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The cybernetic approach differs significantly from the conventional reductionist methods of natural and biological sciences. Norbert Wiener established the theory of cybernetics as a science of control and communication process in living beings (human and animals) and machines. Dutta Majumder in his Norbert Wiener Award winning paper extended the approach to include integrated complex human machine systems and functions with general systems theory as a unitary science laying the mathematical foundation for unifying observing systems, observed systems and the act of observing as indicated in von Foerster’s concept of second‐order cybernetics. Both from the point of view of ontology and that of epistemology the cybernetic approach now enables computer technology to incorporate artificial intelligence (AI) and expert system (ES) for knowledge based instrumentation for diagnostics and therapy planning. Presents the results of a project for development of a knowledge based framework for combining different modalities of medical image processing such as CT, MR(T1), MR(T2), SPECT, PET, USG etc. whichever is relevant for particular pathological investigation for diagnostics and therapeutic planning. Experiments were conducted with (a) Alzheimer’s patient data and (b) detection and grading of malignancy with oncological data for the cancer screening system.

Purpose – The aim is to review developments on the internet, especially those of general cybernetic interest.Design/methodology/approach – A recent episode involving a hoax paper is reviewed, along with discussions of the implications for paper refereeing and information dissemination generally. Some sources of information on nanotechnology, with applications in medicine, are reviewed.Findings – That a hoax paper was accepted casts doubt on the review process, but the situation is not clear‐cut and the usefulness of mammoth conferences is also questioned. Nanotechnology is shown to be poised for major advances.Practical implications – The generation of a convincing hoax paper is an interesting technical achievement in itself. Implications for the review process are explored. Sources of information on nanotechnology are indicated.Originality/value – It is hoped this is a valuable periodic review.

Considers that in ecosystem, landscape and global ecology, an energetics reading of ecological systems is an expression of a cybernetic, systemic and holistic approach. In ecosystem ecology, the Odumian paradigm emphasizes the concept of emergence, but it has not been accompanied by the creation of a method that fully respects the complexity of the objects studied. In landscape ecology, although the emergentist, multi‐level, triadic methodology of J.K. Feibleman and D.T. Campbell has gained acceptance, the importance of emergent properties is still undervalued. In global ecology, the Gaia hypothesis is an expression of an organicist metaphor, while the emergentist terminology used is incongruent with the underlying physicalist cybernetics. More generally, an analytico‐additional methodology and the reduction of the properties of ecosystems to the laws of physical chemistry render purely formal any assertion about the emergentist and holistic nature of the ecological systems studied.

This essay reports work in cybernetics that it is believed can shed light on methodological and conceptual issues in the study of child development. To do so, cybernetics is placed in the larger context of the philosophy of science, drawing particularly on the work of Frederick Suppe and Nicholas Rescher. The concept of explanation in cybernetics is used to elucidate controversies concerning “mechanistic” and “organismic” types of explanation. An account is given of several models that appear to be of use in explicating the concepts of development, self‐organisation and morphogenesis. Finally, the distinctions between first‐ and second‐order cybernetics (due to von Foerster) and taciturn and language oriented systems (due to Pask) are invoked to encompass the social dimensions of child development.

PurposeTo describe six management thoughts that evolved during the past century in the USA and to link them to the concept of cybernetic‐scanning management (CSM) and to vaguely explore the extent it is practiced in today's organizations.Design/methodology/approachEmployed MBA students were asked to rate the extent their corporations, as they perceive it, practice the CSM framework. The framework was fully discussed in class prior to the ratings. Students were asked to prepare a term paper describing the rationality for their ratings.FindingsToday's organizations pay a lot of attention to the external environment, their organizational structures are still too tall, and the Theory X managerial mindset is still more prevalent than the Theory Y mindset.Research limitations/implicationsThe study involved only MBA students enrolled in the writer's management and organization course in the school of business at Montclair State University. Therefore, the study's findings cannot be generalized.Practical implicationsOrganizations need to establish a system where there is more internal communication, cooperation and collaboration among their subunits (e.g. 9/11 and Hurricane Katrina).Originality/valueThe study tells us how the past influences the present and the future.

PurposeThe paper aims to explore how the participants in the closed-loop supply chain (CLSC) select collecting strategies under the scenarios of information symmetry and asymmetry, and to investigate the value of corporate social responsibility (CSR) cost information for participants.Design/methodology/approachThis paper constructs a two-echelon CLSC Stackelberg game consisting of one manufacturer and one retailer, where the manufacturer undertakes CSR and is responsible for the remanufacturing of used products. First, the authors establish two collecting models under information symmetry: manufacturer-collecting and retailer-collecting. Second, the authors construct two collecting models under information asymmetry and propose a two-part tariff contract to coordinate the participants’ profits. Finally, the authors make a numerical analysis to verify the results.FindingsUnder information symmetry, the profit of the participant who does not undertake collecting is positively related to the collecting rate, and the other participant may not benefit from the increase in the collecting rate. Under information asymmetry, the manufacturer will still select the retailer-collecting channel. Asymmetric information only affects the retailer’s selection. In addition, the manufacturer’s private CSR cost information is always valuable to the retailer.Originality/valueThis paper first explores the influence of the CSR cost information value on the selection of collecting channel under information asymmetry in the CLSC. The results can help company managers choose optimal collecting channel under information symmetry or under information asymmetry.

PurposeThis paper aims to establish a theoretic framework to provide a fundamental understanding of cyberspatial objects, their existence and their identification scheme while providing a connection between cyber-enabled spaces and cyberspace. It develops an avenue to quantify general philosophical and theoretical questions, precisely, inherently spatial basis that produces an unprecedented space–time continuum, in which cyber-enabled relations evolve.Design/methodology/approachMultidisciplinary theoretical approaches are needed to describe complex systems, which in this paper are integrated in a quest for the principles underlying the structural organization and dynamics of cyberspace. A theoretic framework is presented, and the spatial conception of cyber-enabled physical, social, information and thinking spaces and entities existence are provided.FindingsWith spatial objects and spatial properties, cyberspace is inherently spatial. Its basic constructs are founded on its spatial qualities and producing radical space–time compression, cyber-enabled spaces in which dynamic relations develop and thrive. The cyberspatial object operations are primarily built on foundations that depend on physical space and other spatial metaphors. Information space, basically missing in the literature, is an important part of cyberspace.Research limitations/implicationsThis work suggested a novel analytical approach to describing cyberspace from broader perspectives and fields. Due to the novelty and divergence of cyber concepts, an interdisciplinary study and methodology are needed. Thus, more research toward theoretical direction could help many of the practical implementations of concepts.Practical implicationsThe research is of particular significance in cyberspatial mechanics to describe the dynamics and behavior of cyber physical systems. For example, object-based analysis functions like spatial query, node pattern analysis, cluster analysis, spatial similarity analysis and location modeling.Originality/valueComplementing the existing literature and defining information space to the research sphere, a theoretical framework providing a fundamental understanding of cyberspatial objects and the general cyberspace foundation has been proposed, resulting in a formalized concept of existence, interactions and applications and services, with respect to philosophy, science and technology, respectively.

