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Diesel engine can produce power more efficiently with lower exhaust emissions when operated at optimum input parameter settings. To achieve this goal, the purpose of this paper is to optimize the input parameters of diesel engine which will lead to optimum performance and exhaust emissions.

To achieve the goal of improving diesel engine performance and exhaust emissions, four input parameters were considered in the study. Five different levels of each input parameter were taken. Four response variables under no load, half load and full load conditions were recorded. Experiments were performed in random manner according to selected Taguchi L25 orthogonal array. The data were analyzed using grey relational analysis coupled with principal component analysis. Analysis of S/N ratio was performed to obtain the optimum combination of input parameters. The grey relational grade at optimum setting of the input parameters was obtained by regression analysis.

Results of the current research work give the optimum input parameter settings for no load, half load and full load conditions of diesel engine. Engine produces power more efficiently with low exhaust emissions when operated at these optimum settings.

In view of the compliance to the stringent air pollution norms of the nations and fast depleting fossil fuels, it is of the utmost importance to design and operate the engine in the optimum range of its input parameters so that it produces more power with low exhaust emissions. This paper aims at optimizing input parameters of diesel engine to improve performance and exhaust emissions. Results of the study presented in this paper are significantly useful for diesel engine-related researchers and professionals.

From the literature review, it appears that only few researchers have conducted studies pertaining to the optimization of the input parameters of diesel engine to improve performance or exhaust emissions. Although few studies related to the optimization of compression ratio, fuel injection timing, fuel injection pressure and air pressure have been reported, no work related to optimization of temperature and pressure of turbocharged air has been reported. Therefore, the main focus of the current research work is on optimizing the charge air temperature and pressure with respect to performance and exhaust emissions.

Despite its devastating nature, the COVID-19 pandemic has also catalyzed a substantial surge in the adoption and integration of technological tools within economies, exerting a profound influence on the dissemination of information among participants in stock markets. Consequently, this present study delves into the ramifications of post-pandemic dynamics on stock market behavior. It also examines the relationship between investors' sentiments, underlying behavioral drivers and their collective impact on global stock markets.

Drawing upon data spanning from 2012 to 2023 and encompassing major world indices classified by Morgan Stanley Capital International’s (MSCI) market and regional taxonomy, this study employs a threshold regression model. This model effectively distinguishes the thresholds within these influential factors. To evaluate the statistical significance of variances across these thresholds, a Wald coefficient analysis was applied.

The empirical results highlighted the substantive role that investors' sentiments and behavioral determinants play in shaping the predictability of returns on a global scale. However, their influence on developed economies and the continents of America appears comparatively lower compared with the Asia–Pacific markets. Similarly, the regions characterized by a more pronounced influence of behavioral factors seem to reduce their reliance on these factors in the post-pandemic landscape and vice versa. Interestingly, the post COVID-19 technological advancements also appear to exert a lesser impact on developed nations.

This study pioneers the investigation of these contextual dissimilarities, thereby charting new avenues for subsequent research studies. These insights shed valuable light on the contextualized nexus between technology, societal dynamics, behavioral biases and their collective impact on stock markets. Furthermore, the study's revelations offer a unique vantage point for addressing market inefficiencies by pinpointing the pivotal factors driving such behavioral patterns.

The present study intends to measure buying intention of Generation Z (Gen Z) on social networking sites (SNSs) incorporating perceived risk with the social commerce adoption model (SCAM).

Data were collected via an online questionnaire, and the study used a total of 349 accurate and useable responses. The population of the study includes Indian young consumers coming from the Gen Z cohort. Data were analyzed using SPSS 20 and AMOS 22.0. The proposed hypotheses were statistically tested.

The empirical results show that perceived risk is a significant and strong predictor of perceived usefulness that, in turn, negatively influences buying intention. Among all the constructs of SCAM, perceived usefulness is the most influential and strongest predictor of buying intention. The proposed model explained approximately 34% of the variance in the behavioral intention.

Based on the findings of this study, many theoretical and practical implications may be inferred that can be used to make recommendations to social commerce companies and help them understand the buying intention of Gen Z.

There are many studies that have examined buying intention and a few have measured it on Gen Z. The present study is novel in itself as it has measured the buying intention of Gen Z using the SCAM in the Indian context. Hence, the present research attempts to comprehend the variables influencing buying intention and analyses the relationship between these factors in the social media setting.

