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The purpose of this study is to address United Nations’ sustainable development goals (SDGs) 8 and 9 from a service perspective. SDG 8 is a call to improve the dignity of service work by enhancing wages, working conditions and development opportunities while SDG 9 calls upon nations to construct resilient infrastructures, promote inclusivity and sustainability and foster innovation.

This study uses a bibliometric review to extract important themes from a variety of scholarly journals.

Researchers tend to investigate policy-level topics, such as national and international standards related to working conditions, while ignoring the experiences or well-being of workers occupying marginalized and low-opportunity roles in service organizations. Service researchers, educators and practitioners must collaborate to improve the state of service industries by conducting participatory action research, promoting grassroots organizing/advocacy, implementing digitized customer service and addressing workforce soft skills deficiencies.

The authors consider how service work can be transformed into respectable employment and present four specific ways nations can enhance their service industries.

Economic planners can view SDGs 8 and 9 as a framework for understanding and promoting the well-being of service employees and accelerating the productivity and innovation levels of the service sector.

The United Nations’ SDGs are examined from a services perspective, which increases their significance in service-dominated economies.

Following the surge of documents laying out organizations' ethical principles for their use of artificial intelligence (AI), there is a growing demand for translating ethical principles to practice through AI governance (AIG). AIG has emerged as a rapidly growing, yet fragmented, research area. This paper synthesizes the organizational AIG literature by outlining research themes and knowledge gaps as well as putting forward future agendas.

The authors undertake a systematic literature review on AIG, addressing the current state of its conceptualization and suggesting future directions for AIG scholarship and practice. The review protocol was developed following recommended guidelines for systematic reviews and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).

The results of the authors’ review confirmed the assumption that AIG is an emerging research topic with few explicit definitions. Moreover, the authors’ review identified four themes in the AIG literature: technology, stakeholders and context, regulation and processes. The central knowledge gaps revealed were the limited understanding of AIG implementation, lack of attention to the AIG context, uncertain effectiveness of ethical principles and regulation, and insufficient operationalization of AIG processes. To address these gaps, the authors present four future AIG agendas: technical, stakeholder and contextual, regulatory, and process. Going forward, the authors propose focused empirical research on organizational AIG processes, the establishment of an AI oversight unit and collaborative governance as a research approach.

To address the identified knowledge gaps, the authors present the following working definition of AIG: AI governance is a system of rules, practices and processes employed to ensure an organization's use of AI technologies aligns with its strategies, objectives, and values, complete with legal requirements, ethical principles and the requirements set by stakeholders. Going forward, the authors propose focused empirical research on organizational AIG processes, the establishment of an AI oversight unit and collaborative governance as a research approach.

For practitioners, the authors highlight training and awareness, stakeholder management and the crucial role of organizational culture, including senior management commitment.

For society, the authors review elucidates the multitude of stakeholders involved in AI governance activities and complexities related to balancing the needs of different stakeholders.

By delineating the AIG concept and the associated research themes, knowledge gaps and future agendas, the authors review builds a foundation for organizational AIG research, calling for broad contextual investigations and a deep understanding of AIG mechanisms. For practitioners, the authors highlight training and awareness, stakeholder management and the crucial role of organizational culture, including senior management commitment.

This study aimed at investigating employee training, employee participation and organizational commitment (OC) and the moderating effect of workplace ostracism among bank employees.

The study used a descriptive and cross-sectional design with the aid of a standard scale constructed into a questionnaire. Cluster, convenience and simple random sampling techniques were used to select 1,067 respondents, of which 870 were deemed fit for the study. The theories underpinning the study were the social exchange theory (SET) and social identity theory (SIT). Four hypotheses were developed and tested using hierarchical multiple regression analysis, and moderation using PROCESS macro.

The study found that employee training and employee participation had a significant positive relationship with organizational commitment, while organizational ostracism had a significant but negative relationship with organizational commitment among bank employees. The study also found that workplace ostracism moderated the relationship between organizational climate and organizational commitment The study recommended that organizational commitment requires management training their workforce, allowing employee participation in decisions, and minimizing or outrightly eradicating the practice of organizational ostracism. It is, therefore, concluded that workers place great value on training and participation in decision-making and frown at organizational ostracism.

This paper fills in the gaps left by the paucity of empirical investigation of the moderating role that workplace ostracism plays between employee training, employee participation and organizational commitment – a feat that is lacking in developing countries. It serves as a reminder to management to prevent or entirely eliminate workplace ostracism to allay an employee's impression of being a threat to an organization when commitment is low.