PurposeThe purpose of this paper is to propose an innovative integration method based on decision-theoretic rough set and the extended VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) methods to address the resilient-sustainable supplier selection and order allocation (SS/OA) problem.Design/methodology/approachSpecifically, a two-stage approach is designed in this paper. First, the decision-theoretic rough set is employed to calculate the rough number for coping with the subjective uncertainty of data and assigning the weights for a resilient-sustainable evaluation criterion. On this basis, the supplier resilient-sustainable performance is ranked in combination with the extended VIKOR method. Second, a novel multi-objective optimization model is proposed that applies an improved genetic algorithm to select the resilient-sustainable supplier and allocate the corresponding order quantity under a multi-tier supplier network.FindingsThe results reveal that joint consideration of resilience and sustainability is essential in the SS/OA process. The method proposed in this study based on decision-theoretic rough sets and the extended VIKOR method can handle imprecise information flexibly, reduce information loss and obtain acceptable solutions for decision-makers. Numerical cases validate that this integrated approach can combine resilience and sustainability for effective and efficient SS/OA.Practical implicationsThis paper provides industry managers with a new perspective on SS/OA from a resilience and sustainability perspective as a basis for best practices for industry resilience and sustainability. The proposed method helps to evaluate the resilient-sustainable performance of potential suppliers, which is applicable to solving real-world SS/OA problems and has important practical implications for the resilient-sustainable development of supply chains.Originality/valueThe two interrelated priorities of resilience and sustainability have emerged as key strategic challenges in SS/OA issues. This paper is the first study of this issue that uses the proposed integrated approach.

PurposeThis 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.Design/methodology/approachFirst, 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.FindingsFolksonomy 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.Originality/valueThe proposed folksonomy-based CF method can help users in crowdsourcing knowledge-sharing communities to better find the resources they need.

PurposeThe purpose of this study was to demonstrate a cloud business intelligence model for industrial SMEs. An initial model was developed to accomplish this, followed by validation and finalization of the cloud business intelligence model. Additionally, this research employs a mixed-techniques approach, including both qualitative and quantitative methods. This paper aims to achieve the following objectives: (1) Recognize the Cloud business intelligence concepts. (2) Identify the role of cloud BI in SMEs. (3) Identify the factors that affect the design and presenting a Cloud business intelligence model based on critical factors affecting SMEs during pandemic COVID-19. (4) Discuss the importance of Cloud BI in pandemic COVID-19 for SMEs. (5) Provide managerial implications for using Cloud BI effectively in Iran’s SMEs.Design/methodology/approachIn the current study, an initial model was first proposed, and the cloud business intelligence model was then validated and finalized. Moreover, this study uses a mixed-methods design in which both qualitative and quantitative methods are used. The fuzzy Delphi Method has been applied for parameter validation purposes, and eventually, the Cloud business intelligence model has been presented through exploiting the interpretive structural modeling. The partial least squares method was also applied to validate the model. Data were also analyzed using the MAXQDA and Smart PLS software package.FindingsIn this research, from the elimination of synonym and frequently repeated factors and classification of final factors, six main factors, 24 subfactors and 24 identifiers were discovered from the texts of the relevant papers and interviews conducted with 19 experts in the area of BI and Cloud computing. The main factors of our research include drivers, enablers, competencies, critical success factors, SME characteristics and adoption. The subfactors of included competitors pressure, decision-making time, data access, data analysis and calculations, budget, clear view, clear missions, BI tools, data infrastructure, information merging, business key sector, data owner, business process, data resource, data quality, IT skill, organizational preparedness, innovation orientation, SME characteristics, SME activity, SME structure, BI maturity, standardization, agility, balances between BI systems and business strategies. Then, the quantitative part continued with the fuzzy Delphi technique in which two factors, decision-making time and agility, were deleted in the first round, and the second round was conducted for the rest of the factors. In that step, 24 factors were assessed based on the opinions of 19 experts. In the second round, none of the factors were removed, and thus the Delphi analysis was concluded. Next, data analysis was carried out by building the structural self-interaction matrix to present the model. According to the results, adoptability is a first-level or dependent variable. Regarding the results of interpretive structural modeling (ISM), the variable of critical success factors is a second-level variable. Enablers, competencies and SME characteristics are the third-level and most effective variables of the model. Accordingly, the initial model of Cloud BI for SMEs is presented as follows: The results of ISM revealed the impact of SME characteristics on BI critical success factors and adoptability. Since this category was not an underlying category of BI; thus, it played the role of a moderating variable for the impact of critical success factors on adoptability in the final model.Research limitations/implicationsSince this study is limited to about 100 SMEs in the north of Iran, results should be applied cautiously to SMEs in other countries. Generalizing the study's results to other industries and geographic regions should be done with care since management perceptions, and financial condition of a business vary significantly. Additionally, the topic of business intelligence in SMEs constrained the sample from the start since not all SMEs use business intelligence systems, and others are unaware of their advantages. BI tools enable the effective management of companies of all sizes by providing analytic data and critical performance indicators. In general, SMEs used fewer business intelligence technologies than big companies. According to studies, SMEs understand the value of simplifying their information resources to make critical business choices. Additionally, they are aware of the market's abundance of business intelligence products. However, many SMEs lack the technical knowledge necessary to choose the optimal tool combination. In light of the frequently significant investment required to implement BI approaches, a viable alternative for SMEs may be to adopt cloud computing solutions that enable organizations to strengthen their systems and information technologies on a pay-per-use basis while also providing access to cutting-edge BI technologies at a reasonable price.Practical implicationsBefore the implementation of Cloud BI in SMEs, condition of driver, competency and critical success factor of SMEs should also be considered. These will help to define the significant resources and skills that form the strategic edge and lead to the success of Cloud BI projects.Originality/valueMost of the previous studies have been focused on factors such as critical success factors in cloud business intelligence and cloud computing in small and medium-sized enterprises, cloud business intelligence adoption models, the services used in cloud business intelligence, the factors involved in acceptance of cloud business intelligence, the challenges and advantages of cloud business intelligence, and drivers and barriers to cloud business intelligence. None of the studied resources proposed any comprehensive model for designing and implementing cloud business intelligence in small and medium-sized enterprises; they only investigated some of the aspects of this issue.

