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Ratees' reactions to performance appraisal (PA) system suggest how effective the system is. However, there is less clarity about those different reactions that good versus poor performing ratees show vis-à-vis their performance appraisals. This paper seeks to examine the possible PA responses (PA fairness and PA satisfaction) of the ratees for the cases where they receive equitable versus equal performance-based rewards and punishments.

Two studies were designed. Study 1 was a scenario-based experiment in Pakistan (N = 100 students) and Study 2 was based on surveys in Japan (N = 123 employed students) and Pakistan (N = 111 full-time working professionals). Data were analyzed using one-way repeated measures (Study 1) and structural equation modeling (Study 2).

Overall, good performers considered PA fairer and more satisfying under equity than under equality. However, poor performers considered PA fairer under equity than under equality.

The study has value for PA theorists and managers, as it offers: (a) an understanding on the differential effect of equity versus equality, on employees' perceptions of fairness and satisfaction in a PA setting; (b) clarity about the likely disparity between good and poor performers' reactions toward perceived fairness and satisfaction; and, (c) ratee reactions from both organizational and social perspectives contributing to the philosophical debate questioning whether both distributive fairness and retributive fairness should operate under similar or different normative principles.

Ferrofluids are aqueous or non-aqueous solutions with colloidal particles of iron oxide nanoparticles with high magnetic characteristics. Their magnetic characteristics enable them to be controlled and manipulated when ferrofluids are exposed to magnetic fields. This study aims to inspect the features of unsteady stagnation point flow (SPF) and heat flux from the surface by incorporating ferromagnetic particles through a special kind of second-grade fluid (SGF) across a movable sheet with a nonlinear heat source/sink and magnetic field effect. The mass suction/injection and stretching/shrinking boundary conditions are also inspected to calculate the fine points of the features of multiple solutions.

The leading equations that govern the ferrofluid flow are reduced to a group of ordinary differential equations by applying similarity variables. The converted equations are numerically solved through the bvp4c solver. Afterward, study and discussion are carried out to examine the different physical parameters of the characteristics of nanofluid flow and thermal properties.

Multiple solutions are revealed to happen for situations of unsteadiness, shrinking as well as stretching sheets. Greater suction slows the separation of the boundary layers and causes the critical values to expand. The region where the multiple solutions appear is observed to expand with increasing values of the magnetic, non-Newtonian and suction parameters. Moreover, the fluid velocity significantly uplifts while the temperature declines due to the suction parameter.

The novelty of the work is to deliberate the impact of mass suction/injection on the unsteady SPF through the special second-grade ferrofluids across a movable sheet with an erratic heat source/sink. The confirmed results provide a very good consistency with the accepted papers. Previous studies have not yet fully explored the entire analysis of the proposed model.

The motivation for this study is to understand the stressful situations leading to great resignation and evaluate the cognitions of psychological attachment (PA) and organizational attractiveness (OA) to mitigate this crisis, using the attachment theory as the theoretical basis.

A cross-sectional study was conducted on individuals employed in Indian organizations (Nifty 50) to identify the most impactful cognitions underlying the dynamics between person–job fit (P-J fit) and the intention to stay (ITS).

This study highlighted that a serial mediation relationship between PA (specifically “internalization”) and OA is influenced by the P-J “needs–supplies” fit, particularly during extraordinarily stressful times. Managers must re-emphasize PA and OA as core organizational resources that must be prioritized, maintained and refined to reinforce employees' intent to stay in their organizations.

No research has studied P-J fit, PA, OA, underpinned by the attachment theory to reinforce the ITS given the context of the great resignation triggered by the pandemic's extraordinarily stressful situation.

This study investigates how the interrelated elements of organizational roles – activities, motives, resources and relationships – are mobilized to construct a code of conduct for the proxy advisory (PA) industry in Europe.