Being grounded in interdependence theory, this study aims to address the following research question: Do managers’ negotiation styles (collaborative versus competitive) make employees’ relational justice-emotional experiences links sporadic?

Data elicited from N = 139 Pakistani undergraduate students participating in an online scenario-based experiment were used to employ repeated measures analysis and partial least square structural equation modeling techniques.

Results suggest that employees’ relational justice is likely to be higher when managers use a collaborative negotiation style than when they use competitive style in performance review meetings. Moreover, per managers’ different negotiation styles, employees’ relational justice perceptions may predict their positive emotions differently. That is, when managers use collaborative negotiation style, employees’ relational justice perceptions may positively predict their hope but not optimism, whereas when managers use competitive negotiation style, employees’ relational justice perceptions may positively predict their optimism but not hope. Furthermore, the positive relationship between employees’ relational justice and their optimism is stronger when their trust in manager is low than when it is high.

The study is of value for performance management theorists who aim to address the issue of ineffectiveness of the practice through relational means. The study includes the recently explicated concept of relational justice and examines its links with employee emotional reactions to performance reviews. Moreover, the study unveils how managers’ negotiation styles in performance review meetings cause variations in the links between employees’ perceptions of relational justice and their emotional experiences.

The importance of e-government projects has been realized in the developed as well as in the developing countries of the world. The successful implementation of e-government projects is a challenging task. Public sector organizations are facing different sorts of challenges while executing e-government initiatives. The purpose of this paper is to explore issues/challenges related to e-government project implementation.

A systematic literature review (SLR) was conducted to understand the challenges of the e-government project, as mentioned in past research. The findings of this SLR helped in developing a proposed framework for e-government challenges. The proposed framework encompasses challenges and issues related to four main aspects such as technological, organizational, project management and enabling environment. These aspects need to be further explained for their remedial measures. For this purpose, an exploratory case study was conducted. The unit of analysis was one of the e-government projects of the ministry of IT. The interviews of the senior and middle management involved in the implementation of this project were conducted and transcribed.

The case study findings endorse a framework proposed. The findings of this research reflect the importance of creating a shared vision, adequate funding and good project management practices as an essential aspect of e-government implementation. Further, the importance of user-related issues such as involvement, capacity building and technical skills was also found critical in successfully executing and sustaining such initiatives in the organization. Further study findings provide necessary guidance for the senior management, which may help to develop effective strategies for the e-government projects in the organization.

The current study focused on developing a better understanding of the challenges commonly faced by organizations during e-government adoption and implementation related to organizational, project management, enabling environment and technological aspects.

The abrupt outbreak of coronavirus disease (COVID-19) hit every nation in 2020–2021, causing a worldwide pandemic. The worldwide COVID-19 epidemic, described as a “black swan”, has severely disrupted manufacturing firms' supply chain. The purpose of this study is to investigate how supply chain data analytics enable the effective deployment of agility, adaptability and alignment (3As) strategies, resulting in improving post-COVID disruption performance. It also analyses the indirect effect of supply chain data analytics on disruption performance through the 3As supply chain strategies.

The hypothesis and theoretical framework were tested using a questionnaire survey. The authors employed structural equation modelling through the SMART PLS version 3.2.7 to analyse data from 163 textile firms located in Pakistan.

The results revealed that the supply chain data analytics contributed positively and significantly to the agility and adaptability, while all 3As supply chain strategies impacted the PPERF substantially. Further, the connection between supply chain data analytics (SCDA) and disruption performance has substantially been influenced through 3As supply chain strategies.

The results imply that in the event of low likelihood, high effect disruptions, managers and decision-makers should focus their efforts on integrating data analytics capabilities with 3As supply chain policies to ensure long-term company success.

This research sheds fresh light on the importance of data analytics in effectively implementing 3As strategies for sustaining company performance amid COVID-19 disruptions.

This study aims to investigate the effect of the diversity of the board of directors (BOD) and the shariah supervisory board (SSB) on credit risk, insolvency, operations, reputation, rate of deposit return risk (RDRR) and equity-based financing risk (EBFR) of Islamic banks (IB).

The study uses 68 IBs from 19 countries covering 2009 to 2019. BOD and SSB diversity attributes data were hand-collected from the annual reports. Financial data were collected from the bankscope database. The robustness test and two-step system generalized method of moment estimation technique were used to address potential endogeneity issues.