PurposeCloud eases information processing, but it holds numerous risks, including hacking and confidentiality problems. It puts businesses at risk in terms of data security and compliance. This paper aims to maximize the covered human resource (HR) vulnerabilities and minimize the security costs in the enterprise cloud using a fuzzy-based method and firefly optimization algorithm.Design/methodology/approachCloud computing provides a platform to improve the quality and availability of IT resources. It changes the way people communicate and conduct their businesses. However, some security concerns continue to derail the expansion of cloud-based systems into all parts of human life. Enterprise cloud security is a vital component in ensuring the long-term stability of cloud technology by instilling trust. In this paper, a fuzzy-based method and firefly optimization algorithm are suggested for optimizing HR vulnerabilities while mitigating security expenses in organizational cloud environments. MATLAB is employed as a simulation tool to assess the efficiency of the suggested recommendation algorithm. The suggested approach is based on the firefly algorithm (FA) since it is swift and reduces randomization throughout the lookup for an optimal solution, resulting in improved performance.FindingsThe fuzzy-based method and FA unveil better performance than existing met heuristic algorithms. Using a simulation, all the results are verified. The study findings showed that this method could simulate complex and dynamic security problems in cloud services.Practical implicationsThe findings may be utilized to assist the cloud provider or tenant of the cloud infrastructure system in taking appropriate risk mitigation steps.Originality/valueUsing a fuzzy-based method and FA to maximize the covered HR vulnerabilities and minimize the security costs in the enterprise cloud is the main novelty of this paper.

PurposeThis study aims to provide an overview of what characterizes the current state of research in the field of cloud computing use in human resource management (HRM) with the identification, analysis and classification of the existing literature and lines of research addressed and to provide guidance for future research.Design/methodology/approachThe systematic literature review (SLR) technique has been used to identify, select, analyze and evaluate the existing publications on cloud computing and HRM. A total of 35 papers published up to December 2020 have been obtained from the Web of Science (WoS) scientific database. The research design has allowed us to determine what characterizes the current state of research on the use of cloud computing in HRM and obtain a novel classification of the literature that identifies four lines of research and the contributions in each line and has allowed us to define the future research agenda.FindingsThe four groups into which the papers on the cloud computing-HRM relationship have been classified are: (1) studies focused on the development of cloud platforms for HRM that highlight technical aspects, (2) papers that focus on the concept of human resource elasticity, (3) papers on the adoption and/or implantation of cloud platforms for HRM and (4) studies that highlight the effects or implications of cloud platforms for HRM. This paper proposes some new opportunities for future research and presents some helpful implications from the theoretical and management perspectives.Research limitations/implicationsThis study uses only scientific articles in the WoS database with a Journal Citation Report (JCR) or SCImago Journal Rank (SJR) impact.Originality/valueThis paper provides an overview of the knowledge on cloud computing and HRM research and offers recommendations for future research.

PurposeOne of the main advantages of the proposed model is that it is flexible to generate n-day pairings simultaneously. It means that, despite previous researches, one-day to n-day pairings can be generated in a single model. The flexibility in generating parings causes that the proposed model leads to better solutions compared to existing models. Another advantage of the model is minimizing the risk of COVID-19 by limitation of daily flights as well as elapsed time minimization. As airports are among high risk places in COVID-19 pandemic, minimization of infection risk is considered in this model for the first time. Genetic algorithm is used as the solution approach, and its efficiency is compared to GAMS in small and medium-size problems.Design/methodology/approachOne of the most complex issues in airlines is crew scheduling problem which is divided into two subproblems: crew pairing problem (CPP) and crew rostering problem (CRP). Generating crew pairings is a tremendous and exhausting task as millions of pairings may be generated for an airline. Moreover, crew cost has the largest share in total cost of airlines after fuel cost. As a result, crew scheduling with the aim of cost minimization is one of the most important issues in airlines. In this paper, a new bi-objective mixed integer programming model is proposed to generate pairings in such a way that deadhead cost, crew cost and the risk of COVID-19 are minimized.FindingsThe proposed model is applied for domestic flights of Iran Air airline. The results of the study indicate that genetic algorithm solutions have only 0.414 and 0.380 gap on average to optimum values of the first and the second objective functions, respectively. Due to the flexibility of the proposed model, it improves solutions resulted from existing models with fixed-duty pairings. Crew cost is decreased by 12.82, 24.72, 4.05 and 14.86% compared to one-duty to four-duty models. In detail, crew salary is improved by 12.85, 24.64, 4.07 and 14.91% and deadhead cost is decreased by 11.87, 26.98, 3.27, and 13.35% compared to one-duty to four-duty models, respectively.Originality/valueThe authors confirm that it is an original paper, has not been published elsewhere and is not currently under consideration of any other journal.

PurposeThe human resource (HR) allocation problem is one of the critical dimensions of the project management process. Due to this nature of the problem, researchers are continually optimizing one or more critical scheduling and allocation challenges in different ways. This study aims to optimize two goals, increasing customer satisfaction and reducing costs using the imperialist competitive algorithm.Design/methodology/approachCloud-based e-commerce applications are preferred to conventional systems because they can save money in many areas, including resource use, running expenses, capital costs, maintenance and operation costs. In web applications, its core functionality of performance enhancement and automated device recovery is important. HR knowledge, expertise and competencies are becoming increasingly valuable carriers for organizational competitive advantage. As a result, HR management is becoming more relevant, as it seeks to channel all of the workers’ energy into meeting the organizational strategic objectives. The allocation of resources to maximize benefit or minimize cost is known as the resource allocation problem. Since discovering solutions in polynomial time is complicated, HR allocation in cloud-based e-commerce is an Nondeterministic Polynomial time (NP)-hard problem. In this paper, to promote the respective strengths and minimize the weaknesses, the imperialist competitive algorithm is suggested to solve these issues. The imperialist competitive algorithm is tested by comparing it to the literature’s novel algorithms using a simulation.FindingsEmpirical outcomes have illustrated that the suggested hybrid method achieves higher performance in discovering the appropriate HR allocation than some modern techniques.Practical implicationsThe paper presents a useful method for improving HR allocation methods. The MATLAB-based simulation results have indicated that costs and waiting time have been improved compared to other algorithms, which cause the high application of this method in practical projects.Originality/valueThe main novelty of this paper is using an imperialist competitive algorithm for finding the best solution to the HR allocation problem in cloud-based e-commerce.

PurposeThe purpose of paper is to develop a performance evaluation framework for manufacturing industry to evaluate overall manufacturing performance.Design/methodology/approachThe best-worst method (BWM) is used to aid in developing a performance evaluation framework for manufacturing industry to evaluate their overall performance.FindingsThe proposed BWM-based manufacturing performance evaluation framework is implemented in an Indian steel manufacturing company to evaluate their overall manufacturing performance. Operational performance of the organization is very consistent and range between 60% and 70% throughout the year. Management performance can be seen high in the 1st and 2nd quarter of the financial year ranging from 70% to 80%, whereas a slight decrease in the management performance is observed in the 3rd and 4th quarter ranging from 60% to 70%. The social stakeholder performance has a peak in first quarter ranging from 80% to 100% as at start of financial year.Originality/valueThis paper utilized BWM, a MCDM method in developing a performance evaluation index that integrates several categories of manufacturing and evaluates overall manufacturing performance. This is a novel contribution to BWM decision-making application.

