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The purpose of this paper is to develop an effective approach to support and guide production improvement processes utilising online product reviews.

This paper combines two methods: (1) natural language processing (NLP) to support advanced text mining to increase the accuracy of information extracted from product reviews and (2) quality function deployment (QFD) to utilise the extracted information to guide the product improvement process.

The paper proposes an approach to automate the process of obtaining voice of the customer (VOC) by performing text mining on available online product reviews while considering key factors such as the time of review and review usefulness. The paper enhances quality management processes in organisations and advances the literature on customer-oriented product improvement processes.

Online product reviews are a valuable source of information for companies to capture the true VOC. VOC is then commonly used by companies as the main input for QFD to enhance quality management and product improvement. However, this process requires considerable time, during which VOC may change, which may negatively impact the output of QFD. This paper addresses this challenge by providing an improved approach.

Contact centers (CCs) are one of the main touch points of customers in an organization. They form one of the inputs to customer relationship management (CRM) to enable an organization to efficiently resolve customer queries. CCs have an important impact on customer satisfaction and are a strategic asset for CRM systems. The purpose of this paper is to review the current literature on CCs and identify their shortcomings to be addressed in the current digital age.

The current literature on CCs can be classified into the analytical and the managerial aspects of CCs. In the former, data mining, text mining, and voice recognition techniques are discussed, and in the latter, staff training, CC performance, and outsourced CCs are discussed.

With the growth of information and communication technologies, the information that CCs must handle both in terms of type and volume, has changed. To deal with such changes, CCs need to evolve in terms of their operation and public relations. The authors present a state-of-the-art review of the challenges in identifying the gaps in order to have the next generation of CCs. Lack of an interactive CC and lack of data integrity for CCs are highlighted as important issues that need to be dealt with properly by CCs.

As far as the authors know, this is the first paper that reviews CCs’ literature by providing the comprehensive survey, critical evaluation, and future research.

This study aims to investigate the development of predictive tools in performance measurement and management (PMM), and modeling of a forward‐looking method to help managers to quantitatively target performance measures based on achieving desired improvement, minimum cost and strategic priorities.

A case‐based methodology is used to test the conceptual approach in a production system. Mathematical models are used to model modules of the proposed approach. The proposed approach is applied to an actual conventional power plant unit to show its applicability and superiority over conventional methods.

The developed system enables managers to develop systematic ways to manage future performance; for example, planning, performance forecasting and target setting. The predictive ability of the developed system is comparable with the judgment of the manager in the case company.

This paper proposes the use of mathematical models in the development of performance measures targeted on performance prediction and desired improvement. The paper also offers practical help to organizations to embed a forward‐looking capability into their operations.

Sharing knowledge with business partners is a challenging issue as firms need to share their valuable know-how assets with individuals or other companies out of their organizational boundaries. As supply chain management (SCM) deals with various stakeholders, firms face difficulties with privacy and ownership when they share their know-how with suppliers or business partners. This study introduces blockchain technology as a mediator in improving knowledge sharing (KS) practices in supply chains.

The data have been collected from surveys with 116 experts working in blockchain start-ups and organizations, and the authors used structural equation modeling for its analysis.

The results show that two features of blockchain technology, namely transparency and security, have the highest impacts on mediating knowledge sharing impacts on supply chain performance. The authors’ findings also highlight that among the performance metrics of SCM, speed is highly improved when blockchain technology is used for knowledge sharing. Their study provides guidance for managers on how to improve SCM performance through KS, which is empowered by a blockchain system.

The authors’ findings help organizations to improve supply chain actions, improve innovation, enhance competitive advantage and increase the speed of relationships in the supply chain. The research also contributes literature by analyzing the key factors showing how knowledge sharing structure may be improved by blockchain technology which would be helpful for both academics and practitioners.

Present research aims to study the determinants of big data analytics (BDA) adoption intention and outsourcing in the context of small and medium-sized enterprises (SMEs).

The partial least squares approach was employed to analyse data collected from 187 SMEs.

The findings indicate that relative advantage, competitive pressure and environmental uncertainty significantly influence SMEs' BDA adoption intention. Top management support moderates the association between the regulatory environment and BDA adoption intention. Furthermore, organisational readiness moderates negatively the association between BDA adoption intention and propensity to outsource BDA.

The findings benefit SMEs' managers/owners in making well-informed decisions in the BDA adoption process.

The majority of the previous research on BDA adoption intention is limited to large corporations. To address the gap on determinant factors of BDA adoption intention among SMEs, the drivers of BDA adoption intention and propensity to outsource were investigated using the technology-organisation-environment model.

