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Using role stress theory, this study examines how work connectivity behavior (WCB) blurs the lines between employees' work and personal lives, thereby encouraging procrastination at work (PAW). The study also investigates the importance of role stress and remote work self-efficacy (RWSE) as mediating and moderating factors, respectively.

The study examines the direct and indirect relationships between WCB and PAW using hierarchical regression and data from 415 Chinese teleworkers. RWSE is also estimated as a second-stage moderator.

The findings indicate that WCB has a direct and indirect (via role stress) positive influence on PAW; however, these effects are weaker among employees with higher (vs lower) RWSE.

This study assists managers and organizations in developing more efficient ways of maximizing employee and organizational performance while minimizing the counterproductive behaviors associated with excessive technology use.

By investigating the links between WCB and PAW in the post-pandemic context, this study adds a new perspective on how excessive technology use for work and non-work purposes can be counterproductive.

The purpose of this paper is to investigate the current level of voluntary corporate disclosure in the Egyptian Stock Exchange. In addition, it explores the factors influencing the extensiveness of voluntary disclosure and examines the potential consequences of such disclosure in regards to the phenomenon of earnings management.

A relevant disclosure index to the Egyptian context was adopted to assess the level of voluntary disclosure in the 2010 annual reports of the most actively traded companies listed on the Egyptian Stock Exchange. The relationship between the extent of voluntary disclosure and each specific-related factor was examined using unranked and ranked OLS regression models. Meanwhile, a system of simultaneous equations was performed using a two-stage least squares regression model in order to investigate whether companies with higher levels of voluntary disclosure exhibit lower levels of earnings management practices.

The results indicate that the level of voluntary disclosure is positively responsive to specific corporate attributes, namely, the type of auditing firm and the two industries of Healthcare and Pharmaceuticals, and Chemicals. However, no significant indications were found that firm size, leverage, profitability and liquidity are important determinants of corporate disclosure. Also, the results show no evidence to support the prior anticipation that a higher level of voluntary disclosure reduces the ability of managers to make use of earnings management. On the contrary, it was found that leverage and the tendency of firms to avoid reporting declines in earnings are the main drivers of the phenomenon of earnings management in Egypt.

This paper has important implications for both domestic and overseas investors in Egypt as well as the regulatory authorities in the developing economies.

The main contribution of this paper is its focus on the extent of voluntary disclosure in a developing country such as Egypt, which has a high potential for economic growth in the near future. Besides, this paper is the first to examine the relationship between the level of voluntary disclosure and the phenomenon of earnings management in the Egyptian context.

Kuwait has taken significant steps to reform its corporate governance (CG) by introducing the New Company Law (NCL) in 2013. This study investigates how this reform of CG mechanisms affects the disclosure of future-oriented information. Likewise, it explores how CG mechanisms affect the informativeness of this disclosure.

The sample comprises the nonfinancial firms listed on the Boursa Kuwait from 2014 to 2018. The study uses an automated textual analysis to measure the level of future-oriented disclosure in the annual reports of these firms. The informativeness of disclosure is proxied by firm value at three months of the date of the annual report.

The study finds that Kuwaiti firms with larger board sizes and substantial ownership by institutional investors are less likely to disseminate future-oriented information. Conversely, firms with more independent directors and larger audit committees are more inclined to provide future-oriented disclosure. Furthermore, the disclosure of future-oriented information carries contents that enhance investors' valuations of Kuwaiti firms, especially in firms with fewer institutional ownership and more prominent audit committees.

It focuses on management decisions to disclose information in the annual reports. Examining other channels of disseminating information, such as social media disclosure, provides avenues for future research.

Policy setters in Kuwait should consider the importance of some CG mechanisms to improve the transparency of Kuwaiti firms, as suggested by the NCL. Likewise, investors should rely on such specific CG mechanisms to build their prospects about the firm's value.

Apart from developed countries, the current study is the first evidence on how CG mechanisms could affect the informativeness of future-oriented disclosure in a developing economy. It is also the first to investigate the new CG mechanism introduced by Kuwait NCL in 2013.

