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Skilled workers are crucial for an organization’s success, and managing, retaining and attracting them is vital in long-term. This study aims to explore talent management practices in the Finnish restaurant industry and to align workers' expectations with the real-world experiences of their work to reduce turnover and enhance job satisfaction.

The study adopts a mixed methods approach, including a survey and interviews with workers and managers to gain insights into their expectations and experiences of work. The study considers themes for designing and implementing effective talent management procedures.

This study highlights the importance of employees' experiences of their work conditions, leveraging positive emotions and fair utilization of temporary agency work (TAW). Understanding the different work preferences of generational cohorts and addressing the challenges associated with owner disengagement and TAW can also contribute to attracting and retaining talent in the restaurant industry.

Skilled workers have often been portrayed as targets that need to be managed, with insufficient consideration given to their preferences, needs and expectations. With the findings of this study, companies can establish mutual understanding with their employees and attract diverse talent.

This paper aims to examine how managers make non-generally accepted accounting principles (GAAP) exclusion decisions depending on the regulatory guidance provided and their motivations. Guidance detail is a double-edged sword: resolving uncertainty but risking rule-based compliance over principled judgment.

This paper uses the context of non-GAAP measures in reporting, given the history of Securities and Exchange Commission changes in guidance detail. Drawing on theories of epistemic motivation and process accountability, this paper manipulates the goal of management (informativeness vs. opportunism) and guidance detail to examine effects on management decisions to exclude an ambiguous charge.

The 2×2 between participants experiment with 132 managers reveals that more detailed guidance increases likelihood of exclusion of an ambiguous charge. This paper further finds that this exclusion is more likely when management is given an informativeness goal, a result of a mediating effect of epistemic motivation. However, these findings only hold at low levels of process accountability.

The findings regarding the psychological concepts recognize the influence of perceived decision uncertainty by suggesting how managers respond to the level of regulatory guidance detail, offering regulators and auditors a basis for understanding and anticipating managerial reporting choices. Also, awareness of heightened epistemic motivation under the informativeness goal provides a nuanced practical understanding of non-GAAP decision drivers. Finally, the finding that effects are more pronounced for managers with lower process accountability highlights the significance of organizational accountability structures in guiding managerial choices, which can inform board-level governance and control decisions.

Pragmatically, this paper finds that detailed guidance leads to more appropriate exclusion decisions under a goal of informativeness but finds no such evidence where the goal is opportunism. No prior study has examined how the level of detail in guidance affects managers’ disclosure choices.

The proliferation of Smart Technologies, Artificial Intelligence, Robotics, and Algorithms (STARA) in the tourism and hospitality (T&H) industry has been shown to trigger negative employee behaviour associated with awareness of these technologies. This study expands the existing literature on technology and organisational behaviour from the perspective of hospitality employees based on a dual appraisal of STARA awareness.

The sample included 324 Turkish hotel employees, and the analysis was conducted through partial least squares structural equation modelling (PLS-SEM).

Results revealed the negative impact of STARA awareness on job outcomes among hotel employees. STARA awareness negatively affected the psychological relationship between individuals and organisations. Despite these findings, when perceived as a companion that could support employees' career development, STARA technologies increased employees’ work engagement.

Organisational support plays a critical role in decreasing employees’ fear of technological unemployment. Hotel managers can increase employees' commitment to the organisation by helping them feel valued and supported. However, the support they give to employees will not always work. While more optimistic employees are warmer toward the organisation, depressed employees may not recognise the value the organisation gives them. Hence, managers should strive to offer personalised assistance that addresses the distinct requirements and worries of individual employees.

The research provides a two-sided assessment, extending the one-sided negative perceptions of employees towards STARA technologies. The research is grounded in the Transactional Stress Theory and provides significant implications for the Job Demands-Resources Theory, Person-Organisation Fit Theory, and Self-Determination Theory.

This paper aims to explore factors contributing to music popularity using machine learning approaches.

A dataset comprising 204,853 songs from Spotify was used for analysis. The popularity of a song was predicted using predictive machine learning models, with the results showing the superiority of the random forest model across key performance metrics.

The analysis identifies crucial genre and audio features influencing music popularity. Additionally, genre specific analysis reveals that the impact of music features on music popularity varies across different genres.

The findings offer valuable insights for music artists, digital marketers and music platform researchers to understand and focus on the most impactful music features that drive the success of digital music, to devise more targeted marketing strategies and tactics based on popularity predictions, and more effectively capitalize on popular songs in this digital streaming age.