PurposeAs inter-company cooperation and competition grow, orchestration capability plays an increasingly important role. This paper aims to present an overview of orchestration capability in the business and management field, identify the current state and explore future research trends.Design/methodology/approachThis literature review is based on 132 papers collected from the Web of Science (WoS) Core Collection data (1997–March 2021). HistCite was used to analyze the year of publication, leading scholars, influential articles, key journals, top countries and institutions. Research streams were identified from analysis of co-citation, bibliographic coupling and keyword co-occurrence by HistCite and VOSviewer.FindingsThis paper finds that Hitt MA is the most prolific scholar, and the Strategic Management Journal is the most dominating publication among the orchestration capability publications. The USA and Texas A&M University are the most influential countries and institutions, respectively. Three major clusters are identified based on citation mapping, bibliographic coupling analysis of documents and keywords co-occurrence analysis: dynamic capability and resource-based view, resource orchestration and network orchestration. Based on the three clusters, the authors analyze how resource orchestration and network orchestration research develops over time and summarize the evolutionary path of orchestration capability literature.Research limitations/implicationsThis article builds on data from WoS Core Collection, and some new but important articles may not be analyzed, since bibliometrics consider high citation as an indicator to select influential articles.Practical implicationsWith the rapid development of the digital economy, the frequent interactions between companies pose many challenges for businesses. Enterprises can take orchestration actions effectively and efficiently from various perspectives and continuously improve their orchestration capabilities in collaboration with partners to achieve and sustain competitive advantages.Originality/valueThis paper provides a systematic review of orchestration capability using bibliometric analysis, which has not been employed in previous studies. Besides, this article presents a broad understanding of how scholars have researched the subject over the years.

PurposeBPR is “the central reconsideration and thorough restructuring of business procedures to enhance the critical and contemporary aspects of performance like the expense, quality, service, and speed”. Also, as it's a key factor for guaranteeing businesses' achievement, however, the profound discussion about the BPR is very rare as far as we know. We need more studies regarding the subject due to the absence of BPR works in the logistics industry. Hence, this study investigates the Systematic Literature Review (SLR) of BPR for logistics companies, leading the managers and writers active in BPR, and making them aware of the present, past and future trends in this discipline.Design/methodology/approachAs BPR is a necessary foundation for ensuring enterprise systems' success, this study will systematically investigate the BPR in logistics. The proposed BPR in logistics research classification framework is based on a comprehensive literature review, which concentrates on peer-reviewed journal papers published until 2019. A total of 22 academic sources have been retrieved and analyzed in terms of research purpose and nature, the employed method, theoretical approach and analysis level.FindingsThe findings of this paper showed that BPR companies outperform the non-BPR ones regarding information computing, technology uses, organizational architecture, coordination and all key logistics procedures. The results can motivate non-BPR logistics organizations to reassess the feasibility of these plans.Research limitations/implicationsThis paper provides an overview of BPR to young academics. Also, it identifies some distinct research gaps that could be worth studying. However, this paper may be restricted by choice of dimensions and the selection of relevant articles. In turn, researchers need to become more innovative in terms of their research techniques when examining BPR implementation.Practical implicationsThis paper guides researchers and practitioners to insight into published research work and their findings. The findings are valuable to logistics firms in an emerging market, as logistics resources may affect logistics service costs and quality. Also, it discusses indications for future research in BPR. It emphasizes the need to bridge the lacuna between BPR theory and evidence-based practice. Furthermore, it provides a better understanding of BPR implementation, which can be applied toward overcoming operational difficulties during the implementation process.Originality/valueThis paper fulfills an identified need for a comprehensive classification framework of BPR in logistics studies. We can consider it as the first-evaluated methodically gathered workaround BPR in logistics. It essentially provides both academics and practitioners with a conceptual map of existing BPR in logistics research and points out future research opportunities.

PurposeChina's agricultural production trusteeship is characterized by the organic link between household operation of small-scale peasant economy and agricultural socialized services, which releases agricultural development vitality and promotes agricultural modernization. As one of the agricultural production trusteeship modes, the whole process trusteeship is suitable for the actual situation of China's aging population and labor force transfer. This paper aims to construct an evolutionary game model containing multistakeholder to explore the behavior decisions through numerical simulation and to provide useful suggestions for the formation of a positive and stable trusteeship relationship and the sound development of the whole process trusteeship of agricultural production.Design/methodology/approachThe paper incorporates village committees, service organizations and farmers into the same research framework, selects “guarantee + dividends” as the income distribution method and applies the evolutionary game method to analyze behavioral choices and evolutionary paths of stakeholders. By constructing the expectation function, establishing the replicator dynamic equations and analyzing the tripartite evolutionary stability strategy, the authors explore the factors that affect the stakeholders’ strategy choice and determine asymptotically stable points and stability conditions.Findings(1) There is a game relationship among village committees, farmers and service organizations in the whole process trusteeship of agricultural production, asymptotically stable points (0,0,1) and (1,1,1) are obtained through calculation. (2) The proportion of stakeholders' strategy choice, the weight of the whole process trusteeship of agricultural production in the performance appraisal of the village committee, the village committee's supervision cost, the village committee's reputation effect and the penalty for false dividends of the service organization will affect the speed at which the curve representing the tripartite relationship approaches two asymptotically stable points.Research limitations/implicationsThe countermeasures proposed in the paper have excellent reference value. (1) For areas that have realized the project: Village committees can solve the trusteeship problems exposed in the initial areas and improve farmers' satisfaction with the project. (2) For areas that have not realized the project: Those regions will receive more experience references and enhance their confidence in this project. The limitation of the paper is that it takes the main grain-producing areas in only the three northeastern provinces of China as the research object. The next research object will be extended to the whole country.Practical implicationsThis paper propose strategies for realizing the orderly operation of the whole process trusteeship of agricultural production: first, increase the proportion of stakeholders' strategy choices; second, reduce the village committee's supervision cost; third, increase the weight of the whole process trusteeship of agricultural production in the performance appraisal of the village committee; fourth, improve the village committee's reputation effect; fifth, increase the penalty for false dividends of the service organization.Originality/valueAgricultural production trusteeship is in its initial stage in China. The interest relationships between stakeholders are not yet clear. The paper innovatively applies the evolutionary game method to the research field of the whole process trusteeship of agricultural production. According to conditions in China, based on ensuring the guaranteed income, the paper introduces the dividend income variable and establishes a tripartite game model of village committees, service organization and farmers. The paper provides suggestions for the orderly and healthy development of China's agricultural production trusteeship and provides experience for the operation of other modes of agricultural production trusteeship.