The authors investigate the impact of disclosure tones and financial risk on the readability of annual reports in the banking sector. The authors also examine the moderating effect of banks' financial risk on the tone–readability relationship.

This study relies on the agency theory and the social psychology theory to formulate its testable hypotheses and explain the empirical findings. It uses a sample of 390 bank-year observations from banks listed in the Gulf Cooperation Council (GCC) Stock Exchanges during the period 2014–2019. It also employs random effect regressions to analyze the data and to examine the reverse causality/endogeneity in order to obtain robust findings.

This study’s results demonstrate that easy (difficult) to read annual reports is significantly associated with positive (negative) tone. Bank managers characterized as “too positive/optimistic” and banks with higher financial risks publish less readable annual reports. The results also show that the interaction between negative tone and a bank's financial risk is inversely associated with reading difficulty, indicating that managers prepare easy text to clarify causes of their banks’ high risks, yet they communicate this easy text with a negative tone that reflects their feelings/emotions towards the financial risks of their banks.

This study’s findings call for the use of a plain English text that bears a neutral tone and urge financial analysts to go beyond the financial aspects of annual reports. They also stimulate policymakers to draft policies, which ensure the presence of audit committee members who possess a broad expertise to uncover the linguistic issues embedded in the annual reports.

To the best of the authors' knowledge, this is the first study dedicated to exploring the tone–readability association in the GCC's banking sector.

The Sustainable Development Goals (SDGs) framework emerged as a guidepost for the transition to sustainable development. To achieve this transition, companies are encouraged to integrate these goals into their business strategies, processes and corporate reporting cycle. The purpose of this paper is to review and critique the corporate SDGs reporting literature, develop insights into the state of this research field and identify a future research agenda.

Using a structured literature review (SLR) methodology, the paper reviews 65 empirical papers published in this field to identify how the current research is developing, offers a critique and identifies future research avenues to advance this field.

Corporate SDGs reporting is developing as a research area of great importance. The findings reveal that current SDGs reporting literature lacks theorisation, overly focusses on publicly listed companies and succinctly describes organisations’ engagement with the SDGs as superficial. Surprisingly, regions such as North America, the UK and other emerging economies have received less attention from scholars. Further, only a few authors have specialised in this field, and there currently exists low levels of international collaborations among authors as well as practitioners.

The paper provides a novel contribution to the emerging field of corporate SDGs reporting. The key theoretical implications from this study’s SLR include the need for more interventionist research. Although there is an increasing number of accounting scholars developing research within this field, the prevailing research is concentrated on corporate SDGs engagement, drivers of SDGs reporting and scope of SDGs reporting. Furthermore, the scientific discourse remains largely under-theorised with positivist framings primarily focussed on the “what” questions. Thus, a modification to the current approaches and research methods is necessary to advance this field further.

The study provides practitioners with valuable insights into the current state of corporate reporting on the SDGs. To achieve more substantive engagement and reporting, a deeper understanding of the factors that influence corporate behaviour and disclosure practices is necessary. In particular, the study identifies new opportunities for practitioners to enhance the value relevance of corporate SDGs reporting.

The paper offers a comprehensive structured review of the empirical papers published on corporate SDGs reporting. It contributes to deepening this nascent research field by identifying five distinct areas where accounting and business scholars may focus to advance the field further and contribute to achieving the SDGs agenda.

The purpose of this paper is to present a new mathematical model for the unrelated parallel machine scheduling problem with aging effects and multi-maintenance activities.

The authors assume that each machine may be subject to several maintenance activities over the scheduling horizon and a machine turn into its initial condition after maintenance activity and the aging effects start anew. The objective is to minimize the weighted sum of early/tardy times of jobs and maintenance costs.

As this problem is proven to be non-deterministic polynomial-time hard (NP-hard), the authors employed imperialist competitive algorithm (ICA) and genetic algorithm (GA) as solution approaches, and the parameters of the proposed algorithms are calibrated by a novel parameter tuning tool called Artificial Neural Network (ANN). The computational results clarify that GA performs better than ICA in quality of solutions and computational time.

