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As the technology evolves, the ways in which supply chain is coordinated improve. During a careful study on the intelligent wireless web (IWW) and its services for future applications, its great potentials for the implementation of a mobile real‐time system for supply chain coordination were realized. This paper seeks to introduce a development process for the IWW. In addition, it aims to explain the concept of mobile real‐time supply chain coordination, and propose and describe a practical model for this subject matter based on the most recent technologies including the IWW and agents.

Objectives were achieved through a thorough study on the IWW, agent technology, and the ways of applying them for mobile real‐time coordination in supply processes. As a method to conduct the research, first, the paper made out what the IWW services are and how one may develop them. Since mobile real‐time coordination is an absolutely innovative concept, the study prepared a comprehensive understanding of it and then, a practical framework was sketched and explained to implement the suggested system. The approach to this topic was a realistic one and an attempt was made to include all the prerequisites and details for the intended system.

In the course of the work, it was found that the IWW and other corresponding technologies have the greatest potentials ever available for the realization of a mobile real‐time supply chain coordination system and most of the chapters illustrate the claim.

Mobile real‐time coordination and its use in supply chains is something new. The development process for IWW proposed here is totally practicable and no other implementation scenario for the application of the IWW in mobile real‐time coordination has been suggested yet.

Acquiring and retaining profitable customers are major concerns of a business. In this paper, the customers of the Restoration and Beauty Clinic of Iran University are segmented using three value types: current value, expected value, and loyalty in a case study to predict the probability of customer churn and future purchase services in the clinic.

This study utilized customers' data records with nine data fields (socio‐demographic and transactional) from three year's transactions of the clinic. Logistic regression as a data mining technique is then used to predict the future behavior of the customers. In addition, the verification and the validation of the models are done using lift charts.

This research segments the customers of the clinic into four categories based on three values (current value, expected value, and loyalty). Then simple marketing strategies that might be adopted are suggested. These strategies might help the shareholders and experts of the clinic to promote relationships with patients and deliver better services to attract and retain their customers.

The results of this research enable public health agencies to evaluate the effectiveness of their policies and detect their shortcomings in order to better serve patients. Also, it will help to increase their profits from the clinics and raise customer satisfaction.

This research aims to focus on supply chain coordination and its three determinants, i.e. responsibility interdependence, uncertainty, and inter‐functional conflict, developing research propositions to conceptualize how supply chain coordination is driven by its determinants.

The research propositions are tested by a case study in a shoe factory to examine the differences between the theory and the practice of coordination.

The findings show that the firm carried out only piecemeal coordination in defining and fulfilling customer needs.

A supply chain is known as a network of facilities and distribution options that performs the functions of procurement of materials, transformation of these materials into intermediate and finished products, and the distribution of these finished products to customers. Supply chain coordination plays a critical role in integrating different actors along the supply chain to enhance performance.

The research on people and project factors is found extensively in general but not specific to software engineering. Secondly, the existing research has not concentrated on the communication and time complexity of the teams on software economics. The purpose this paper is to develop a model to investigate and quantify the impact of time pressure (TP) on software economics through the communication influence of software team sizes (TS).

A research model and five hypotheses are developed based on the gaps in the literature. The data set from International Software Benchmarking Standards Group repository is used for testing the hypotheses.

Important findings include: smaller TS tends to exert less TP on average; TP is directly proportional to software economics, however; and TP does not affect the productivity required for the software.

The study has the following implications: Selection of an appropriate TS for project completion that ensures minimum pressure on team members; and maximize software outcomes in stress-free environment.

This work is useful for organizations carrying out software projects with teamwork. The project managers can benefit from the results while planning the team factors for achieving the project goals.

The results uphold not to exert pressure on the team as it will not only affect the duly completion of the project but also the well-being of employees.

The paper is the first one where the proposition of TP estimation is done using TS and communication complexity, and empirically evaluating the impact of TP on four major software economics are the major key contributions of this research work.