PurposeOne of the most significant threats of COVID-19 in the world is the closure of universities, schools, training courses and even companies and organizations. In such a situation and with the free time that has arisen, this threat of education closure can become a golden opportunity for learning and progress in virtual education. E-learning uses information technology (IT) to distribute knowledge and information for training and education. Also, cloud computing is a technology utilized in the IT domain. It can be employed in performing e-learning. Therefore, the main goal of this study is to assess the impact of cultural characteristics, economic situations, skills and knowledge on the development and success of CELS in the COVID-19 era.Design/methodology/approachCloud-based e-learning system (CELS) provides all e-learning requirements like software and hardware resources to promote conventional e-learning technologies. The CELS stands on several factors of diverse aspects that have been of high significance in CELS success. So, these systems must be checked to analyze their significance rate and successfully carry out their effectiveness. On the other hand, these days, the 2019 coronavirus disease (COVID-19) changes our daily lifestyles. Therefore, the present investigation provides a new model investigating the development and success of CELS in the COVID-19 era. Also, an online questionnaire was used to gather the data. The content validity of the questionnaire was obtained by applying the opinions of ten experts from e-learning specialists. The collected data are analyzed using LISREL and Smart PLS software.FindingsThe results from the path coefficient and the sample t-test have implied that skills and knowledge positively influence CELS in the COVID-19 era. In addition, the relationship between cultural characteristics and CELS in the COVID-19 era has been positive and significant. The relationship between the economic situations and the CELS in the COVID-19 era is positive and significant.Practical implicationsThe proposed model helps managers get a big picture of CELS necessities and more effectively in the COVID-19 era. This research has a unique impact on universities to develop an e-learning platform to facilitate the education process in the COVID-19 era. It provides guidelines for educational institutions to effectively implement the learning management system to facilitate students' education.Originality/valueCELS are getting increasingly essential to offer training courses more efficiently in educational institutions. Although the intersection between cloud computing and e-learning has increasingly grown in both practical and academic contexts, few studies on the impact of cultural characteristics, economic situations, skills and knowledge on the development and success of CELS in the COVID-19 era. This paper explores the ignored but critically important subject of CELS. This paper's main contribution is to present a new and integrated model containing the essential aspects of the development and success of CELS in the COVID-19 era. The proposed framework comprises cultural characteristics, economic situations, skills and knowledge aspects simultaneously, as well as sub-criteria denoting each element.

PurposeThe purpose of this paper is to develop a system dynamics approach based on Susceptible, Exposed, Infected, Recovered (SEIR) model to investigate the coronavirus pandemic and the impact of therapeutic and preventive interventions on epidemic disaster.Design/methodology/approachTo model the behavior of COVID-19 disease, a system dynamics model is developed in this paper based on SEIR model. In the proposed model, the impact of people's behavior, contact reduction, isolation of the sick people as well as public quarantine on the spread of diseases is analyzed. In this model, data collected by the Iran Ministry of Health have been used for modeling and verification of the results.FindingsThe results show that besides the intervening policies, early application of them is also of utmost priority and makes a significant difference in the result of the system. Also, if the number of patients with extreme conditions passes available hospital intensive care capacity, the death rate increases dramatically. Intervening policies play an important role in reducing the rate of infection, death and consequently control of pandemic. Also, results show that if proposed policies do not work before the violation of the hospital capacity, the best policy is to increase the hospital’s capacity by adding appropriate equipment.Research limitations/implicationsThe authors also had some limitations in the study including the lack of access to precise data regarding the epidemic of coronavirus, as well as accurate statistics of death rate and cases in the onset of the virus due to the lack of diagnostic kits in Iran. These parameters are still part of the problem and can negatively influence the effectiveness of intervening policies introduced in this paper.Originality/valueThe contribution of this paper includes the development of SEIR model by adding more policymaking details and considering the constraint of the hospital and public health capacity in the rate of coronavirus infection and death within a system dynamics modeling framework.

PurposeAccurate evaluation of the consequences of new technologies in various industries is of great significance. So, it will be essential to examine the impact of this technology on the banking industry, representing how to create, deliver and gain value in this industry. This study aims to investigate whether blockchain can affect the business intelligence efficiency of banks. This study also aims to examine the impact of security, fraud reduction and privacy of blockchain, equal and anonymous access to the blockchain, decentralization and sustainability of blockchain, accountability and transparency of blockchain, quality, speed and efficiency of blockchain on business intelligence efficiency.Design/methodology/approachTechnological changes are creating new challenges and opportunities for various industries. The inability of organizations to adapt to these changes may even lead to their deletion from the market. Blockchain is one of the most critical technologies in recent years. One of the sectors that will undergo significant changes in blockchain technology is the banking industry. According to the reviewed literature in this study, a comprehensive model has been proposed to examine the impact of security, fraud reduction and privacy of blockchain, equal and anonymous access to the blockchain, the decentralization and sustainability of blockchain, accountability and transparency of blockchain and quality, speed and efficiency of blockchain on business intelligence efficiency. A survey method was used to collect data from banks of the Nanjing city. The partial least square technique was used for data analysis.FindingsThe results showed that the fit of the proposed model was very good. Also, all assumptions except one were confirmed. It means that security, fraud reduction and privacy of blockchain factor have a remarkable and positive impact on all aspects of business intelligence efficiency, namely information technology, employees, competitors and customers. Also, equal and anonymous access to the blockchain factor has a positive and significant effect on all aspects of business intelligence efficiency. The decentralization and sustainability of blockchain factors have an impact on business intelligence efficiency. Also, blockchain's accountability and transparency as a fourth factor have a positive and significant impact on all aspects of business intelligence efficiency. Finally, the last factor (quality, speed and efficiency of blockchain) has a positive and significant effect on information technology, employees and customers' dimensions. But, it does not affect the competitors' dimension, and this hypothesis has not been confirmed.Practical implicationsThis paper offers valuable insight for business intelligence practitioners into how blockchain technology has the potential to disrupt existing business intelligence provisions.Originality/valueThis paper is one of the first studies to examine the impact of blockchain on IT dimension, organizational employees' dimension, customer dimension and competitors' dimension. It lays a firm foundation for future research.

PurposeThis paper proposes an integrated IT2F-FMEA model under a group decision-making setting. In risk assessment models, experts' evaluations are often aggregated beforehand, and necessary computations are performed, which in turn, may cause a loss of information and valuable individual opinions. The proposed integrated IT2F-FMEA model aims to calculate risk priority numbers from the experts' evaluations and then fuse experts' judgments using a novel integrated model.Design/methodology/approachThis paper presents a novel failure mode and effect analysis (FMEA) model by integrating the fuzzy inference system, best-worst method (BWM) and weighted aggregated sum-product assessment (WASPAS) methods under interval type-2 fuzzy (IT2F) environment. The proposed FMEA approach utilizes the Mamdani-type IT2F inference system to calculate risk priority numbers. The individual FMEA results are combined by using integrated IT2F-BWM and IT2F-WASPAS methods.FindingsThe proposed model is implemented in a real-life case study in the furniture industry. According to the case study, fifteen failure modes are considered, and the proposed integrated method is used to prioritize the failure modes.Originality/valueMamdani-type singleton IT2F inference model is employed in the FMEA. Additionally, the proposed model allows experts to construct their membership functions and fuzzy rules to capitalize on the experience and knowledge of the experts. The proposed group FMEA model aggregates experts' judgments by using IT2F-BWM and IT2F-WASPAS methods. The proposed model is implemented in a real-life case study in the furniture company.