No systematic review has previously been dedicated to comprehensively investigate predictors of well-being and ill-being in working contexts. Empirical studies have vastly associated well-being as the result of autonomous motivation and basic psychological needs satisfaction, while frustration results in ill-being. The purpose of this study is to integrate the variables identified in empirical studies associated with the occurrence of the phenomena, individual/organizational features and consequences associated with workers' well-being/ill-being.

This systematic review includes 44 empirical studies published up to February 2021. Findings are summarized based on quantitative analysis of the evidence.

Results reinforce the role of self-determined motivation and needs satisfaction in promoting well-being, while amotivation and needs frustration led to ill-being. Besides, they indicate that ill-being can both lead to negative consequences and diminish positive work outcomes. Findings also revealed that: integrated motivation does not seem to be empirically distinct from intrinsic and identified motivation in promoting well-being; introjected motivated behaviors may be less harmful to psychological health than externally oriented ones; the relationship between external motivation and well-being/ill-being requires prospective investigations; and amotivation seems to have a detrimental effect in workers' psychological health.

Results provide practical information for HRM practitioners to design work environments and practices that promote employees' psychological health.

An unprecedented framework that aggregates empirical findings regarding the antecedents, predictors and consequences of ill-being/well-being in working contexts is presented.

This study aims to predict overnight stays in Italy at tourist accommodation facilities through a nonlinear, single factor, stochastic model called CIR#. The contribution of this study is twofold: in terms of forecast accuracy and in terms of parsimony (both from the perspective of the data and the complexity of the modeling), especially when a regular pattern in the time series is disrupted. This study shows that the CIR# not only performs better than the considered baseline models but also has a much lower error than other additional models or approaches reported in the literature.

Typically, tourism demand tends to follow regular trends, such as low and high seasons on a quarterly/monthly level and weekends and holidays on a daily level. The data set consists of nights spent in Italy at tourist accommodation establishments as collected on a monthly basis by Eurostat before and during the COVID-19 pandemic breaking regular patterns.

Traditional tourism demand forecasting models may face challenges when massive amounts of search intensity indices are adopted as tourism demand indicators. In addition, given the importance of accurate forecasts, many studies have proposed novel hybrid models or used various combinations of methods. Thus, although there are clear benefits in adopting more complex approaches, the risk is that of dealing with unwieldy models. To demonstrate how this approach can be fruitfully extended to tourism, the accuracy of the CIR# is tested by using standard metrics such as root mean squared errors, mean absolute errors, mean absolute percentage error or average relative mean squared error.

The CIR# model is notably simpler than other models found in literature and does not rely on black box techniques such as those used in neural network (NN) or data science-based models. The carried analysis suggests that the CIR# model outperforms other reference predictions in terms of statistical significance of the error.

The proposed model stands out for being a viable option to the Holt–Winters (HW) model, particularly when dealing with irregular data.

The proposed model has demonstrated superiority even when compared to other models in the literature, and it can be especially useful for tourism stakeholders when making decisions in the presence of disruptions in data patterns.

The novelty lies in the fact that the proposed model is a valid alternative to the HW, especially when the data are not regular. In addition, compared to many existing models in the literature, the CIR# model is notably simpler and more transparent, avoiding the “black box” nature of NN and data science-based models.

一般来说, 旅游需求往往遵循规律的趋势, 例如季度/月的淡季和旺季, 以及日常的周末和假期。该数据集包括欧盟统计局在打破常规模式的2019冠状病毒病大流行之前和期间每月收集的在意大利旅游住宿设施度过的夜晚。

本研究旨在通过一个名为cir#的非线性单因素随机模型来预测意大利游客住宿设施的过夜住宿情况。这项研究的贡献是双重的:在预测准确性方面和在简洁方面(从数据和建模复杂性的角度来看), 特别是当时间序列中的规则模式被打乱时。我们表明, cir#不仅比考虑的基线模型表现更好, 而且比文献中报告的其他模型或方法具有更低的误差。

