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This paper compares three business excellence models (BEMs): Baldrige criteria for performance excellence (BCPE), European Foundation for Quality Management (EFQM) model and Deming Prize (DP) model, representing American, European and Japanese excellence models, respectively. These models are compared in terms of contents, scope, orientation, emphasis and administration.

Using an inductive-deductive approach, the paper identifies and compares the contents, core values, unique features, points of emphasis and the overall approach of these models.

Significant differences exist in these models, and that none of these models is perfect. Overall, the BCPE is results-oriented, mainly economic-focused, explicit in presenting requirements and better structured. The EFQM model provides an augmented focus on sustainability, stakeholder engagement and inclusivity. The DP model is an internally oriented TQM model that focuses on processes improvement as a means to excellence.

The findings can guide managers regarding what they should expect from the application of excellence models and make better decisions regarding the choice of excellence models. Further, these findings can provide a foundation for updating the excellence models.

This paper stands alone as a qualitative comparison of BEMs in terms of contents, scope, administration and emphasis on excellence related matters.

The growing emphasis on “managerialism” in police and the pressure to employ scientific methods of performance measurement warrants the need for a structured framework. The scope of police duties is large as it relates to several preventive and corrective action related to public safety and crime management. A challenge in measuring police performance is to take into consideration a range of variables that can potentially influence performance. The purpose of this paper is to provide a structured framework for measuring different facets of police efficiency, which is especially useful in managerial decision making.

This paper uses data envelopment analysis and discusses efficiency measurement in terms of the technical, managerial and scale efficiency, resources utilization patterns, returns-to-scale analysis and measurement of super-efficiency. The application of framework is based on the data of the police stations of Lahore, a large metropolitan city in Pakistan.

The paper shows the application of different measures of efficiency in making decisions pertaining resources allocation, prioritizing areas for improvement and identifying benchmarks for performance improvement. Different measures of efficiency are presented in the form of a structured framework.

Managers can use this framework to glean rich insights into different types of efficiency and sources of inefficiency. Further, a discussion of variables provided in this paper can be especially useful in determining trade-offs during the selection of inputs and outputs.

The key contribution of this paper is in providing a multifaceted efficiency measurement framework, that is capable of providing rich insights into the sources of inefficiency and helps scientific decision making. To the best of our knowledge, such a multifaceted approach has not been provided in previous publications.

Previous studies have mainly discussed the impact of student engagement on different facets of academic performance. Research on the approaches to promote student engagement in a classroom setting (i.e., micro-level practices) is relatively sparse. This paper provides a framework for enhancing student engagement in a university undergraduate classroom setting.

This study builds on action research. At the core of this approach is making interventions through a set of pedagogical approaches, derived from the literature. The impact of these interventions was measured, followed by reflections on the outcomes and developing a future improvement strategy.

Student engagement can be enhanced by using heterogeneous pedagogical approaches that positively influence student performance. Further, the use of mixed pedagogical approaches helps students and teachers acquire meta-cognitive knowledge (i.e., knowledge of their learning preferences) and sets the direction for learning.

The key contribution of this study is providing a student engagement framework applicable in a sophomore-level classroom setting. The framework discusses a set of techniques, their theoretical underpinnings, the course of their execution and the challenges faced in this process. The framework can be used to guide enhancing student engagement.

Contrary to the macro-level measures, research on micro-level measures for promoting student engagement approaches is scarce. This study discusses not only different strategies but also details the dynamic course of their deployment. The study, therefore, is unique in its contribution.

The purpose of this study is to develop a scale for measuring housing quality in the context of Pakistan.

The inductive and deductive approaches for item generation have been combined, and items have been purified using multistage expert review. Data was collected from a sizeable purposive sample of 445 respondents, and exploratory and confirmatory factor approaches used for assessing psychometric properties of the scale.

The result is a 21-item scale covering five dimensions, namely, Design and Construction Quality, Neighborhood Quality, Adequacy of Space, Quality of Institutional Services and Proximity of Basic Amenities.

This study contributes to housing quality literature by deepening our understanding of the concept of housing quality in the context of Pakistan, the world’s fifth most populous country. The findings of the study have important implications for both theory and practice.

Empirical studies on the supply chain quality management (SCQM) of the pharmaceutical sector are scarce and scattered. This paper investigates a set of key quality determinants in the pharmaceutical sector to offer a holistic quality control framework for its supply chain (SC).

A mixed-method design was used. A focused group discussion with seven pharmaceutical SC managers summarized the narratives of the SCQM in this sector and helped to develop a questionnaire. The survey data were collected from the managers of 299 pharmaceutical firms.

The statistical results show the key determinants are leadership (LS), customer focus (CF), process integration and management (PIM), supplier relations (SR), quality practice (QP), human resource practices (HRP) and safety (SF). Unlike many other sectors, in the pharmaceutical SC, logistics and inventory costs have been proved to be not as important as other measures in the eyes of the SC managers and as part of SCQM performance measures, which can be a result of the priority systems influenced by the World Health Organization standards.