Predictive maintenance (PM) activities carry out the operations on machines and tools before the breakdown takes place and it helps to prevent failures before they happen.

In this paper, a data mining approach is proposed for monitoring the conditions leading to a rail wheel high impact load. The proposed approach incorporates logical analysis of data (LAD) and ant colony optimization (ACO) algorithms in extracting patterns of high impact loads and normal loads from historical railway records. In addition, the patterns are employed in establishing a classification model used for classifying unseen observations. A case study representing real-world impact load data is presented to illustrate the impact of the proposed approach in improving railway services.

Application of artificial intelligence and machine learning approaches becomes an essential tool in improving the performance of railway transportation systems. By using these approaches, the knowledge extracted from historical data can be employed in railway assets monitoring to maintain the assets in a reliable state and to improve the service provided by the railway network.

Results achieved by the proposed approach provide a prognostic system used for monitoring the conditions surrounding rail wheels. Incorporating this prognostic system in surveilling the rail wheels indeed results in better railway services as trips with no-delay or no-failure can be realized. A comparative study is conducted to evaluate the performance of the proposed approach versus other classification algorithms. In addition to the highly interpretable results obtained by the generated patterns, the comparative study demonstrates that the proposed approach provides classification accuracy higher than other common machine learning classification algorithms.

The methodology followed in this research employs ACO algorithm as an artificial intelligent technique and LDA as a machine learning algorithm in analyzing wheel impact load alarm-collected datasets. This new methodology provided a promising classification model to predict future alarm and a prognostic system to guide the system while avoiding this alarm.

In real manufacturing systems, schedules are often disrupted with uncertainty factors such as random machine breakdown, random process time, random job arrivals or job cancellations. This paper aims to investigate robust scheduling for a two-stage assembly flow shop scheduling with random machine breakdowns and considers two objectives makespan and robustness simultaneously.

Owing to its structural and algorithmic complexity, the authors proposed imperialist competitive algorithm (ICA), genetic algorithm (GA) and hybridized with simulation techniques for handling these complexities. For better efficiency of the proposed algorithms, the authors used artificial neural network (ANN) to predict the parameters of the proposed algorithms in uncertain condition. Also Taguchi method is applied for analyzing the effect of the parameters of the problem on each other and quality of solutions.

Finally, experimental study and analysis of variance (ANOVA) is done to investigate the effect of different proposed measures on the performance of the obtained results. ANOVA's results indicate the job and weight of makespan factors have a significant impact on the robustness of the proposed meta-heuristics algorithms. Also, it is obvious that the most effective parameter on the robustness for GA and ICA is job.

Robustness is calculated by the expected value of the relative difference between the deterministic and actual makespan.

This study aims to understand how travelers evaluate travel destination recommendations received from either artificial intelligence (AI) or human travel experts (TEs) based on the size of recommendation and their travel involvement.

This study used a 2 (agent type: AI vs TE) × 2 (size of recommendation: small vs large) × 2 (travel involvement: low vs high) between-subjects design.

When AI recommends destinations, less-involved travelers perceive the recommendations as more credible and trust the system when AI offers larger recommendations than smaller ones. Meanwhile, when TEs offer recommendations, travelers consider the recommendations as equally credible and similarly trust the system, regardless of the recommendation size and travel involvement.

This study sheds light on the design of human-centered AI travel destination recommendation services.

本研究旨在了解旅行者如何根据推荐的规模和他们的旅行参与度来评估从人工智能 (AI) 或人类旅行专家 (TE) 收到的旅行目的地推荐。

本研究使用 2（代理类型：AI 与 TE）×2（推荐数量：小与大）×2（旅行参与：低与高）受试者间设计。

当 AI 推荐目的地时, 参与度较低的旅行者认为推荐更可信, 并且当 AI 提供的建议比较小的建议大时信任系统。 同时, 当 TE 提供推荐时, 无论推荐数量大小和旅行参与度如何, 旅行者都认为这些推荐同样可信并且同样信任系统。

这项研究揭示了以人为本的人工智能旅游目的地推荐服务的设计。