This qualitative study uses archival documents from three consecutive regulatory consultations and 16 interviews with key stakeholders. It analyzes how different stakeholder groups (i.e. PA firms, investors, issuers and the regulator) perceive and mobilize the elements of PA firms’ role to construct the accountability regime’s boundaries (accountability problem and action, and users and providers of accounts).

This study shows how PA firms, investors, issuers and the regulator refer to the perceived motives behind PA firms’ activities to construct an accountability problem. The regulator accepted the motives of an information intermediary for PA firms’ role and required PA firms to develop a corresponding accountability action: a code of conduct. PA firms involved in developing the code of conduct formalized who is accountable to whom by aligning this accepted motive with their activities, relationships, and resources into a common role.

The study highlights how aligning role elements to reflect PA firms’ common roles enables the construction of an accountability regime that stakeholders accept as a means of regulation. Analyzing the role elements offers insights into the development and functioning of accountability regimes that rely on self-regulation. We also highlight the role of smaller regional firms in helping shape transnational accountability regimes.

Genre classification of songs based on lyrics is a challenging task even for humans, however, state-of-the-art natural language processing has recently offered advanced solutions to this task. The purpose of this study is to advance the understanding and application of natural language processing and deep learning in the domain of music genre classification, while also contributing to the broader themes of global knowledge and communication, and sustainable preservation of cultural heritage.

The main contribution of this study is the development and evaluation of various machine and deep learning models for song genre classification. Additionally, we investigated the effect of different word embeddings, including Global Vectors for Word Representation (GloVe) and Word2Vec, on the classification performance. The tested models range from benchmarks such as logistic regression, support vector machine and random forest, to more complex neural network architectures and transformer-based models, such as recurrent neural network, long short-term memory, bidirectional long short-term memory and bidirectional encoder representations from transformers (BERT).

The authors conducted experiments on both English and multilingual data sets for genre classification. The results show that the BERT model achieved the best accuracy on the English data set, whereas cross-lingual language model pretraining based on RoBERTa (XLM-RoBERTa) performed the best on the multilingual data set. This study found that songs in the metal genre were the most accurately labeled, as their text style and topics were the most distinct from other genres. On the contrary, songs from the pop and rock genres were more challenging to differentiate. This study also compared the impact of different word embeddings on the classification task and found that models with GloVe word embeddings outperformed Word2Vec and the learning embedding layer.

This study presents the implementation, testing and comparison of various machine and deep learning models for genre classification. The results demonstrate that transformer models, including BERT, robustly optimized BERT pretraining approach, distilled bidirectional encoder representations from transformers, bidirectional and auto-regressive transformers and XLM-RoBERTa, outperformed other models.

This study aims to investigate the effect of monetary policy on housing prices for US economy. It specifically examines whether nominal or real interest rates are the key drivers behind fluctuations in housing prices in US.

Monthly data from January 1991 to July 2023 and various appropriate analytical tools such as unit root tests, Johansen’s cointegration test, vector error correction model (VECM), impulse response function and Granger causality test were applied for the data analysis.

The Johansen cointegration findings reveal the presence of a long-term relationship among the variables. VECM results indicate a negative correlation between nominal and real interest rates and housing prices in both the short and long terms, suggesting that a strict monetary policy can help in controlling the housing price increase in the USA. However, housing prices are more responsive to changes in nominal interest rates than to real interest rates. Additionally, the study reveals that the COVID-19 pandemic contributed to the upsurge in housing prices in the USA.

This study contributes by examining the role that nominal or real interest rates play in shaping housing prices in the USA. Moreover, given the recent significant upsurge in housing prices, this study presents a unique opportunity to investigate whether these price increases are influenced by the Federal Reserve's monetary policy decisions regarding nominal or real interest rates. Additionally, using monthly data, this study provides a deeper understanding of the fluctuations in housing prices and their connection to monetary policy tools.

The purpose of this study is to analyze the nonlinear relationship between electricity consumption (EC) and electricity transmission losses (ETL) in Ghana. Also, we examined how ETL moderate the effect of EC on economic growth in Ghana from 1980 to 2021.