PurposeThis study proposes a dynamic performance evaluation model to support the material availability of the public institution under performance- based logistics (PBL) and to select the most appropriate service provider.Design/methodology/approachThe model consists of four stages. In the first stage, a criteria set to evaluate alternatives is created. In the second stage, the DEA-MTFP index method is applied for performance evaluation of the alternatives by using crisp data. In the third stage, IFS theory is utilized for aggregating decision-maker judgments on alternatives, and in the last stage, the results of both methods are turned into single value, and it is selected as the most suitable alternative.FindingsIt is verified that the proposed approach can be implemented to the real-life dynamic multi-criteria decision-making (MCDM) problem that have crisp and fuzzy data under the PBL strategy.Practical implicationsThis paper offers an integrated approach for performance analysis of service providers in a dynamic MCDM problem in which crisp and fuzzy data are used together. To illustrate applicability and validity of the proposed model, it is applied to a real-life problem.Originality/valueThis paper utilizes the DEA-MTFP index method and IFS theory in an integrated way.

Purpose This paper aims to propose a new assessment system module for handling the comprehensive answers written through the answer interface. Design/methodology/approach The working principle is under three major phases: Preliminary semantic processing: In the pre-processing work, the keywords are extracted for each answer given by the course instructor. In fact, this answer is actually considered as the key to evaluating the answers written by the e-learners. Keyword and semantic processing of e-learners for hierarchical clustering-based ontology construction: For each answer given by each student, the keywords and the semantic information are extracted and clustered (hierarchical clustering) using a new improved rider optimization algorithm known as Rider with Randomized Overtaker Update (RR-OU). Ontology matching evaluation: Once the ontology structures are completed, a new alignment procedure is used to find out the similarity between two different documents. Moreover, the objects defined in this work focuses on “how exactly the matching process is done for evaluating the document.” Finally, the e-learners are classified based on their grades. Findings On observing the outcomes, the proposed model shows less relative mean squared error measure when weights were (0.5, 0, 0.5), and it was 71.78% and 16.92% better than the error values attained for (0, 0.5, 0.5) and (0.5, 0.5, 0). On examining the outcomes, the values of error attained for (1, 0, 0) were found to be lower than the values when weights were (0, 0, 1) and (0, 1, 0). Here, the mean absolute error (MAE) measure for weight (1, 0, 0) was 33.99% and 51.52% better than the MAE value for weights (0, 0, 1) and (0, 1, 0). On analyzing the overall error analysis, the mean absolute percentage error of the implemented RR-OU model was 3.74% and 56.53% better than k-means and collaborative filtering + Onto + sequential pattern mining models, respectively. Originality/value This paper adopts the latest optimization algorithm called RR-OU for proposing a new assessment system module for handling the comprehensive answers written through the answer interface. To the best of the authors’ knowledge, this is the first work that uses RR-OU-based optimization for developing a new ontology alignment-based online assessment of e-learners.

PurposeThis 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.Design/methodology/approachCloud 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.FindingsThe 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.Research limitations/implicationsAlthough 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.Practical implicationsThe 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.Originality/valueAlthough 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.

PurposeTo be more effective, artificial intelligence (AI) requires a broad overall view of the design and transformation of enterprise architecture and capabilities. Maturity models (MMs) are the recognized tools to identify strengths and weaknesses of certain domains of an organization. They consist of multiple, archetypal levels of maturity of a certain domain and can be used for organizational assessment and development. In the case of AI, quite a few numbers of MMs have been proposed. Generally, the links between AI technology, AI usage and organizational performance stay unclear. To address these gaps, this paper aims to introduce the complete details of the AI maturity model (AIMM) for AI-driven platform companies. The associated AI-Driven Platform Enterprise Maturity framework proposed here can help to achieve most of the AI-driven platform companies' objectives.Design/methodology/approachQualitative research is performed in two stages. In the first stage, a review of the existing literature is performed to identify the types, barriers, drivers, challenges and opportunities of MMs in AI, Advanced Analytics and Big Data domains. In the second stage, a research framework is proposed to align company value chain with AI technologies and levels of the platform enterprise maturity.FindingsThe paper proposes a new five level AI-Driven Platform Enterprise Maturity framework by constructing a formal organizational value chain taxonomy model that explains a vast group of MM phenomena related with the AI-Driven Platform Enterprises. In addition, this study proposes a clear and precise description and structuring of the information in the multidimensional Platform, AI, Advanced Analytics and Big Data domains. The AI-Driven Platform Enterprise Maturity framework assists in identification, creation, assessment and disclosure research of AI-driven platform business organizations.Research limitations/implicationsThis research is focused on the basic dimensions of AI value chain. The full reference model of AI consists of much more concepts. In the last few years, AI has achieved a notable drive that, if connected appropriately, may deliver the best of expectations over many application sectors across the field. For this to occur shortly in machine learning, especially in deep neural networks, the entire community stands in front of the barrier of explainability. Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is widely acknowledged as a crucial feature for the practical deployment of AI models in industry. Our prospects lead toward the concept of a methodology for the large-scale implementation of AI methods in platform organizations with fairness, model explainability and accountability at its core.Practical implicationsAI-driven platform enterprise maturity framework can be used for better communicate to clients the value of AI capabilities through the lens of changing human-machine interactions and in the context of legal, ethical and societal norms.Social implicationsThe authors discuss AI in the enterprise platform stack including talent platform, human capital management and recruiting.Originality/valueThe AI value chain and AI-Driven Platform Enterprise Maturity framework are original and represent an effective tools for assessing AI-driven platform enterprises.

PurposeHealthcare employs informatics to offer its services through information technology where the social network can aid virtual medical learning. Since the usage of the internet and other electronic tools for medical services delivery is at the initial stage, it is essential to examine the factors that condition patients and medical elements in a virtual environment can develop relationship models on the health services. So, the authors have systematically reviewed virtual medical learning and offered some suggestions for the upcoming works. The authors have also discovered gaps in the state-of-the-art papers and provided solutions for them.Design/methodology/approachNumerous novel advancements have changed the old exercise of therapeutic and analytic learning. Virtual spaces have quickly turned into a section of the learning technology vision. Given the importance of its achievements and endless low-cost expansion of the educational system, virtual education has been considered as one of the issues raised by the information communities. Medicine and health are some of the most important fields in virtual technologies. Hence, in this paper, we have used a systematic literature review to deeply examine virtual medical learning. After establishing exclusion and inclusion criteria, an independent systematic search in Google Scholar, ACM, Scopus, Eric, Science Direct, Springer link, Emerald, Global ProQuest and IEEE for relevant studies have been performed, and 21 papers have been analyzed. Detailed data have been mined out of the papers.FindingsThe authors have found that virtual medical learning improves and expands the knowledge core and meaningfully affects the exercise. Virtual learning (VL) has been used in many therapeutic zones, like therapeutic learning, surgery, diagnosing, combining and regularizing processes. It has presented a fundamental access point and a referral mechanism for all of a course's component communities. It can also simplify communicative education, allowing learners to get abilities before applying them in a real-world situation. Also, the communicative characteristics of different VL programs can somehow be like direct teaching.Research limitations/implicationsSome excellent work may be removed owing to applying the filters to select the primary papers. Surveying all the documents on the topic of virtual medical learning is impossible, too. Nevertheless, the authors have tried to present a perfect survey of virtual medical learning. The results will be helpful for scholars to propose better virtual medical learning techniques.Practical implicationsE-learning has become an indispensable additional learning tool in medical education. The introduction of new learning technologies, the exponential growth of Internet usage and the advent of the World Wide Web can change the face of higher education. The results will be helpful for scholars for the upcoming works. The application of a literature review of partial least squares theory was useful for offering comprehensive literary coverage and completing the knowledge development analysis. The authors have backed scholars and experts for better understanding the development of virtual medical learning systems via presenting comparative data and scrutinizing the present advances.Originality/valueThe paper enhances intellectual knowledge by improving the conception of virtual medical learning. It informs the development, use of virtual medical learning and the upcoming works. The lack of comprehensive papers in this field has increased the importance of this paper. The present paper can handle the pace of publications.

