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Enterprise risk management (ERM) is a risk management approach that calls for integrating all the organization-wide risks and takes a portfolio view point of managing organizational risks. The purpose of this paper is to investigate the factor that influence a firm’s decision to adopt ERM.

The authors employ a particular technique of survival data analysis, the Cox proportional hazards model, to investigate the factors that lead towards the decision of initiating an ERM programme. The authors constructed a unique sample of French firms derived from the information in 315 corporate news announcements for the hiring of a chief risk officer and information retrieved from publicly available annual reports to identify firms that initiated an ERM programme, over the period from year 1999 to 2008.

The results suggest that besides the growing international and local regulatory pressure, factors that are internal to the organizations like the expected probability of financial distress and its explicit and implicit costs, poor earnings performance and the existence of growth opportunities play vital role in motivating firms to adopt ERM. It was also found that corporate governance practices such as the independence of the board may also lead towards an initiation of the ERM.

This study makes theoretical and methodological contribution the ERM literature by employing a novel methodology and presenting empirical evidence based on data form French firms.

This research study proposes a feature-centric spam email detection model (FSEDM) based on content, sentiment, semantic, user and spam-lexicon features set. The purpose of this study is to exploit the role of sentiment features along with other proposed features to evaluate the classification accuracy of machine learning algorithms for spam email detection.

Existing studies primarily exploits content-based feature engineering approach; however, a limited number of features is considered. In this regard, this research study proposed a feature-centric framework (FSEDM) based on existing and novel features of email data set, which are extracted after pre-processing. Afterwards, diverse supervised learning techniques are applied on the proposed features in conjunction with feature selection techniques such as information gain, gain ratio and Relief-F to rank most prominent features and classify the emails into spam or ham (not spam).

Analysis and experimental results indicated that the proposed model with sentiment analysis is competitive approach for spam email detection. Using the proposed model, deep neural network applied with sentiment features outperformed other classifiers in terms of classification accuracy up to 97.2%.

This research is novel in this regard that no previous research focuses on sentiment analysis in conjunction with other email features for detection of spam emails.

This research aimed to study the impact of compensation on employee retention and turnover intentions among healthcare employees. The study also tested the mediation role of job satisfaction in the relationship.

In the present study, self-administrated questionnaires were distributed among 600 doctors working in public hospitals of Pakistan, following stratified sampling. The data analysis was conducted through SPSS and smart-PLS.

Results of the present study supported all the hypotheses (H1–H7), such as the significant relationship of compensation with employee retention and turnover intentions. Results further confirmed the mediation effect of job satisfaction between compensation and employee retention as well as compensation and turnover intentions.

This study is useful for policymakers and organizational managers since the study provides guidelines on employee retention and high turnover intentions and how these factors are influenced by improved compensation.

This study sheds light on the relationship of compensation together with employee retention and turnover intentions through the mediating role of job satisfaction in healthcare context, which was overlooked in the existing literature.

The concept of green supply chain management (GSCM) is gaining popularity in developing countries due to the environmental and economic impact along with increasing awareness of environmental safety. Enterprises are trying to express their sincere commitment toward green practices. The purpose of this paper is to examine the impact of GSCM on green and economic performance of the firm under the influence of leadership and institutional pressures.

Conceptual model was developed from previous research works to understand the driving forces of green and economic performance which had inconsistent findings in the literature. Data were collected from 174 leading ISO 14001 certified manufacturing firms in Pakistan by using a structured questionnaire. Partial least squares-structural equation modeling is used for testing the hypotheses.

The statistical results indicate that the leadership and institutional pressures influence the firm for adoption of internal green practices and external green collaboration. The statistical results also suggest that green practices significantly improve firm’s green and economic performance. However, firm’s external green collaboration does not significantly affect green performance, but it improves green performance significantly.

This research is conducted on Pakistan as a case of highly populated developing country.