We used timeseries data from 1980 to 2021 within an autoregressive distributed lag framework to analyze the links among ETL, EC and economic growth in Ghana.

Findings show the existence of an asymmetric long-run relationship between EC and ETL. Also, the negative effects of ETL on EC are bigger in the long run. In addition, ETL and EC combine to reduce economic growth, in the long run, providing evidence for the energy-led growth theory in Ghana. Population and inflation were also found to have a significant effect on economic growth in Ghana.

We examined the nonlinear nexus of EC and ETL, which extant studies have ignored in discussing the link between EC and economic growth. Again, we showed that ETL reduces EC causing a reduction in economic growth.

Needs for information literacy, disparities in society, bridging digital divides, richness of information sources in electronic (e-)environments and the value of dictionaries have often been propagated. To improve information sources and information literacy training, information behaviour must be understood (i.e. all information activities). This paper conceptualises new opportunities for information sources (e.g. electronic dictionaries) to all society sectors, dictionary literacy and research lenses such as lexicography to supplement information literacy and behaviour research.

A scoping review of information literacy and behaviour, lexicography and dictionary literature grounds the conceptualisation of dictionary literacy, its alignment with information literacy, information activities and information behaviour and lexicography as additional research lens.

Research lenses must acknowledge dictionary use in e-environments, information activities and skills, meanings of information and dictionary literacy, the value of e-dictionaries, alignment with information behaviour research that guides the development of information sources and interdisciplinary research from, e.g. lexicography – thus contextualisation.

Research implications – information behaviour and information literacy research can be enriched by lexicography as research lens. Further conceptualisation could align information behaviour, information literacy and dictionary literacy.

Dictionary training, aligned with information literacy training, can be informed by this paper.

The value of dictionary literacy for all sectors of societies can be improved.

Large bodies of literature on information behaviour and lexicography individually do not cover combined insights from both.

This study develops a model and algorithm to solve the decentralized resource-constrained multi-project scheduling problem (DRCMPSP) and provides a suitable priority rule (PR) for coordinating global resource conflicts among multiple projects.

This study addresses the DRCMPSP, which respects the information privacy requirements of project agents; that is, there is no single manager centrally in charge of generating multi-project scheduling. Accordingly, a three-stage model was proposed for the decentralized management of multiple projects. To solve this model, a three-stage solution approach with a repeated negotiation mechanism was proposed.

The experimental results obtained using the Multi-Project Scheduling Problem LIBrary confirm that our approach outperforms existing methods, regardless of the average utilization factor (AUF). Comparative analysis revealed that delaying activities in the lower project makespan produces a lower average project delay. Furthermore, the new PR LMS performed better in problem subsets with AUF < 1 and large-scale subsets with AUF > 1.

A solution approach with a repeated-negotiation mechanism suitable for the DRCMPSP and a new PR for coordinating global resource allocation are proposed.

This study aims at investigating how hybrid intelligence might enhance employee engagement in breakthrough innovation. Specifically, it empirically examines the mediating role of self-extinction and moderating role of social intelligence.

This study, using the lens of socio-technical system (STS) theory, collected data from 317 employees through cross-sectional survey. The hypotheses were tested using MPlus 8.3 by applying Structural Equation Modelling (SEM).

The results support the proposed model, suggesting that hybrid intelligence fosters employees' breakthrough innovation engagement and such a relationship is fully mediated by self-extinction. Besides, the findings provide support for the positive moderating impact of social intelligence on such indirect relationships in a way that high social intelligence will further strengthen the relationship.

As a pioneering contribution, the study uncovers the social mechanism that underlies hybrid intelligence–breakthrough innovation engagement relationship via self-extinction. The research suggests managers leveraging employees' social intelligence for playing a critical role in countering the negative impact of self-extinction by enhancing the employees' engagement in the breakthrough innovation process.