The study is distinctive in its scope as is carried out in a developing country with focus on concept of SCQM with reference to pharmaceutical SC. It shows great potential for further studies and improvements in logistics and warehousing in this sector, for the interest of the pharmaceutical SC managers and regulators who aim to ensure safety and quality in the health-care sector.

The purpose of this paper is to conduct a comprehensive review of the noteworthy contributions made in the area of the Feedforward neural network (FNN) to improve its generalization performance and convergence rate (learning speed); to identify new research directions that will help researchers to design new, simple and efficient algorithms and users to implement optimal designed FNNs for solving complex problems; and to explore the wide applications of the reviewed FNN algorithms in solving real-world management, engineering and health sciences problems and demonstrate the advantages of these algorithms in enhancing decision making for practical operations.

The FNN has gained much popularity during the last three decades. Therefore, the authors have focused on algorithms proposed during the last three decades. The selected databases were searched with popular keywords: “generalization performance,” “learning rate,” “overfitting” and “fixed and cascade architecture.” Combinations of the keywords were also used to get more relevant results. Duplicated articles in the databases, non-English language, and matched keywords but out of scope, were discarded.

The authors studied a total of 80 articles and classified them into six categories according to the nature of the algorithms proposed in these articles which aimed at improving the generalization performance and convergence rate of FNNs. To review and discuss all the six categories would result in the paper being too long. Therefore, the authors further divided the six categories into two parts (i.e. Part I and Part II). The current paper, Part I, investigates two categories that focus on learning algorithms (i.e. gradient learning algorithms for network training and gradient-free learning algorithms). Furthermore, the remaining four categories which mainly explore optimization techniques are reviewed in Part II (i.e. optimization algorithms for learning rate, bias and variance (underfitting and overfitting) minimization algorithms, constructive topology neural networks and metaheuristic search algorithms). For the sake of simplicity, the paper entitled “Machine learning facilitated business intelligence (Part II): Neural networks optimization techniques and applications” is referred to as Part II. This results in a division of 80 articles into 38 and 42 for Part I and Part II, respectively. After discussing the FNN algorithms with their technical merits and limitations, along with real-world management, engineering and health sciences applications for each individual category, the authors suggest seven (three in Part I and other four in Part II) new future directions which can contribute to strengthening the literature.

The FNN contributions are numerous and cannot be covered in a single study. The authors remain focused on learning algorithms and optimization techniques, along with their application to real-world problems, proposing to improve the generalization performance and convergence rate of FNNs with the characteristics of computing optimal hyperparameters, connection weights, hidden units, selecting an appropriate network architecture rather than trial and error approaches and avoiding overfitting.

This study will help researchers and practitioners to deeply understand the existing algorithms merits of FNNs with limitations, research gaps, application areas and changes in research studies in the last three decades. Moreover, the user, after having in-depth knowledge by understanding the applications of algorithms in the real world, may apply appropriate FNN algorithms to get optimal results in the shortest possible time, with less effort, for their specific application area problems.

The existing literature surveys are limited in scope due to comparative study of the algorithms, studying algorithms application areas and focusing on specific techniques. This implies that the existing surveys are focused on studying some specific algorithms or their applications (e.g. pruning algorithms, constructive algorithms, etc.). In this work, the authors propose a comprehensive review of different categories, along with their real-world applications, that may affect FNN generalization performance and convergence rate. This makes the classification scheme novel and significant.

The purpose of this paper is three-fold: to review the categories explaining mainly optimization algorithms (techniques) in that needed to improve the generalization performance and learning speed of the Feedforward Neural Network (FNN); to discover the change in research trends by analyzing all six categories (i.e. gradient learning algorithms for network training, gradient free learning algorithms, optimization algorithms for learning rate, bias and variance (underfitting and overfitting) minimization algorithms, constructive topology neural networks, metaheuristic search algorithms) collectively; and recommend new research directions for researchers and facilitate users to understand algorithms real-world applications in solving complex management, engineering and health sciences problems.

The FNN has gained much attention from researchers to make a more informed decision in the last few decades. The literature survey is focused on the learning algorithms and the optimization techniques proposed in the last three decades. This paper (Part II) is an extension of Part I. For the sake of simplicity, the paper entitled “Machine learning facilitated business intelligence (Part I): Neural networks learning algorithms and applications” is referred to as Part I. To make the study consistent with Part I, the approach and survey methodology in this paper are kept similar to those in Part I.