This paper follows a systematic literature review (SLR) method covering the published studies until March 2021. The authors have extracted the related studies from different online databases utilizing quality-assessment-criteria. In order to review high-quality studies, 32 papers have been chosen through the paper selection process. The selected papers have been categorized into three main groups, decision-making methods (17 papers), meta-heuristic methods (8 papers) and fuzzy-based methods (7 papers). The existing methods in each group have been examined based on important qualitative parameters, namely, time, cost, scalability, efficiency, availability and reliability.

Cloud computing is known as one of the superior technologies to perform large-scale and complex computing. With the growing tendency of network service users to utilize cloud computing, web service providers are encouraged to provide services with various functional and non-functional features and supply them in a service pool. In this regard, choosing the most appropriate services to fulfill users' requirements becomes a challenging problem. Since the problem of service selection in a cloud environment is known as a nondeterministic polynomial time (NP)-hard problem, many efforts have been made in recent years. Therefore, this paper aims to study and assess the existing service selection approaches in cloud computing.

The obtained results indicate that in decision-making methods, the assignment of proper weights to the criteria has a high impact on service ranking accuracy. Also, since service selection in cloud computing is known as an NP-hard problem, utilizing meta-heuristic algorithms to solve this problem offers interesting advantages compared to other approaches in discovering better solutions with less computational effort and moving quickly toward very good solutions. On the other hand, since fuzzy-based service selection approaches offer search results visually and cover quality of service (QoS) requirements of users, this kind of method is able to facilitate enhanced user experience.

Although the current paper aimed to provide a comprehensive study, there were some limitations. Since the authors have applied some filters to select the studies, some effective works may have been ignored. Generally, this paper has focused on journal papers and some effective works published in conferences. Moreover, the works published in non-English formats have been excluded. To discover relevant studies, the authors have chosen Google Scholar as a popular electronic database. Although Google Scholar can offer the most valid approaches, some suitable papers may not be observed during the process of article selection.

The outcome of the current paper will be useful and valuable for scholars, and it can be a roadmap to help future researchers enrich and improve their innovations. By assessing the recent efforts in service selection in cloud computing and offering an up-to-date comparison of the discussed works, this paper can be a solid foundation for understanding the different aspects of service selection.

Although service selection approaches have essential impacts on cloud computing, there is still a lack of a detailed and comprehensive study about reviewing and assessing existing mechanisms in this field. Therefore, the current paper adopts a systematic method to cover this gap. The obtained results in this paper can help the researchers interested in the field of service selection. Generally, the authors have aimed to specify existing challenges, characterize the efficient efforts and suggest some directions for upcoming studies.

This study aims to propose a quantitative approach to reduce the number of suppliers in an organization. This method is based on grouping, and different parts are grouped based on the capabilities they need and are allocated to suppliers who have these capabilities. In this regard, an integrated model for supplier reduction and grouping of parts using a group technology-based algorithm is proposed.

Design science research methodology was used in this study. The main problem under investigation is a large number of suppliers in an organization’s supply base. The proposed model was used to solve this problem in the electric motor industry.

The results of implementing the proposed model in the electric motor industry showed that reducing suppliers had a significant effect on reducing cost, increasing information sharing, increasing supplier innovation and technology, enhancing the relationship between buyers and sellers and reducing risks in the production process.

From a managerial point of view, reducing the number of suppliers plays an important role in the company’s overall strategy, and seems to be a prerequisite for building a strong supplier partnership and an effective supply chain, and will have many benefits for the focal company and suppliers.

To the best of the authors’ knowledge, grouping and formation of product families have never been performed based on the similarity of the operational capabilities required for producing parts, and it has not been addressed as a solution for reducing suppliers.

Currently, the evaluation of scientific performance of universities is one of the important indicators in various ranking systems. One way to evaluate the academic performance of universities is to analyze the scientific documents of universities in reputable international databases. The purpose of this article is to analyze and evaluate the scientific publications of Alzahra University (Iran) as the top 100–200 universities during 1986–2019.

This study was performed using bibliometrics and visualization techniques. The Scopus database was used to collect data. Affiliation search and advanced search were used to retrieve the data. Excel, VOSviewer and CRExplorer software were used to analyze the data.

The results showed that the scientific publications and received citations by Alzahra University documents during the time have been upward. At the national level, it was the most scientific collaboration with researchers at the University of Tehran. Also at the international level, the most scientific collaboration has been with the United States, Canada and Germany. In total, 80% of scientific publications were published by 20% of authors. Also, 70% of the highly cited articles were published in journals with quartile 1. Finally, clustering results showed that Alzahra University's scientific publications are in five main categories, including “chemistry,” “physics,” “biology,” “psychology and educational sciences” and “accounting sciences, management, and computer science.”

This study could be a good model for evaluating the performance of scientific productions of universities and scientific institutions with bibliometrics and visualization approaches.