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This study aims to ascertain the impact of customer perception of CSR activities (philanthropic, environmental and ethical) conducted on various consumer and corporate related dimensions including; customer loyalty, consumer attachment, corporate performance and repurchase intention. The study also adds value by taking customer perception of CSR as a mediator between green image and performance.

Structural equation modelling is used after using different (valid and reliable) instruments to measure latent constructs. The study has a sample size of 250 “CSR Consumers”, who had some knowledge and awareness of CSR and green image being advertised and or labelled by the company (such as printing “Recycled” or other eco-friendly images/labels on shopping bag, fliers, outlets, etc.) and are consumers/customers of such firms. The respondent’s awareness was measured by randomly asking them to recall organizations that might have eco-friendly policies.

The key findings of the study are that perceived fit of culture along with CSR capability radically influences CSR perception within consumer minds and so, subsequently, customer attachment and overall performance of the corporation. The outcomes bestow significant ramifications for marketing and advertising philosophy combined with practice.

Stakeholders exist in the form of consumers other than employees. So consumer satisfaction must be imparted its fair share of importance. Managers must make sure that initiatives for societal benefit are well accepted and well recognized by consumers in a positive array of light. Corporations enthusiastically involved in initiating CSR activities and forecast a positive income. The study guides managers into not falling in this misconception and by recognizing that the fact is that the company managers must only expect higher performance levels once their CSR is in synchronization with the firm’s culture.

A number of studies have been conducted about CSR practices in the Indian context for example (Khan and Atkinson, 1987; Krishna, 1992; Arora and Puranik, 2004; Sood and Arora, 2006; Mishra and Suar, 2010); however, there is dearth of research in its neighbouring country Pakistan about CSR practices and consumer perceptions. Therefore, this research aims to fill this gap by examining CSR practices in Pakistan which has similar historical and colonial roots with India. In doing so, this study ascertains the impact of CSR activities conducted on various consumer and corporate related dimensions that incorporate customer loyalty, consumer attachment and corporate performance.

This research aims to examine the relationship between transactive memory systems and team sensemaking in the presence of critical boundary conditions, namely, task conflict and reward interdependence.

The data for Study 1 was collected from 304 team members who worked in 87 organizations in the Information, Communication and Technology sector of Pakistan. Study 2 is based on team-level data that was collected from 180 teams working in the New Product Development sector, with four to seven members in each team. The data tested the three-way interaction effect of the transactive memory systems, task conflict and reward interdependence on team sensemaking.

Results have shown that transactive memory systems have a positive relationship with team sensemaking, particularly when both task conflict and reward interdependence were perceived to be high.

To reap synergies, human resource managers should avoid disrupting team structures, assigning new members to a team or rotating team members very frequently. Moreover, if a team is experiencing high task conflict, reward interdependence may encourage conflict to remain constructive.

The current study is one of the first few attempts that examine the pivotal role of task conflict and reward interdependence as boundary conditions on transactive memory systems and team sensemaking. This research, therefore, highlights the role of transactive memory systems in enhancing team sensemaking at higher levels of task conflict and reward interdependence.

The purpose of this study is to alleviate the specified issues to a great extent. To promote patients’ health via early prediction of diseases, knowledge extraction using data mining approaches shows an integral part of e-health system. However, medical databases are highly imbalanced, voluminous, conflicting and complex in nature, and these can lead to erroneous diagnosis of diseases (i.e. detecting class-values of diseases). In literature, numerous standard disease decision support system (DDSS) have been proposed, but most of them are disease specific. Also, they usually suffer from several drawbacks like lack of understandability, incapability of operating rare cases, inefficiency in making quick and correct decision, etc.

Addressing the limitations of the existing systems, the present research introduces a two-step framework for designing a DDSS, in which the first step (data-level optimization) deals in identifying an optimal data-partition (Popt) for each disease data set and then the best training set for Popt in parallel manner. On the other hand, the second step explores a generic predictive model (integrating C4.5 and PRISM learners) over the discovered information for effective diagnosis of disease. The designed model is a generic one (i.e. not disease specific).

The empirical results (in terms of top three measures, namely, accuracy, true positive rate and false positive rate) obtained over 14 benchmark medical data sets (collected from https://archive.ics.uci.edu/ml) demonstrate that the hybrid model outperforms the base learners in almost all cases for initial diagnosis of the diseases. After all, the proposed DDSS may work as an e-doctor to detect diseases.

The model designed in this study is original, and the necessary parallelized methods are implemented in C on Cluster HPC machine (FUJITSU) with total 256 cores (under one Master node).

Private telecommunication companies in Yemen face huge challenges to improve their competitive position. Many of them suffer from performance management systems (PMS) that are not aligned nor integrated and do not give adequate support. This research aims at identifying the current state and quality of PMS in Yemeni private telecom companies and developing recommendations that will assist in improving their performance and competitive position.

As research tools, the performance management analysis – used to measure the quality of a PMS – and the high performance organization (HPO) framework – used to measure the quality of an organization and thereby its ability to achieve sustainable high competitive performance – are applied at three Yemeni private telecom companies.

There is a considerable performance gap between the actual and the ideal PMS in the Yemeni private telecom companies; this performance gap is highly correlated with competitive performance in a negative relationship. Both the structural and behavioral dimensions’ performance gaps have a significant impact on competitive performance, with the impact of the behavioral dimensions being considerably higher than that of the structural dimensions.

The current academic literature on HPOs and PMS in Yemen is scarce. This study adds knowledge to these areas, and thus provides a basis for further study. This research results also help managers of the Yemeni private telecom companies to take strategic action to redesign and implement new and improved PMS to increase their competitive performance.

