restructuring (massive layoffs) and the second to the family of financial restructuring (debt increases).

The authors investigate experimentally the impact of managers’ cognitive load and social value orientation on the decision to restructure leveraged buyout (LBO) firms in financial distress by using either massive layoffs or debt increases.

By investigating the impact of managers’ cognitive load and social value orientation on the restructuring decision of an LBO firm in financial distress, the research reveals that, on average, cognitively loaded managers prefer massive layoffs over increased debt levels. The massive layoffs seemingly provide a relatively easier way to avoid conflict with influential, residual claimants. In contrast, social value–oriented managers actively avoid massive layoffs and prefer to increase debt.

These results imply that the performance mechanisms emphasized to improve agency relations, for example, in LBOs, have their own limitations during periods of financial distress. This study shows that one of these limits is related to cognitive distortions and personality traits.

In this research, the originality lies in understanding how managers’ internal factors affect their restructuring decision-making, in the case of LBO firms in financial distress.

This paper critically evaluates potential barriers to employment opportunities for ethnic minority (EM) individuals in Scottish Local Authorities – both in terms of access to job and development opportunities. It provides a fundamental discussion of concepts around race and ethnicity, and the levels of social injustice, with an explicit focus on institutional racialisation, discrimination and segregation. The paper explores organisational approaches towards recruitment, including positive action and workforce development.

This paper adopts a subjectivist (ontology) and interpretivist (epistemology) stance, based on a small-scale, in-depth investigation. The data have been gathered through semi-structured interviews with equality diversity and inclusion (EDI) officers in four Scottish Local Authorities, utilising thematic analysis.

The finding suggests that participating local authorities have a long way to go to ensure the elimination of barriers to employment for EM people. This is largely based on concerns around limitations in the application of positive action and elimination of disadvantages in recruitment and access to career and development opportunities?

The paper aims to contribute by exploring the availability of employment opportunities for EMs through the eyes of EDI Officers in four local authorities. Their thorough understanding, over- and insight into potential equality issues from an employment perspective are invaluable, focussing on more tangible organisational issues and approaches.

Allocation problems in accounting require joint costs to be allocated among participating agents. In this setting, however, unfair allocations can stifle cooperation and lead to inefficient group outcomes. Then, what qualifies as fair enough for individual agents to agree to cooperate and extract joint benefits? Building on prior analytical literature that has offered perspectives involving joint cost allocations, we experimentally evaluate two common notions of fairness that present competing predictions in the cost allocation context – proportionality and equality. We operationalize two notions of fairness using a behavioral approach and examine which fairness notion prevails in cost allocation problems. More specifically, we examine fairness considerations in the cost allocation context using a modified ultimatum game, where joint cost savings can only be acquired through cooperation between two agents and individual contributions are varied transparently. Our experimental evidence suggests that fairness considerations in cost allocations coincide more with the proportionality notion when individuals make different contributions to create joint benefits. These findings provide important insights on the key rationale underlying the prevalent cost allocation method in accounting practices and the design of fair cost allocations that promote cooperation among agents.

This study examines how asking employees to self-assess their performance during the compensation setting process, when they are unaware of their marginal contribution to firm profit, affects employer welfare. Previous research suggests that giving employees a voice in the compensation setting process can positively affect employee performance and firm profit (Jenkins & Lawler, 1981; Roberts, 2003). However, the study proposes that asking employees to assess their own performance as part of the compensation setting process can have unintended consequences that ultimately lead to higher employee compensation demands. This is because asking employees to assess their performance increases their overconfidence in their own performance and their compensation demands. As a result, employers may face the dilemma of whether to meet these higher compensation demands or risk economic losses due to employee retaliation if their demands are not met. Through experimental evidence comparing a control condition without self-assessments and three self-assessment reporting conditions, the study provides evidence that supports the notion that eliciting employee self-assessments as part of the compensation process reduces employer welfare. Data on employee perceptions of performance further support the notion that asking employees to evaluate their performance leads to an inflated perception of their performance. These findings provide a theory-based explanation of why, in practice, many companies disentangle employee performance assessments from the compensation setting process and that companies are well advised in doing so.

This article aims to explore the impact of climate disasters on small-scale farmers’ willingness to cooperate and explore the mediating effect of social capital.

The study investigates farmers’ willingness to cooperate through a framed field approach and surveys the information of individuals and villages, including climate disasters and social capital, using a structured questionnaire from rural communities in Jiangxi and Sichuan, China.

The results show that climate disasters and social capital are significant and positive determinants of farmers’ willingness to cooperate. In specific types of climate disasters, drought is positively associated with farmers’ cooperation willingness. Moreover, the mediation effect of drought on farmers’ willingness to cooperate through social capital has been demonstrated to be significant although negative, whereas the mediation effect of flood on farmers’ willingness to cooperate through social capital is significant and positive.

First, given the limited studies focusing on the impact of climate disasters on small-scale farmers’ willingness to cooperate, the authors complement the existing literature through a framed field experiment approach by designing a scenario that every farmer may encounter in their production activities. Second, the study figures out the roles of drought and flood as different kinds of climate disasters in farmers’ decision-making of cooperation and sheds light on the positive impact of climate disasters on small-scale farmers. Finally, this paper provides empirical evidence of social capital as a potential channel through which climate disasters could possibly affect farmers’ willingness to cooperate.