While previous research has explored different factors that may contribute to the popularity of music, this study makes a pioneering effort as the first to consider the intricate interplay between genre and audio features in predicting digital music popularity.

This study aims to illustrate the mechanisms underlying the effect of stress on flow states in the context of a multilevel organization, in which case employees' perseverative cognition and reactions to challenge–hindrance stressors are affected by leader mindfulness.

Study 1 employed a three-wave time-lag survey, and study 2 conducted a diary study across 10 workdays to replicate the results of study 1. Multilevel structural equation modeling and Monte Carlo simulation were performed using Mplus 8.0 software to test all hypotheses.

Problem-solving pondering transmits the nonlinear effect of challenge stressors on flow, and affective rumination mediates the negative effect of hindrance stressors on flow. Leader mindfulness amplifies the tendency of followers to ruminate on the positive aspects of challenge stressors, consequently increasing their positive reactions and flow. Although leader mindfulness fails to influence followers to ruminate less on hindrance stressors, it negates the harmful effect of affective rumination on the flow experience.

This study is one of the first to examine the associations between stressor types and flow in the workplace. The authors also develop a new theory that highlights the ability of leader mindfulness to shape subordinates' stress, cognitions and reactions through social modeling and the authors identify the boundaries of its beneficial effects.

COVID entered the world in 2019 as a pandemic and the intensity of this health crisis is only increasing in several regions. Therefore, it is critical to study and detect the public's frame of mind, government and economists' perception regarding the COVID crisis, as well as the primary worries that the public has expressed, and how this evolves over time. Responsive measures towards COVID-19 from the Indian economy have been explored as a key objective. Moreover, efforts have been made to explore recovery in India through economists and policymakers. Data have been explored through online interviews of key economists which were published in leading newspapers and covered through media channels such as NDTV, CNBC, etc. Moreover, various newspapers and reports were explored to understand government initiatives to address COVID-19 in India. The study's findings show how essential economic recovery from the second wave is in India, and how it may be achieved by strong fiscal and monetary policies, as well as specific attention to impoverished households, small and micro-businesses and increased employment. The short-term focus of the developing economic strategy must be on giving crisis relief to the most unprotected segments of society since long-term system stimulation is impossible.

The purpose of this study was to investigate restaurant and foodservice workers’ perceptions of working with a service robot and the extent to which the workers’ well-being was impacted by a mandated service robot adoption.

This study used a qualitative methodology where 42 US restaurant and foodservice workers from two organizations were interviewed. The data analyzed generated 1,302 coded segments that clustered into six overarching themes.

The findings from this research revealed that restaurant and foodservice workers who regularly use service robots in the dining room experience a complex set of issues and challenges related to robot reliability, management training and support, leveraging the robot to entertain the customer, feelings of dread, anger and frustration, and indications of decreased physical exertion as a proxy for well-being.

As an initial qualitative investigation, the results of this study can be used as a starting point for quantitative investigations, as well as informing restaurant and foodservice industry stakeholders as to the best practices for a comprehensive and successful service robot adoption and integration.

This research presents an intersection between service robot technology acceptance with worker well-being using a broad range of frameworks including National Institute for Occupational Safety and Health’s Future of Work, SERVQUAL and technology acceptance models to gain a deep and rich set of service worker perspectives.

本研究旨在调查餐厅和餐饮服务工作人员对与服务机器人合作的感知, 以及服务机器人采用对工作人员福祉的影响程度。

本研究采用定性方法, 对来自两家机构的42名美国餐厅和餐饮服务工作人员进行了访谈。分析的数据生成了1,302个编码段, 分为六个总体主题。

本研究的发现显示, 餐厅和餐饮服务工作人员在餐厅使用服务机器人时经历了一系列与机器人可靠性、管理培训和支持、利用机器人娱乐顾客、恐惧、愤怒和挫折感、以及作为福祉代理的身体活动减少相关的复杂问题和挑战。

作为初步的定性调查, 本研究的结果可作为定量调查的起点, 并向餐饮服务行业的利益相关者提供有关全面和成功采用和整合服务机器人的最佳实践。

本研究将服务机器人技术接受与工作人员福祉相结合, 利用包括NIOSH的未来工作、SERVQUAL和技术接受模型在内的广泛框架, 获得了丰富多样的服务工作者观点。

The Indian pharmaceutical industry currently faces multiple challenges, including rising costs and slowing export growth, which in turn have limited its ability to expand presence in global canvas. Given the nature of sectoral dynamics, a pharmaceutical firm must undertake huge investments in R&D to introduce product innovation, in turn enhancing market share and sustaining profit streams. The development of novel medicines, confirmed by the granting of patent rights, provides a pharma company edge over its competitors. In addition, presence of innovator firms within the industry invigorates the sectoral value chain and raises efficiency. Hence, it is important to analyze whether granting patent rights enhances the exports of pharmaceutical products in the Indian context.