This research presents the empirical evaluation of the influence of leadership and institutional pressure on green practices and improved green and economic performance. The results offer useful understanding for SCM practitioners seeking to adopt GSCM practices. The results also provide policy insights for regulators, organizations and legislators to further promote GSCM.

The purpose of this paper is to investigate the role of gender in the relationships between employee’s work experience and their chances of obtaining a job promotion.

Five hundred middle to lower management Pakistan employees from service industries were surveyed. Measures used to obtain data included work experience, job promotions and gender. Gender was dummy coded. Moderation analyses via SPSS was used to investigate the moderating effect of gender in the relationship between employee’s work experience and job promotions.

The results indicated that work experience was positively associated with job promotion. However, the results indicated that gender fully moderated the relationship between work experience and job promotion. Specifically, female employees were less likely to get promoted compared to male employees despite having similar work experiences.

Drawing together the human capital theory, social role theory and cultural factors, this study highlighted the socioeconomic/cultural barriers’ impact on Pakistani women’s career ascendance.

There is a lack of empirical evidence about career ascendance among Pakistani women. Therefore, the aim of the present study is to conduct and provide empirical evidence so that relevant agencies within Pakistan can develop more gender-equitable promotional policies and processes.

This study responded to the call for more empirical investigation of career ascendancy among women in developing countries. While this issue has been discussed in many developed western countries, there is a lack of empirical evidence in Muslim Pakistan.

This paper aims to provide a framework regarding Information Technology (IT) Flexibility in Supply Chain and its relationship with the benefits we could see from Enterprise Resource Planning (ERP) systems. Furthermore, this research explores the moderating effect of Process Integration Capability in the relationship between IT flexibility and ERP benefits.

This research model will help organizations get additional benefits from their ERP systems that incurred huge costs, time and multiple resources at their implementation. The technique used for analyzing data is structural equation modeling (SEM), and data is collected from 107 respondents through a questionnaire from Business and IT Professionals.

The study findings reveal a positive and significant relationship between IT flexibility and ERP systems benefits; moreover, results also confirmed that the organization's process integration capability significantly increased the benefits of ERP systems. The findings also highlight empirical evidence about the significance of the top-to-bottom approach investing in IT flexibility and the bottom-to-top approach during the implementation of IT systems for successful implementations.

This study has various implications for practitioners that help them successfully implement and long-term viability of their IT infrastructure.

This study's findings will help IT managers and strategists make effective decisions for creating IT flexibility in alignment with the strategic goals to realize the desired results expected from ERP systems and implementations of new IT systems.

This study aims to examine the perceptions and opinions of relevant stakeholders regarding entrepreneurship opportunities and growth in the Gilgit-Baltistan (GB) province of Pakistan. Specifically, it focuses on the role of special economic zones (SEZs), such as Maqpondass SEZ and the China–Pakistan Economic Corridor (CPEC), in fostering nascent entrepreneurship (NE) and promoting regional development.

The study employs ordered logistic regression to estimate the relationship between various independent variables and nascent entrepreneurship (NE). The independent variables include awareness of CPEC (AAC), awareness of Maqpondass SEZ (AAMEZ), SEZ incentives (SEZInc), regional market competitiveness (RMC), loan availability (LA) and education and experience (EE).

The findings indicate a robust positive relationship between SEZ-based industries and the growth of local small businesses and enterprises in Gilgit-Baltistan. Furthermore, the study suggests that government incentives, access to finance, skill development, relevant knowledge, and connections with local businesses facilitate the establishment of new ventures.

The study underscores the importance of focusing on human capital development, providing financial assistance, and creating incentives for adopting advanced technology to foster the growth of local businesses in Gilgit-Baltistan through SEZs. It emphasizes the need for policymakers and stakeholders to prioritize initiatives that support entrepreneurship and innovation in the region.