The present study aimed to explore the consequences of perceived authenticity in artificial intelligence (AI) assistants and develop a serial-mediation architecture specifying causation of loyalty in human–AI relationships. It intended to assess the predictive power of the developed model based on a training-holdout sample procedure. It further attempted to map and examine the predictors of loyalty, strengthening such relationship.

Partial least squares structural equation modeling (PLS-SEM) based on bootstrapping technique was employed to examine the higher-order effects pertaining to human–AI relational intricacies. The sample size of the study comprised of 412 AI assistant users belonging to millennial generation. PLS-Predict algorithm was used to assess the predictive power of the model, while importance-performance analysis was executed to assess the effectiveness of the predictor variables on a two-dimensional map.

A positive relationship was found between “Perceived Authenticity” and “Loyalty,” which was serially mediated by “Perceived-Quality” and “Animacy” in human–AI relational context. The construct “Loyalty” remained a significant predictor of “Emotional-Attachment” and “Word-of-Mouth.” The model possessed high predictive power. Mapping analysis delivered contradictory result, indicating “authenticity” as the most significant predictor of “loyalty,” but the least effective on performance dimension.

The findings of the study may assist marketers to understand the relevance of AI authenticity and examine the critical behavioral consequences underlying customer retention and extension strategies.

The study is pioneer to introduce a hybrid AI authenticity model and establish its predictive power in explaining the transactional and communal view of human reciprocation in human–AI relationship. It exclusively provided relative assessment of the predictors of loyalty on a two-dimensional map.

We operate in a complex dynamic world where human activities interfere with the environment in ways that may cause unpredictable extreme outcomes that reflect exposure to uncertainty and unknown factors as opposed to identifiable risk events. As humans, we tend to downplay the effects of uncertainty and create a false sense of security by adopting formal control-based management practices where we in truth face more uncertainties than we realize. Hence, we attempt to assess extreme disaster events to mitigate adverse effects but fail to address the underlying causes for the exposures rooted in the way we have organized global economic activities. Exposures partially derive from the way enterprises govern their economic assets while systemic exposures derive from the way we conduct our global business practices. Identified exposures are typically local whereas the systemic causes are global with collective societal effects. Therefore, mitigating enterprise exposures will not address the global systemic causes for the extreme societal effects. To reach more sustainable solutions we must involve businesses that operate the global economy as well as the societies they operate in around the world. Given the extreme uncertainty of systemic risks, grand solutions cannot be derived from computational analytics but require experimentation among engaged public and private organizations and open collaborative learning to identify viable solutions from diverse insights. We must embrace uncertainty to explore innovative opportunities for proposed solutions and implement them in gradual co-evolving progression to develop viable sustainable economic outcomes.

With the intensification of market competition, there is a growing demand for weak signal identification and evolutionary analysis for enterprise foresight. For decades, many scholars have conducted relevant research. However, the existing research only cuts in from a single angle and lacks a systematic and comprehensive overview. In this paper, the authors summarize the articles related to weak signal recognition and evolutionary analysis, in an attempt to make contributions to relevant research.

The authors develop a systematic overview framework based on the most classical three-dimensional space model of weak signals. Framework comprehensively summarizes the current research insights and knowledge from three dimensions of research field, identification methods and interpretation methods.

The research results show that it is necessary to improve the automation level in the process of weak signal recognition and analysis and transfer valuable human resources to the decision-making stage. In addition, it is necessary to coordinate multiple types of data sources, expand research subfields and optimize weak signal recognition and interpretation methods, with a view to expanding weak signal future research, making theoretical and practical contributions to enterprise foresight, and providing reference for the government to establish weak signal technology monitoring, evaluation and early warning mechanisms.

The authors develop a systematic overview framework based on the most classical three-dimensional space model of weak signals. It comprehensively summarizes the current research insights and knowledge from three dimensions of research field, identification methods and interpretation methods.

This study aims to investigate the effect of financial satisfaction (FS) on customer loyalty in the banking industry.

The study followed an explanatory research design using responses from 334 respondents from commercial banks in Tanzania. A stepwise regression analysis was used to validate the relevance of the study model.

The results indicate a positive and statistically significant association between customer loyalty and FS with levels of assets (β = 0.598, p < 0.001), savings (β = 0.186, p < 0.001) and debts (β = 0.065, p < 0.001). Of the three dimensions of FS, the level of assets had the strongest contribution to customer loyalty, followed by the level of savings and debts, in that order.

The study used a model of FS that was linked to customer loyalty in the Tanzanian banking industry. It is recommended that the model be tested in other environments to increase the generalizability of the findings.

This study provides an alternative way for banks to strengthen customer loyalty by enhancing FS.

The FS model (Joo and Grable, 2004) and the social exchange theory (Blau, 1964) are used in this study to propose a model of customer loyalty in the banking industry. Customer loyalty and FS have not been connected in prior studies.