PurposeThe main goal of this paper is to study the cloud service discovery mechanisms. In this paper, the discovery mechanisms are ranked in three major classes: centralized, decentralized, and hybrid. Moreover, in this classification, the peer-to-peer (P2P) and agent-based mechanisms are considered the parts of the decentralized mechanism. This paper investigates the main improvements in these three main categories and outlines new challenges. Moreover, the other goals are analyzing the current challenges in a range of problem areas related to cloud discovery mechanisms and summarizing the discussed service discovery techniques.Design/methodology/approachSystematic literature review (SLR) is utilized to detect, evaluate and combine findings from related investigations. The SLR consists of two key stages in this paper: question formalization and article selection processes. The latter includes three steps: automated search, article selection and analysis of publication. These investigations solved one or more service discovery research issues and performed a general study of an experimental examination on cloud service discovery challenges.FindingsIn this paper, a parametric comparison of the discovery methods is suggested. It also demonstrates future directions and research opportunities for cloud service discovery. This survey will help researchers understand the advances made in cloud service discovery directly. Furthermore, the performed evaluations have shown that some criteria such as security, robustness and reliability attained low attention in the previous studies. The results also showed that the number of cloud service discovery–related articles rose significantly in 2020.Research limitations/implicationsThis research aimed to be comprehensive, but there were some constraints. The limitations that the authors have faced in this article are divided into three parts. Articles in which service discovery was not the primary purpose and their title did not include the related terms to cloud service discovery were also removed. Also, non-English articles and conference papers have not been reviewed. Besides, the local articles have not been considered.Practical implicationsOne of the most critical cloud computing topics is finding appropriate services depending on consumer demand in real-world scenarios. Effective discovery, finding and selection of relevant services are necessary to gain the best efficiency. Practitioners can thus readily understand various perspectives relevant to cloud service discovery mechanisms. This paper's findings will also benefit academicians and provide insights into future study areas in this field. Besides, the drawbacks and benefits of the analyzed mechanisms have been analyzed, which causes the development of more efficient and practical mechanisms for service discovery in cloud environments in the future.Originality/valueThis survey will assist academics and practical professionals directly in their understanding of developments in service discovery mechanisms. It is a unique paper investigating the current and important cloud discovery methods based on a logical categorization to the best of the authors’ knowledge.

PurposeCustomer churn prediction is one of the most well-known approaches to manage and improve customer retention. Machine learning techniques, especially classification algorithms, are very popular tools to predict the churners. In this paper, three ensemble classifiers are proposed based on bagging and boosting for customer churn prediction.Design/methodology/approachIn this paper, three ensemble classifiers are proposed based on bagging and boosting for customer churn prediction. The first classifier, which is called boosted bagging, uses boosting for each bagging sample. In this approach, before concluding the final results in a bagging algorithm, the authors try to improve the prediction by applying a boosting algorithm for each bootstrap sample. The second proposed ensemble classifier, which is called bagged bagging, combines bagging with itself. In the other words, the authors apply bagging for each sample of bagging algorithm. Finally, the third approach uses bagging of neural network with learning based on a genetic algorithm.FindingsTo examine the performance of all proposed ensemble classifiers, they are applied to two datasets. Numerical simulations illustrate that the proposed hybrid approaches outperform the simple bagging and boosting algorithms as well as base classifiers. Especially, bagged bagging provides high accuracy and precision results.Originality/valueIn this paper, three novel ensemble classifiers are proposed based on bagging and boosting for customer churn prediction. Not only the proposed approaches can be applied for customer churn prediction but also can be used for any other binary classification algorithms.

Purpose Based on the analysis of the dissipative structure of the retail service supply chain (RSSC), this paper divides the system into two internal and external dissipative mechanisms, including the internal performance dissipation mechanism and the perceived quality dissipation mechanism outside the system. Based on the prediction of RSSC performance, this paper aims to discuss the application of Hidden Markov Model (HMM) in this field and puts forward a set of complete process of forecasting the service supply chain (SSC) performance based on HMM model. Design/methodology/approach Based on the theory of dissipative structure, this paper selects the RSSC as the research object, analyzes the system characteristics of the dissipation structure of RSSC from three aspects, such as system opening type, distance from equilibrium state and nonlinear order and describes the quality fluctuation process of RSSC as a Hidden Markov process. Taking the RSSC of J Company as an example, this paper makes use of the observed state value of customer perceived service quality from 1997 to 2016, predicts the performance status of the enterprise's RSSC. Findings The research results show that: RSSC is a dissipative structure system, and its performance is the internal entropy flow of the system, and the customer perceived service quality is external, their interaction determines the dynamic evolution of the system dissipation structure, and the Markov property between supply chain performance and perceived service quality. There is a Markov property between supply chain performance and perceived service quality. Using the perceived service quality observation state data of the external consumers of the system can effectively predict the implicit state of RSSC performance. Based on this prediction result, the strategy adjustment and optimization of the action mechanism of internal and external entropy flow in the dissipative structure system can be carried out to promote the sustainable development of the RSSC. Originality/value This paper thinks that RSSC is a dissipative structure system and the SSC performance and customer perceived service quality are the internal and external entropy flow of the system, which determines the dynamic evolution of the system dissipation structure. There is a Markov property between supply chain performance and perceived service quality. The hidden state of SSC performance can be predicted effectively by using a hidden Markov model and observing state data of perceived service quality from consumers outside the system.

Purpose Accurate foreign tourist arrivals forecasting can help public and private sectors to formulate scientific tourism planning and improve the allocation efficiency of tourism resources. This paper aims to address the problem of low prediction accuracy of Chinese inbound tourism demand caused by the lack of valid historical data. Design/methodology/approach A novel hybrid Chinese inbound tourism demand forecasting model combining fractional non-homogenous discrete grey model and firefly algorithm is constructed. In the proposed model, all adjustable parameters of the fractional non-homogenous discrete grey model are optimized simultaneously by the firefly algorithm. Findings The data sets of annual foreign tourist arrivals to China are used to verify the validity of the proposed model. Experimental results show that the proposed method is effective and can be used as a useful predictor for the prediction of Chinese inbound tourism demand. Originality/value The method proposed in this paper is effective and can be used as a feasible approach for forecasting the development trend of Chinese inbound tourism.

