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This paper aims to investigate the role-play of Protean Career Attitude (PCA) and Career Success (CS) in Affective Organizational Commitment (AOC).

A cross-sectional study on 376 employees from 55 hotels in Malaysia were conducted. The co-variance-based structural equation modeling was employed to analyze the data to test the direct and indirect relationships of PCA and CS with AOC.

The findings reveal that self-directed career attitude (SDCA) has a positive direct influence on AOC as well as indirect influence through the mediation of OCS and SCS. However, the value-driven career attitude (VDCA) neither influences AOC nor the OCS.

This is a first paper to body of knowledge in Asian context which identify mediating role of career success (SCA and OCS) to PCA and AOC. The findings of this research are the workplace learning in hospitality management. The authors argue that hotels should not assume spontaneously PCA with diminishing AOC, but rather hotels' attention is required to identify the most important preferences of these butterfly career attitudes such as OCS and SCS. Most importantly the research negates many negative labels of PCA and adds new perception to the contemporary career literature. Higher education institutions, government, and primary, secondary, and post-secondary education departments can play a significant role in developing PCA dispositions like SDCA and VDCA toward career success. Therefore, further study should examine PCA and their relevance to career outcome like job searching and employability of students in Malaysia. The paper is the first, to one's knowledge, to assess organizational commitment with specific measures of PCA. While the results are simple, they refute many stereotypes of the new career and, in that sense, add an important perspective to the career literature.

Predicting highly cited papers can enable an evaluation of the potential of papers and the early detection and determination of academic achievement value. However, most highly cited paper prediction studies consider early citation information, so predicting highly cited papers by publication is challenging. Therefore, the authors propose a method for predicting early highly cited papers based on their own features.

This research analyzed academic papers published in the Journal of the Association for Computing Machinery (ACM) from 2000 to 2013. Five types of features were extracted: paper features, journal features, author features, reference features and semantic features. Subsequently, the authors applied a deep neural network (DNN), support vector machine (SVM), decision tree (DT) and logistic regression (LGR), and they predicted highly cited papers 1–3 years after publication.

Experimental results showed that early highly cited academic papers are predictable when they are first published. The authors’ prediction models showed considerable performance. This study further confirmed that the features of references and authors play an important role in predicting early highly cited papers. In addition, the proportion of high-quality journal references has a more significant impact on prediction.

Based on the available information at the time of publication, this study proposed an effective early highly cited paper prediction model. This study facilitates the early discovery and realization of the value of scientific and technological achievements.

The purpose of this study is to examine the impact of sustainable supply chain management (SSCM) practices on both competitive advantage (CA) and organizational performance (OP) in the manufacturing sector in Ethiopia.

Data for the study were collected from a sample of 221 manufacturing companies operating in the four manufacturing groups/sectors in Ethiopia. In addition, data analysis was performed using the partial least squares method, which is a variance-based Structural Equation Modeling approach in the Smart-PLS software version (SmartPLS 4.0).

Based on the statistical analysis of the collected data, it demonstrates that SSCM has a significant and positive impact on both competitive advantage and organizational performance. Furthermore, statistical findings offer proof of the clear connection between competitive advantage and organizational performance. Moreover, competitive advantage indirectly mediates the relationship between SSCM and OP.

The primary limitation of this research is its reliance on a cross-sectional design. The generalizability of the findings obtained from the present study may be hindered. The variable under investigation in this research assessed organizational performance, a concept that is widely acknowledged to be extremely dynamic.

The study provides managers and researchers with valuable information on Sustainable Supply Chain Management strategies and how they influence competitive advantage and organizational performance in commercial and industrial environments.

This paper adds to the body of knowledge by providing new data and empirical insights into the relationship between SSCM practices and the performance of manufacturing companies in Ethiopia.