The current study explored this question using a simultaneous-equation framework. Specifically, the authors use the methods developed by Davidson and MacKinnon (1993) and Greene (2003) to obtain heteroscedasticity-consistent estimates. The time-series properties of the data were further probed, and robust estimates were used to test the theory. Methods developed by Baltagi (1981) have been used further to refine the authors’ estimations.

After controlling for relevant variables, it is observed that granting of patents caused a significantly positive impact on pharmaceutical exports. Furthermore, the change in the patent administration regime had a significant impact on patent fillings, which further impacted their exportability. Compared to patents granted patents filed had a higher impact on pharmaceutical exports.

This study attempts to apply the framework developed by Goldstein and Khan (1978) with necessary modifications to suit the context of a developing country. The application of the 3SLS method to estimate the export supply equation for pharmaceutical products is a novel approach to the research question in general and to the Indian context in particular. System autocorrelation and heteroscedasticity tests were performed to refine the results further.

Algorithms are widely used to manage various activities in the gig economy. Online car-hailing platforms, such as Uber and Lyft, are exemplary embodiments of such algorithmic management, where drivers are managed by algorithms for task allocation, work monitoring and performance evaluation. Despite employing substantially, the platforms face the challenge of maintaining and fostering drivers' work engagement. Thus, this study aims to examine how the algorithmic management of online car-hailing platforms affects drivers' work engagement.

Drawing on the transactional theory of stress, the authors examined the effects of algorithmic monitoring and fairness on online car-hailing drivers' work engagement and revealed the mediation effects of challenge-hindrance appraisals. Based on survey data collected from 364 drivers, the authors' hypotheses were examined using partial least squares structural equation modeling (PLS-SEM). The authors also applied path comparison analyses to further compare the effects of algorithmic monitoring and fairness on the two types of appraisals.

This study finds that online car-hailing drivers' challenge-hindrance appraisals mediate the relationship between algorithmic management characteristics and work engagement. Algorithmic monitoring positively affects both challenge and hindrance appraisals in online car-hailing drivers. However, algorithmic fairness promotes challenge appraisal and reduces hindrance appraisal. Consequently, challenge and hindrance appraisals lead to higher and lower work engagement, respectively. Further, the additional path comparison analysis showed that the hindering effect of algorithmic monitoring exceeds its challenging effect, and the challenge-promoting effect of algorithmic fairness is greater than the algorithm's hindrance-reducing effect.

This paper reveals the underlying mechanisms concerning how algorithmic monitoring and fairness affect online car-hailing drivers' work engagement and fills the gap in the research on algorithmic management in the context of online car-hailing platforms. The authors' findings also provide practical guidance for online car-hailing platforms on how to improve the platforms' algorithmic management systems.

Given the workplace’s reinvention to accommodate the global pandemic’s novel conditions, knowledge hiding (KH) behaviour in knowledge-intensive organisations must be examined from a fresh perspective. In this context, the relationship between workplace ostracism (WO) as KH’s antecedent and quiet quitting (QQ) as its consequence is undertaken via the mediating role of KH behaviour among knowledge workers (KWs).

Through stratified sampling, data from 649 KWs is obtained to test the model. Partial least squares structural equation modelling (PLS-SEM) using SMART-PLS 4.0. software establishes a significant influence of WO on KH and QQ. KH significantly mediates the relationship between WO and QQ, highlighting its critical intermediary role PLSPredict evaluates the model’s predictiveness. WO and KH’s effects on QQ are examined using necessity logic by collectively applying PLS-SEM and necessary condition analysis (NCA).

The model wherein WO plays a significant role in increasing KH and QQ, with KH as a partial mediator in the relationship, has high predictive relevance. Moreover, NCA confirms WO as the key predictor variable that provides variance in QQ, followed by KH. The Importance-performance map analysis technique supports the study’s managerial implications.

This study enriches QQ’s emerging literature by empirically identifying its antecedents-WO and KH. Methodologically, this paper gives a model for using PLS-SEM and NCA together in relation to QQ by identifying WO as its necessary condition. Evidence of selected constructs’ interrelationships may help organisations draft leadership programmes to curtail KH and QQ behaviour.