Purpose The present investigation goals to empirically test the role of expert cloud on team performance and employee creativity. Here, the expert cloud comprises cloud application, cloud management, cloud infrastructure and cloud resources. The present study aims to identify important and key criteria and examine the relationships among them. In other words, the purpose of this study to find out the impact and relationship between cloud application, cloud management, cloud infrastructure and cloud resources and team performance and employee creativity. Design/methodology/approach Today, human societies’ rapid growth and the environmental changes that surround us every day are clearly visible. They highly affect our activities. In today’s highly complex organizations, people alone cannot handle all the issues that have arisen. As organizational managers are faced with diverse cultures in the governance of organizations, the need to use work teams with different abilities and specializations to achieve the goals of organizations leads managers to use teamwork and focus on employee creativity. On the other hand, the expert cloud makes it possible for human societies like universities, firms, industries, institutes, businesses and colleges to pool and share their human resources’ skills, knowledge and experiences to meet the competitive era’s demands. Therefore, the current investigation examines the impact of expert cloud on team performance and employee creativity. The research information is collected using an online questionnaire. The data collected is analyzed using AMOS and SMART PLS software. Findings All of the formulated hypotheses are supported. The results have shown that cloud application, cloud management, cloud infrastructure and cloud resources positively and significantly affect team performance and employee creativity. Practical implications Managers must be conscious of the vital role that the professional cloud plays in team performance and the innovation of workers. This paper would also make executives more conscious of the powerful tools in the field of cloud computing. Firms can use the outcomes of this paper investigation to improve team performance and employee creativity. Originality/value It is considered one of the initial efforts to demonstrate the impacts of expert cloud on team performance and employee creativity. This study’s value relies on that practitioners and academics may have supporting evidence on the role played by novel technology such as an expert cloud.

Purpose Carbon emission is a significant issue for the current business market and global warming. Nowadays, most countries have focused to reduce the environmental impact of business with durable financial benefits. The purpose of this study is to optimize the entire cost functions with carbon emission and to find the sustainable optimal ordering quantity for retailers. Design/methodology/approach This paper illustrates a sustainable inventory model having a set of two non-instantaneous substitutable deteriorating items under joint replenishment with carbon emission. In this model demand and deterioration rate are considered as deterministic, constant and triangular fuzzy numbers. The objective is to find the optimal ordering quantity for retailers and to minimize the total cost function per unit time with carbon emission. The model is then solved with the help of Maple software. Findings This paper presents a solution method and also develop an algorithm to determine the order quantities which optimize the total cost function. A numerical experiment illustrates the improvement in optimal total cost of the inventory model with substitution over without substitution. The graphical results show the convexity of the cost function. Finally, sensitivity analysis is given to get the impact of parameters and validity of the model. Originality/value This study considers a set of two non-instantaneous substitutable deteriorating items under joint replenishment with carbon emission. From the literature review, in the authors’ knowledge no researcher has undergone this kind of study.

Purpose This study aims to propose an integrated model based on the DeLone and McLean Information System Success Model (DMISS) to examine whether quality factors (system, service and information) can affect user satisfaction and performance of cloud-based marketing systems. Design/methodology/approach Recently, technologies change quickly, innovation becomes a vital base of productivity and sustainable growth of businesses is widely accepted. Cyber-physical system technologies help industries change production and marketing cycles according to customers’ needs in real-time. In addition, processing information through cloud service helps companies meet customer needs. The advantages of cloud technology also make it easier for companies to quickly collect the latest data from various sources, making it more effective in decision-making. This research recommends cloud-based marketing to help companies maximize their revenue by providing useful information and better quality for business development. The data were gathered from China automotive companies’ customers. A total of 220 questionnaires were distributed, and 165 (82.5%) usable questionnaires were analyzed using structural equation modeling. Findings This study verified that costumers’ perceived information quality, system quality and service quality positively caused the user satisfaction in the cloud-based marketing system. Practical implications This paper presents beneficial advice for improving cloud-based marketing systems. Besides, the topic is relevant to cloud-based marketing systems’ success. A better understanding of the impact of intention to use and user satisfaction on cloud-based marketing systems could significantly enhance companies’ success. This paper’s theoretical and practical contributions are expressed to guide organizations and policymakers in increasing cloud-based marketing systems acceptance. Originality/value This study empirically tests the relationship of quality factors and performance outcome of cloud-based marketing system through a model based on DeLone and McLean theory. This study bridges the research gap by identifying the factors that drive the adoption of cloud-based services in marketing and the impact of user satisfaction and intention to use on the cloud-based marketing system performance in the case of china companies.

Purpose Up to date development in sentiment analysis has resulted in a symbolic growth in the volume of study, especially on more subjective text types, namely, product or movie reviews. The key difference between these texts with news articles is that their target is defined and unique across the text. Hence, the reviews on newspaper articles can deal with three subtasks: correctly spotting the target, splitting the good and bad content from the reviews on the concerned target and evaluating different opinions provided in a detailed manner. On defining these tasks, this paper aims to implement a new sentiment analysis model for article reviews from the newspaper. Design/methodology/approach Here, tweets from various newspaper articles are taken and the sentiment analysis process is done with pre-processing, semantic word extraction, feature extraction and classification. Initially, the pre-processing phase is performed, in which different steps such as stop word removal, stemming, blank space removal are carried out and it results in producing the keywords that speak about positive, negative or neutral. Further, semantic words (similar) are extracted from the available dictionary by matching the keywords. Next, the feature extraction is done for the extracted keywords and semantic words using holoentropy to attain information statistics, which results in the attainment of maximum related information. Here, two categories of holoentropy features are extracted: joint holoentropy and cross holoentropy. These extracted features of entire keywords are finally subjected to a hybrid classifier, which merges the beneficial concepts of neural network (NN), and deep belief network (DBN). For improving the performance of sentiment classification, modification is done by inducing the idea of a modified rider optimization algorithm (ROA), so-called new steering updated ROA (NSU-ROA) into NN and DBN for weight update. Hence, the average of both improved classifiers will provide the classified sentiment as positive, negative or neutral from the reviews of newspaper articles effectively. Findings Three data sets were considered for experimentation. The results have shown that the developed NSU-ROA + DBN + NN attained high accuracy, which was 2.6% superior to particle swarm optimization, 3% superior to FireFly, 3.8% superior to grey wolf optimization, 5.5% superior to whale optimization algorithm and 3.2% superior to ROA-based DBN + NN from data set 1. The classification analysis has shown that the accuracy of the proposed NSU − DBN + NN was 3.4% enhanced than DBN + NN, 25% enhanced than DBN and 28.5% enhanced than NN and 32.3% enhanced than support vector machine from data set 2. Thus, the effective performance of the proposed NSU − ROA + DBN + NN on sentiment analysis of newspaper articles has been proved. Originality/value This paper adopts the latest optimization algorithm called the NSU-ROA to effectively recognize the sentiments of the newspapers with NN and DBN. This is the first work that uses NSU-ROA-based optimization for accurate identification of sentiments from newspaper articles.