This paper proposes a recommendation method that mines the semantic relationship between resources and combine it with collaborative filtering (CF) algorithm for crowdsourcing knowledge-sharing communities.

First, structured tag trees are constructed based on tag co-occurrence to overcome the tags' lack of semantic structure. Then, the semantic similarity between tags is determined based on tag co-occurrence and the tag-tree structure, and the semantic similarity between resources is calculated based on the semantic similarity of the tags. Finally, the user-resource evaluation matrix is filled based on the resource semantic similarity, and the user-based CF is used to predict the user's evaluation of the resources.

Folksonomy is a knowledge classification method that is suitable for crowdsourcing knowledge-sharing communities. The semantic similarity between resources can be obtained according to the tags in the folksonomy system, which can be used to alleviate the data sparsity and cold-start problems of CF. Experimental results show that compared with other algorithms, the algorithm in this paper performs better in mean absolute error (MAE) and F1, which indicates that the proposed algorithm yields better performance.

The proposed folksonomy-based CF method can help users in crowdsourcing knowledge-sharing communities to better find the resources they need.

The purpose of this paper is to evaluate the relationship between strategy and customer lifetime value (CLV). A new model was proposed for defining customers’ values based on the RFM model and segmenting bank customers using the K-means algorithm. In addition, the authors combined a new category with the delta model in order to analyze the behavior of each cluster.

This case study was based on an applied method following its objectives and a descriptive-analytic method in terms of data collection. In this research, the AHP, data mining and K-means clustering methods, as well as the discriminant analysis were applied for computing the weights of the indices, examining the relationship between the identified variables, clustering the records and ensuring the clustering accuracy based on the RFM model, respectively.

The paper confirmed the relationship between the strategies and CLV. For a cluster whose strategy was the best product, customers had a minimal CLV. For a cluster whose strategy was based on total customer solutions, customers had a median CLV. For a cluster whose strategy was a lock-in system, customers had a maximal CLV. The results suggested that the delta model with these three strategies could act as the CLV developers in two stages: conversion of transient customers to attached customers and conversion of attached customers to locked-in customers.

One of the limitations of this study was the lack of access to all the bank accounts and assessment of only the strategy type, while highlighting the exact association between every component of the strategies (e.g. structure, environment, etc.) and CLV as a dependent variable deemed to be of a great necessity. Hence, it is recommended that several studies on the relationships presented in this paper be performed to provide further insights into and guidelines on this issue in the future.

This study emphasized the relevance of strategy and CLV. Managers should differently treat customers in distinct CLV and loyalty levels. In other words, managers must segment their customers based on CLV and apply appropriate strategies for each segment.

This research tried to fulfill an identified need to study how strategy can be effect CLV through the application of the delta model with three strategic options.

In this research, the authors apply artificial neural networks (ANNs) to uncover non-linear relationships among factors that influence the productivity of ragpickers in the Indian context.

A broad long-term action research program provides a means to shape the research question and posit relevant factors, whereas ANNs capture the true underlying non-linear relationships. ANN models the relationships between four independent variables and three forms of waste value chains without assuming any distributional forms. The authors apply bootstrapping in conjunction with ANNs.

The authors identify four elements that influence ragpickers’ productivity: receptiveness to non-governmental organizations, literacy, the deployment of proper equipment/technology and group size.

This study provides a unique way to analyze bottom of the pyramid (BoP) operations via ANNs.

This study provides a road map to help ragpickers in India raise incomes while simultaneously improving recycling rates.

This research is grounded in the stakeholder resource-based view and the network–individual–resource model. It generalizes these theories to the informal waste value chain at BoP communities.

The purpose of this paper is to propose a new methodology that handles the issue of the dynamic behavior of customers over time.

A new methodology is presented based on time series clustering to extract dominant behavioral patterns of customers over time. This methodology is implemented using bank customers’ transactions data which are in the form of time series data. The data include the recency (R), frequency (F) and monetary (M) attributes of businesses that are using the point-of-sale (POS) data of a bank. This data were obtained from the data analysis department of the bank.