Logistics is the part of the supply chain (SC) that plans, executes and handles forward and reverse movement and storage of products, services and related information, in order to respond to customers' needs effectively and efficiently. The main concern for logistics is to ensure that the correct product is placed at the right time. This paper introduces a linear model of shipping focused on decision-making, which includes configuration of shipping network, choosing of transport means and transfer of individual customer shipments through a particular transport system.

In this study, authors try to address the problem of supply chain network (SCN) where the primary goal is to determine the appropriate order allocation of products from different sources to different destinations. They also seek to minimize total transportation cost and inventory cost by simultaneously determining optimal locations, flows and shipment composition. The formulated problem of getting optimal allocation turns out to be a problem of multi-objective programming, and it is solved by using the max-addition fuzzy goal programming approach, for obtaining optimal order allocation of products. Furthermore, the problem demand and supply parameters have been considered random in nature, and the maximum likelihood estimation approach has been used to assess the unknown probabilistic distribution parameters with a specified probability level (SPL).

A case study has also been applied for examining the effectiveness and applicability of the developed multi-objective model and the proposed solution methods. Results of this study are very relevant for the manufacturing sector in particular, for those facing logistics issues in SCN. It enables researchers and managers to cope with various types of uncertainty and logistics risks associated with SCN.

The principal contribution of the proposed model is the improved modelling of transportation and inventory, which are affected by different characteristics of SCN. To demonstrate computational information of the suggested methods and proposed model, a case illustration of SCN is provided. Also, environmentalism is increasingly becoming a significant global concern. Hence, the concept proposed could be extended to include environmental aspects as an objective function or constraint.

Efficient integration of logistical cost components, such as transportation costs, inventory costs, with mathematical programming models is an important open issue in logistics optimization. This study expands conventional facility location models to incorporate a range of logistic system elements such as transportation cost and different types of inventory cost, in a multi-product, multi-site network. The research is original and is focused on case studies of real life.

Over the last two decades, green supply chain management (GSCM) has enabled businesses to operate in an environmentally friendly manner. The present review examines 234 research articles and proposes a methodical literature review on GSCM, focusing on the aspects of sustainable development.

The work examines conceptual, analytical, empirical and non-empirical research articles, analyzing at all levels of the organization, such as firm, dyad, supply chain and network. The objective of the review is to provide insights into the state and scope of existing research in the domain of GSCM, to identify the prevalence of GSCM and to consolidate the trend of future research. The literature review follows a systematic methodology for analyzing the literature.

The findings can support researchers in identifying research areas with significant impact and streamline research on GSCM in the future. Practitioners can utilize this structured classification to strategize their green initiatives in their firms.

The work contributes to providing literature that explores a detailed review in GSCM. The proposed literature review captures critical aspects in the domain of GSCM and offers future research directions.

This study aims to minimize the cost and time of blood delivery in the whole blood supply chain network (BSCN) in disaster conditions. In other words, integrating all strategic, tactical and operational decisions of three levels of blood collection, processing and distribution leads to satisfying the demand at the right time.

This paper proposes an integrated BSCN in disaster conditions to consider four categories of facilities, including temporary blood collection centers, field hospitals, main blood processing centers and medical centers, to optimize demand response time appropriately. The proposed model applies the location of all permanent and emergency facilities in three levels: blood collection, processing and distribution. Other essential decisions, including multipurpose facilities, emergency transportation, inventory and allocation, were also used in the model. The LP metric method is applied to solve the proposed bi-objective mathematical model for the BSCN.

The findings show that this model clarifies its efficiency in the total cost and blood delivery time reduction, which results in a low carbon transmission of the blood supply chain.

The researchers proposed an integrated BSCN in disaster conditions to minimize the cost and time of blood delivery. They considered multipurpose capabilities for facilities (e.g. field hospitals are responsible for the three purposes of blood collection, processing and distribution), and so locating permanent and emergency facilities at three levels of blood collection, processing and distribution, support facilities, emergency transportation and traffic on the route with pollution were used to present a new model.

The coronavirus disease 2019 (COVID-19) pandemic represents one of the most significant disruptions to supply chains (SCs), stimulating both practitioners and scholars to seek ways to enhance supply chain performance (SCP). Recent advancements in technology, particularly supply chain analytics (SCA) technologies, offer promising avenues for mitigating risks associated with SC disruptions like those posed by the COVID-19 pandemic. However, the existing literature lacks a comprehensive analysis of the connection between SCA and healthcare SC (HSC) performance. To address this research gap, we employed the dynamic capability perspective to investigate the mediating roles of supply chain innovation (SCI), resilience (SCR) and flexibility (SCF) in the relationship between SCA and HSC performance. The study further examined the moderating role of a data-driven culture (DDC).

The proposed model was tested using survey data from 374 managers of healthcare facilities in Ghana. SPSS and Amos were used to analyze the data gathered.

The results showed that while SCA may drive HSC performance, the presence of SCI, SCR and SCF may serve as channels to drive enhanced HSC performance. Additionally, we also found that different levels of a DDC induce varying effects of SCA on SCI, SCR and SCF.

The study’s results have theoretical and practical implications, offering valuable insights for the advancement of SCA in healthcare literature. They also deepen SC managers’ comprehension of how and when SCA can boost HSC performance. However, as the study was limited to healthcare facilities in Ghana, its findings may not be universally applicable.

This study contributes to the literature by demonstrating that SCI, SCR, SCF and a DDC could serve as transformative mechanisms to reap superior HSC outcomes. This study also offers contemporary guidance to managers regarding SCA investment decisions.