Combining the work performed in Part I, the authors studied a total of 80 articles through popular keywords searching. The FNN learning algorithms and optimization techniques identified in the selected literature are classified into six categories based on their problem identification, mathematical model, technical reasoning and proposed solution. Previously, in Part I, the two categories focusing on the learning algorithms (i.e. gradient learning algorithms for network training, gradient free learning algorithms) are reviewed with their real-world applications in management, engineering, and health sciences. Therefore, in the current paper, Part II, the remaining four categories, exploring optimization techniques (i.e. optimization algorithms for learning rate, bias and variance (underfitting and overfitting) minimization algorithms, constructive topology neural networks, metaheuristic search algorithms) are studied in detail. The algorithm explanation is made enriched by discussing their technical merits, limitations, and applications in their respective categories. Finally, the authors recommend future new research directions which can contribute to strengthening the literature.

The FNN contributions are rapidly increasing because of its ability to make reliably informed decisions. Like learning algorithms, reviewed in Part I, the focus is to enrich the comprehensive study by reviewing remaining categories focusing on the optimization techniques. However, future efforts may be needed to incorporate other algorithms into identified six categories or suggest new category to continuously monitor the shift in the research trends.

The authors studied the shift in research trend for three decades by collectively analyzing the learning algorithms and optimization techniques with their applications. This may help researchers to identify future research gaps to improve the generalization performance and learning speed, and user to understand the applications areas of the FNN. For instance, research contribution in FNN in the last three decades has changed from complex gradient-based algorithms to gradient free algorithms, trial and error hidden units fixed topology approach to cascade topology, hyperparameters initial guess to analytically calculation and converging algorithms at a global minimum rather than the local minimum.

The existing literature surveys include comparative study of the algorithms, identifying algorithms application areas and focusing on specific techniques in that it may not be able to identify algorithms categories, a shift in research trends over time, application area frequently analyzed, common research gaps and collective future directions. Part I and II attempts to overcome the existing literature surveys limitations by classifying articles into six categories covering a wide range of algorithm proposed to improve the FNN generalization performance and convergence rate. The classification of algorithms into six categories helps to analyze the shift in research trend which makes the classification scheme significant and innovative.

The aim of this case study‐based paper is to study the application of Six Sigma, a breakthrough improvement strategy in the field of cell site construction of a telecom company.

This research provides action research of a Six Sigma project using DMAIC methodology carried out in cell site construction function of a telecom company. The research illustrates how the various Six Sigma tools and techniques were applied in a mutually inclusive manner in one project. The infrastructure department of the company had constructed 900 cell sites last year, out of which 150 were not according to standards and were either disapproved by the operations department or underwent maintenance soon after their use. In 2010, the company spent US$ 0.5 million on rework and maintenance at these sites, thus highlighting the urgency of the problem.

The paper shows how, after the implementation of the Six Sigma project, the company made savings worth US$ 0.45 million.

Six Sigma as a means of waste reduction has gained popularity among researchers and practitioners. The literature on the methodology of Six Sigma and the management approach towards Six Sigma is burgeoning. While various Six Sigma tools and techniques and their application are discussed in literature independent of each other, the need has arisen to observe their systematic application as they apply in a project; every company can use this breakthrough improvement strategy to improve its processes by reducing waste and deriving the financial benefits.

The purpose of this paper is to develop a scale for the measurement of service quality in pharmaceutical supply chains.

A total of 413 pharmaceutical retailers working in the two biggest cities of Pakistan responded to a survey. Confirmatory factor analysis developed a valid and reliable service quality measurement scale with four dimensions and ten items.

Findings of this research are consistent with other service quality studies. There is no universal set of dimensions and items that determine service quality across a section of service industries. Service quality measurement must be adapted to fit the context.

Convenience sampling was undertaken for this research. This research contributes to the measurement of service quality by developing a valid and reliable measurement scale in a previously ignored sector.

The scale developed in this research can be used by pharmaceutical distribution companies in Pakistan to measure, control and improve the service quality provided to pharmaceutical retailers.

This research provides framework to researchers to build up more pharmaceutical supply chains service quality scale development studies in similar situations so that more concrete generalizations can be made.

The purpose of this paper is to identify the critical success factors of total quality management (TQM) in pharmaceutical wholesale distribution companies in Pakistan. The paper also seeks to contribute to reduce the existing lack of TQM studies in developing countries.

A 29‐item survey questionnaire is sent to 90 pharmaceutical distributors. Response rate is 56.7 per cent. Confirmatory factor analysis deletes one item in the scale purification process.

Data analysis reveal that “process design” (PD) is critical TQM success factor in pharmaceutical wholesale distribution companies in Pakistan. Like other studies in developing countries, top management in pharmaceutical wholesale distribution companies in Pakistan does not adequately support the TQM implementation.

The research is based in Pakistan only. Convenience sampling is done. The response size of the study is small (n=51) though the response rate and significance are high. Small response size prevents more complex analysis such as structural equation modelling.

Pharmaceutical wholesale distribution companies are more focused on “PD”. Increased top management support is required for proper TQM implementation.

This research provides framework to researchers to build up more TQM critical success factor studies in similar sector and situations so that more concrete generalizations can be made.