After carrying out an empirical study on the acquired transaction data of 2,531 business customers that are using POS devices of the bank, the dominant trends of behavior are discovered using the proposed methodology. The obtained trends were analyzed from the marketing viewpoint. Based on the analysis of the monetary attribute, customers were divided into four main segments, including high-value growing customers, middle-value growing customers, prone to churn and churners. For each resulted group of customers with a distinctive trend, effective and practical marketing recommendations were devised to improve the bank relationship with that group. The prone-to-churn segment contains most of the customers; therefore, the bank should conduct interesting promotions to retain this segment.

The discovered trends of customer behavior and proposed marketing recommendations can be helpful for banks in devising segment-specific marketing strategies as they illustrate the dynamic behavior of customers over time. The obtained trends are visualized so that they can be easily interpreted and used by banks. This paper contributes to the literature on customer relationship management (CRM) as the proposed methodology can be effectively applied to different businesses to reveal trends in customer behavior.

In the current business condition, customer behavior is changing continually over time and customers are churning due to the reduced switching costs. Therefore, choosing an effective customer segmentation methodology which can consider the dynamic behaviors of customers is essential for every business. This paper proposes a new methodology to capture customer dynamic behavior using time series clustering on time-ordered data. This is an improvement over previous studies, in which static segmentation approaches have often been adopted. To the best of the authors’ knowledge, this is the first study that combines the recency, frequency, and monetary model and time series clustering to reveal trends in customer behavior.

This study aims to address the critical need for innovative financing solutions in the global housing sector, focusing specifically on Malaysia’s distinct housing finance system encompassing both conventional and Islamic loans. The primary objective is to develop a transformative housing finance model that addresses affordability challenges and reshapes the Malaysian housing landscape.

The study presents an alternate housing finance model for Malaysia, integrating lower monthly payments and reduced household debt. Key variables include house price appreciation rates, interest rates, initial guarantee fees and loan-to-value ratios. Inspired by the Help to Buy (HTB) scheme, the model aligns with proven global initiatives for enhanced affordability, balancing payment amounts, loan interest rates and acceptable price thresholds.

The study’s findings promise to address affordability disparities and reshape Malaysia’s housing finance landscape. The emphasis is on introducing a structured repayment plan that offers a sustainable path to homeownership, particularly for low-income families. Incorporating the future value adaptation concept, inspired by reverse mortgages and Islamic finance, enhances adaptability, ensuring long-term sustainability despite economic shifts.

The proposed model promotes widespread access to homeownership, offering practical solutions for policymakers to improve affordability, prompting adaptable risk management strategies for financial institutions and empowering potential homebuyers with increased flexibility.

The study introduces a transformative housing finance model for Malaysia, merging elements from reverse mortgages, Islamic finance and the HTB scheme, offering potential applicability to similar systems globally.

The purpose of this paper is to identify factors that are crucial for the successful implementation of enterprise resource planning (ERP) systems. Although there are many factors that influence the success, this study focuses on factors related to the ERP project environment, namely, project management, team composition and competence, and business process reengineering.

The study was conducted using a survey questionnaire distributed to ERP users in Iranian organizations. In total, 384 responses were collected and analyzed.

A significant relationship was found between project management and team composition with ERP implementation success. The better the project management activities the more likely the implementation will be successful. Likewise, the possibility of successful implementation is higher when the ERP team is more coordinated and experienced.

ERP adopting organizations and managers could gain an understanding of the complexities inherent in ERP installations to avoid barriers and increase the likelihood of achieving desired results. The outcomes of this study are also useful to ERP vendors and consultants to prepare some strategies to overcome the misfit between their ERP products and ERP adopting organizations in developing countries.

This study is one of the few that examine the success of ERP implementation from the perspective of key stakeholders (operational/unit/functional managers). It has contributed to academic research by producing empirical evidence to support the theories of critical success factors and ERP implementation success. The findings may be useful to ERP vendors and other organizations in other countries, as they could be used as a guideline for future ERP adoption and implementation.