This study provides evidence that diversity in the experience and cross-membership of board members decreases the risk. Gender diversity increases the risk, but the BOD’s education level diversity has no relationship with risk. More interestingly, influences in the experience and cross-membership of the SSB’s members positively influence risk. However, members’ education levels and gender diversity have not been proven to affect risk.

The paper recommends that Islamic banking authorities play a stronger role and make a greater effort in driving corporate governance reform. Also, determining individual characteristics of the board is a requirement to become a member of a BOD or an SSB.

This paper expands the commitment literature through the diversity of the BOD’s and the SSB’s members in terms of their education levels, experience, cross-membership and gender. This study expands the list of potential risks for IBs, by including the RDRR and EBFR.

This study aims to investigate the impact of Shari’ah and corporate governance on Islamic banks performance in Asia.

The study uses hand collected data set on Shari’ah and corporate governance variables of 79 Islamic banks of 19 countries of Asia, for the period of 2011-2016. Augmented Mollah et al. (2017) composite corporate governance index into Islamic corporate governance (ICG) index by incorporate Shari’ah board’s (SBs) attributes. Two types of statistical analysis were performed; descriptive statistics, sample t-test and panel random effects regression. The analysis was further sub-sampled by considering the supervisory vs advisory, GCC vs non-GCC and large vs small effects of Shari’ah and corporate boards on Islamic banks performance.

The results of the baseline model reveal that Shari’ah governance-related variables are more influential in determining the financial performance of the Islamic banks. The sub-sampled data findings illustrated some interesting facts. Shari’ah supervisory vs advisory boards regression results show that the ICG index was found significant in both the models. However, when SBs are weak the general board dominates in determining the performance. GCC vs non-GCC results show a relatively good governance in non-GCC countries. While, in case of large vs small Islamic banks, banks having high total assets demonstrates sound governance characteristics.

Independent, large and educated SB can play a significant role in removing the hurdles facing the Islamic banking industry and can also enhance stakeholders’ value.

This study enriches the understanding on Shari’ah governance, corporate governance and financial performance of Islamic banks in Asia.

This study examines the influence of contextual factors (e.g. age and ownership type) on HRM formality (including the underlying functions of recruitment, selection, training and development, performance appraisal and compensation) in SMEs.

Data were collected through a quantitative survey of 300 owners/managers of services, manufacturing and trade SMEs in Pakistan.

Firm age, association with a larger parent entity, existence of a strategic business plan and the presence of a human resource information system (HRIS) are positively related with higher HRM formality. Firm size, family ownership and exporting characteristics had no association with formality.

This study suggests a highly influential role for contextual factors in shaping HRM practices in Pakistani SMEs. Since the lack of a strategic approach towards human resource development is directly linked to the inferior performance of SMEs in Pakistan, this study provides an understanding of the contextual institutional setting that shapes the nature of HRM practices. The findings inform both SME owners/managers and policy makers.

Institutional influences on HRM systems have attracted attention but organisational factors are less often studied. Studies mostly relate to Western contexts and lack perspectives from SMEs. The findings of this empirical investigation highlight the importance of context specific research given the different nature of institutional settings.

The primary aim of this study is to examine the decision-making process of a reverse supply chain within a cooperative-competitive environment, with a particular emphasis on the incorporation of Internet of Things (IoT) technology in the recycling process.

The impact of IoT on recycling effectiveness, RF (refurbished) product demand and remanufacturing expenses is analyzed. The game-theoretic models are constructed to incorporate the co-competitive relationship between a IR (Internet Recycler) and a remanufacturer, with specific attention paid to the IR's application of IoT technology.

The results indicate that the use of IoT reduces the costs associated with remanufacturing, leading to an increase in the transfer price of pre-owned items procured from IRs. The efficacy of the reverse supply chain depends not only on its cost-saving capabilities but also on consumer preferences toward RF and remanufactured (RM) items.

IoT technology can benefit Internet recycling and remanufacturing by enhancing transparency, quality monitoring and automation of processes. It can also help reduce operating costs, improve customer engagement and incentivize cooperation between organizations.

This study examined the strategic decision-making processes in the context of cooperation and competition between IR and remanufacturers. In addition, this research makes a valuable contribution to the literature by elucidating the optimal conditions for the effective implementation of Internet of Things (IoT) technology in recycling operations.