当大量搜索强度指标被作为旅游需求指标时, 传统的旅游需求预测模型将面临挑战。此外, 鉴于准确预测的重要性, 许多研究提出了新的混合模型或使用各种方法的组合。因此, 尽管采用更复杂的方法有明显的好处, 但风险在于处理难使用的模型。为了证明这种方法能有效地扩展到旅游业, 使用RMSE、MAE、MAPE或AvgReIMSE等标准指标来测试cir#的准确性。

cir#模型明显比文献中发现的其他模型简单, 并且不依赖于黑盒技术, 例如在神经网络或基于数据科学的模型中使用的技术。所进行的分析表明, cir#模型在误差的统计显著性方面优于其他参考预测。

这个模型作为Holt-Winters模型的一个拟议模型, 特别是在处理不规则数据时。

即使与文献中的其他模型相比, 所提出的模型也显示出优越性, 并且在数据模式中断时对旅游利益相关者做出决策特别有用。

创新之处在于所提出的模型是Holt-Winters模型的有效替代方案, 特别是当数据不规律时。此外, 与文献中的许多现有模型相比, cir#模型明显更简单、更透明, 避免了神经网络和基于数据科学的模型的“黑箱”性质。

Normalmente, la demanda turística tiende a seguir tendencias regulares, como temporadas altas y bajas a nivel trimestral/mensual y fines de semana y festivos a nivel diario. El conjunto de datos consiste en las pernoctaciones en Italia en establecimientos de alojamiento turístico recogidas mensualmente por Eurostat antes y durante la pandemia de COVID-19, rompiendo los patrones regulares.

El presente estudio pretende predecir las pernoctaciones en Italia en establecimientos de alojamiento turístico mediante un modelo estocástico no lineal de un solo factor denominado CIR#. La contribución de este estudio es doble: en términos de precisión de la predicción y en términos de parsimonia (tanto desde la perspectiva de los datos como de la complejidad de la modelización), especialmente cuando un patrón regular en la serie temporal se ve interrumpido. Demostramos que el CIR# no sólo aplica mejor que los modelos de referencia considerados, sino que también tiene un error mucho menor que otros modelos o enfoques adicionales de los que se informa en la literatura.

Los modelos tradicionales de previsión de la demanda turística pueden enfrentarse a desafíos cuando se adoptan cantidades masivas de índices de intensidad de búsqueda como indicadores de la demanda turística. Además, dada la importancia de unas previsiones precisas, muchos estudios han propuesto modelos híbridos novedosos o han utilizado diversas combinaciones de métodos. Así pues, aunque la adopción de enfoques más complejos presenta ventajas evidentes, el riesgo es el de enfrentarse a modelos poco manejables. Para demostrar cómo este enfoque puede extenderse de forma fructífera al turismo, se comprueba la precisión del CIR# utilizando métricas estándar como RMSE, MAE, MAPE o AvgReIMSE.

El modelo CIR# es notablemente más sencillo que otros modelos encontrados en la literatura y no se basa en técnicas de caja negra como las utilizadas en los modelos basados en redes neuronales o en la ciencia de datos. El análisis realizado sugiere que el modelo CIR# supera a otras predicciones de referencia en términos de significación estadística del error.

El modelo propuesto destaca por ser una opción viable al modelo Holt-Winters, sobre todo cuando se trata de datos irregulares.

El modelo propuesto ha demostrado su superioridad incluso cuando se compara con otros modelos de la bibliografía, y puede ser especialmente útil para los agentes del sector turístico a la hora de tomar decisiones cuando se producen alteraciones en los patrones de datos.

La novedad radica en que el modelo propuesto es una alternativa válida al Holt-Winters especialmente cuando los datos no son regulares. Además, en comparación con muchos modelos existentes en la literatura, el modelo CIR# es notablemente más sencillo y transparente, evitando la naturaleza de “caja negra” de los modelos basados en redes neuronales y en ciencia de datos.

Drawing on social exchange theory and the expectancy–value model, this study has two objectives. First, it sought to explore the mediating role of nurses’ self-concept and affective commitment between perceived organizational support (POS) and three different targets (organization, co-workers and patients) of organizational citizenship behavior (OCB). Second, it aimed to develop a better understanding of how nurses´ self-concept and affective commitment mediate the influence of POS on OCB directed toward different targets through sequential mediation.

A cross-sectional study was conducted with 229 nurses. This sample was representative of the nursing population based on several demographic characteristics. Data analysis was performed using partial least squares analysis.

The study revealed that nurses´ self-concept plays a mediating role between POS and OCB directed toward the organization, co-workers and patients, while affective commitment has a mediating effect between POS and OCB directed toward the organization and co-workers. Finally, the indirect influence of POS on OCB through nurses´ self-concept and affective commitment was significant only at the organizational level.