This study contributes to the existing literature by providing novel insights into the perceptions of entrepreneurship development in Gilgit-Baltistan, particularly concerning the influence of natural resources and SEZs. It fills a gap in the research by offering valuable implications for policymakers, researchers, and practitioners seeking to promote sustainable economic development in the region.

Twitter users’ generated data, known as tweets, are now not only used for communication and opinion sharing, but they are considered an important source of trendsetting, future prediction, recommendation systems and marketing. Using network features in tweet modeling and applying data mining and deep learning techniques on tweets is gaining more and more interest.

In this paper, user interests are discovered from Twitter Trends using a modeling approach that uses network-based text data (tweets). First, the popular trends are collected and stored in separate documents. These data are then pre-processed, followed by their labeling in respective categories. Data are then modeled and user interest for each Trending topic is calculated by considering positive tweets in that trend, average retweet and favorite count.

The proposed approach can be used to infer users’ topics of interest on Twitter and to categorize them. Support vector machine can be used for training and validation purposes. Positive tweets can be further analyzed to find user posting patterns. There is a positive correlation between tweets and Google data.

The results can be used in the development of information filtering and prediction systems, especially in personalized recommendation systems.

Twitter microblogging platform offers content posting and sharing to billions of internet users worldwide. Therefore, this work has significant socioeconomic impacts.

This study guides on how Twitter network structure features can be exploited in discovering user interests using tweets. Further, positive correlation of Twitter Trends with Google Trends is reported, which validates the correctness of the authors’ approach.

“Psychosocial health” is a new term to comprehend the already established factors involved in mental health and psychological well-being. The term has not been specifically defined and explained within the framework of psychology.

The study proposed and validated a new model of psychosocial health. Principal component analysis, exploratory factor analysis and confirmatory factor analysis were conducted by involving a total of 4,086 participants.

Psychosocial health was interpreted as the “sexual, emotional, social, environmental, cognitive, religious, moral and spiritual satisfaction” of a person. The proposed model of psychosocial health was statistically validated. The additional findings revealed significantly higher levels of psychosocial health in women and significant inverse correlations between psychosocial health and age.

The current paper provided a comprehensive picture of psychosocial health from a psychological perspective and presented a statistically reliable tool for measuring psychosocial health.

This paper aims to investigate the influence of different post-annealing cooling conditions, i.e. furnace cooling (heat treatment (HT) 1 – slow cooling) and air cooling (HT 2 – fast cooling), on the microstructure and mechanical properties of selective laser melting (SLM) built austenitic 316L stainless steel (SS).

Three sets of 316L SS samples were fabricated using a machine standard scanning strategy. Each set consists of three tensile samples and a cubic sample for microstructural investigations. Two sets were subsequently subjected to annealing HT with different cooling conditions, i.e. HT 1 and HT 2, whereas one set was used in the as-built (AB) condition. The standard metallographic techniques of X-ray diffraction, scanning electron microscopy and electron back-scattered diffraction were used to investigate the microstructural variations induced by different cooling conditions. The resultant changes in mechanical properties were also investigated.

The phase change of SLM fabricated 316L was observed to be independent of the investigated cooling conditions and all samples consist of austenite phase only. Both HT 1 and HT 2 lead to dissolved characteristic melt pools of SLM. Noticeable increase in grain size of HT 1 and HT 2 samples was also observed. Compared with AB samples, the grain size of HT 1 and HT 2 was increased by 12.5% and 50%, respectively. A decreased hardness and strength, along with an increased ductility was also observed for HT 2 samples compared with HT 1 and AB samples.

From previous studies, it has been noticed that most investigations on HT of SLM fabricated 316L were mainly focused on the HT temperature or holding time. However, the post-HT cooling rate is also an equally important factor in deciding the microstructure and mechanical properties of heat-treated components. Therefore, this paper investigates the influence of different post-annealing cooling conditions on microstructure and mechanical properties of SLM fabricated 316L components. This study provides a foundation for considering the post-HT cooling rate as an influential parameter that controls the properties of heat-treated SLM components.