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The purpose of this paper is to examine a Chinese indigenous concept of organizational ownership behavior (OOB) as an aspect of employee suzhi in relation to organizational citizenship behavior (OCB) in the Western context.

A content analysis based on a review of related research in Western mainstream and Chinese domestic literature is conducted.

Suzhi at the organizational level can be linked to the construct of OCB. In Chinese organizations, a relevant concept to OCB can be better understood as OOB to capture the sociopolitical and cultural context unique to Chinese organizations. The dimensional structure of OOB is presented to differentiate it from OCB which is popular in the Western context.

The identified construct of OOB offers important implications for indigenous Chinese management research and human resources management (HRM) practice. OOB, based on Chinese management practice, can better conform to China’s unique historical and cultural context and management practices. This concept varies distinctively from Western OCB in terms of its connotation and dimensions.

The concept of OOB as an indigenous employee organizational behavior in the Chinese context is conceptualized. The paper differentiates the OOB construct from OCB and presents an initial set of six dimensions of OOB for future research.

The purpose of this paper is to examine the indigenous Chinese concept of suzhi (素质) with the aim of furthering the development of Chinese human resource management (HRM) research and practice.

An extensive review of the literature on suzhi, published in the West, as well as in China, is the basis for proffering an organizational-level conceptualization of suzhi in the Chinese context.

Instead of understanding it as a free-floating signifier, we argue that suzhi can be considered as a criterion-based framework for HRM research and practice. Suzhi research is classified into two major sources – indigenous Chinese and indigenized Western constructs. We further make a distinction between intrinsic and extrinsic suzhi, and analyze a popular set of suzhi criteria, considering de (morality) and cai (talent), while focusing on de in HRM selection (德才兼备, 以德为先). As multilevel and multidimensional framework, suzhi criteria may form different gestalts in different organizations and industries.

From a social cultural and historical perspective, HRM research that incorporates a combination of indigenous and indigenized suzhi characteristics may receive better acceptance by individuals, organizations and the society in the Chinese context. Accordingly, the reconstruction of suzhi into manageable and measurable dimensions can be undertaken for more effective HRM practice in the Chinese context.

The HRM literature is advanced by linking the indigenous suzhi discourse to Chinese indigenous HRM research and practice.

Food waste in the hospitality sector has emerged as a global concern. Various technology-driven online food services such as the food delivery apps (FDA) contribute to hospitality food waste. FDA users might behave irresponsibly by ordering more foods than required which may lead to food waste generation. To date, limited studies have been attempted to understand how consumers’ over-ordering behavior through FDA result in hospitality food waste.

The authors used partial least squares structural equation modeling (PLS-SEM) to analyze survey data from 248 FDA users.

The results indicated that perceived convenience and trust positively influence consumers' attitude toward FDA, which in turn promotes over-ordering behavior. Interestingly, the anticipated positive relationship between price advantage and attitude toward FDA was not supported by the data. Furthermore, the authors confirmed that over-ordering behavior contributes to food waste, an outcome that has crucial implications for both the hospitality sector and sustainability efforts.

The current study employs the stimulus-organism-behavior-consequence (SOBC) theory to investigate the catalysts and consequences of over-ordering behavior via FDA. This study thus highlights the importance of the SOBC model in understanding consumer behavior.

NOW, WITH THE AID of the European Community, the Equal Pay Commission and who knows who else, an Industrial Tribunal has smashed poor old Euclid right over his head. They have proved to their own satisfaction (if to nobody else's) that things which are unequal to any other thing are quite definitely equal to each other.

Data envelopment analysis (DEA) has wide applications in the agricultural sector to evaluate the efficiency with crisp input and output data. However, in agricultural production, impreciseness and uncertainty in data are common. As a result, the data obtained from farmers vary. This impreciseness in crisp data can be represented in fuzzy sets. This paper aims to employ a combination of fuzzy data envelopment analysis (FDEA) approach to yield crisp DEA efficiency values by converting the fuzzy DEA model into a linear programming problem and machine learning algorithms for better evaluation and prediction of the variables affecting the farm efficiency.

DEA applications are focused on the use of a common two-step approach to find crucial factors that affect efficiency. It is important to identify impactful variables for minimizing production adversities. In this study, first, FDEA was applied for efficiency estimation and ranking of the paddy growers. Second, the support vector machine (SVM) and random forest (RF) were used for identifying the key leading factors in efficiency prediction.

The proposed research was conducted with 450 paddy growers. In comparison to the general DEA approach, the FDEA model evaluates fuzzy DEA efficiency giving the user the flexibility to measure the performance at different possibility levels.

The use of machine learning applications introduces advanced strategies and important factors influencing agricultural production, which may help future research in farms' performance.

The purpose of this study is to develop a method to assess social performance. Traditionally, environment, social and governance (ESG) rating providers use subjectively weighted arithmetic averages to combine a set of social performance (SP) indicators into one single rating. To overcome this problem, this study investigates the preconditions for a new methodology for rating the SP component of the ESG by applying machine learning (ML) and artificial intelligence (AI) anchored to social controversies.

This study proposes the use of a data-driven rating methodology that derives the relative importance of SP features from their contribution to the prediction of social controversies. The authors use the proposed methodology to solve the weighting problem with overall ESG ratings and further investigate whether prediction is possible.

The authors find that ML models are able to predict controversies with high predictive performance and validity. The findings indicate that the weighting problem with the ESG ratings can be addressed with a data-driven approach. The decisive prerequisite, however, for the proposed rating methodology is that social controversies are predicted by a broad set of SP indicators. The results also suggest that predictively valid ratings can be developed with this ML-based AI method.

This study offers practical solutions to ESG rating problems that have implications for investors, ESG raters and socially responsible investments.

The proposed ML-based AI method can help to achieve better ESG ratings, which will in turn help to improve SP, which has implications for organizations and societies through sustainable development.

To the best of the authors’ knowledge, this research is one of the first studies that offers a unique method to address the ESG rating problem and improve sustainability by focusing on SP indicators.