This study contributes to the extant literature by identifying the mediating role of nurses´ self-concept among social exchange constructs such as POS, affective commitment and OCB directed toward different targets.

Small- and medium-sized enterprises (SMEs) often lack adequate accounting systems and may even fail because of accounting inefficiencies. Indeed, accounting can mitigate the course of a crisis and support a troubled SME’s turnaround. Its impact on reorganization success, however, has scarcely been researched so far. Therefore, this paper aims to examine the effects of several accounting parameters, namely, the quality of accounting systems, quality of early warning systems, formal planning, the standard of financial accounting and reorganization planning on the short- and long-term success of court-supervised reorganization.

The impact of accounting on reorganization success is investigated in a sample of all SME bankruptcy cases with ten or more employees (n = 117) in Upper Austria in 2012 including data for short-term survival (in 2016) and long-term survival (in 2019).

This study found evidence that the general quality of accounting systems, the quality of early warning systems and written reorganization plans positively influence the outcomes of the analyzed court-supervised reorganizations of SMEs. In particular, the existence of a reorganization plan significantly increases the short- and long-term reorganization success by ensuring the efficient and effective use of resources in the reorganization process.

This study should increase the awareness of SMEs’ owner managers, consultants, creditors and legislators for the importance of accounting in the context of reorganization. The fact that the effect of accounting on reorganization success is less pronounced in the long-term view indicates the necessity of increasing the strategic focus in SMEs’ accounting instruments.

This study provides new evidence on the impact of specific accounting parameters on the short- and long-term success of the court-supervised reorganization of SMEs. Furthermore, this study points out the high relevance of reorganization plans for SMEs.

The study aims to analyze for the Spanish context the influence of the involvement of several generations in the firm's management on family firm internationalization. The authors also respond to the call in the literature to consider the influence of SEW on family firm internationalizations by analyzing the moderating effect of the importance family managers attach to each of the socioemotional wealth (SEW) dimensions – enrichment, continuity and prominence on the relationship between multiple generations involved in management and family firm internationalization.

The information was obtained by means of a questionnaire sent to the CEOs of family businesses. The authors’ sample consists of 147 Spanish family firms.

The authors find that the involvement of multiple generations in management is positively related to the internationalization of family firms. Furthermore, the importance that family CEOs attribute to the enrichment dimension of SEW reduces the intensity of the effect of the involvement of several generations in management on family firm internationalization.

The authors’ results, for the Spanish context, complement previous studies (Meneses et al., 2014) showing that the entry of new generations into the family business opens a window of opportunity for the internationalization of the family business. Furthermore, their study shows that the diverse family objectives by CEOs can have different, even conflicting effects on the internationalization decision. These results suggest that the enrichment dimension, which focuses on the short-term family goals may restrain the internationalization of the family business. However, continuity and prominence dimensions, which are related with long term family objectives and jointly enable the fulfillment of nonfamily stakeholders’ objectives, do not influence the internationalization of the family firms analyzed.

This paper aims to analyze factors based on organizational knowledge management (KM; transactional memory systems and knowledge-oriented leadership [K-OL]) that help firms to mitigate conflicts based on task management at work, with the aim to improve their innovation capabilities (IC). The knowledge-based view of the firm, conflict management theory and cognitive collective engagement theory have been used to build a model of relationships that connects the development of positive KM contexts and management of dysfunctional conflict with IC improvement.

Data survey collected from inland hotel establishments in Spain is used to test seven hypotheses by means of structural equations modeling, applying the partial least squares technique. Direct, indirect and mediating relationships between variables are examined from the structural path model.

The results confirm that, as expected, IC improve when K-OL and transactive memory systems (TMSs) are properly implemented by hotel establishments, which leads them to reduce negative effects of task management conflict (TMC). Significant direct effects are found between the key variables of the study and also a significant indirect effect between K-OL and IC through TMS reinforcement and the mitigation of TMC.

This paper provides useful ideas for hotel managers about how to improve KM contexts in their establishments while avoiding TMC. Efforts devoted to creating those contexts by hotel establishments are shown to be effective to improve their IC and create competitive advantages.

The analysis of IC improvement by studying TMC mitigation had not been researched to date by the KM literature. The consideration and testing of a model that integrates KM-related tools such as K-OL and TMS to avoid TMC in the hotel industry is the main contribution of this study.