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The purpose of this paper is to investigate the behavioral intentions of business-to-business (B2B) sales managers to use mobile customer relationship management (CRM) systems in the course of their day-to-day activities.

An extended Technology Acceptance Model (TAM) of mobile CRM system adoption is developed and tested with data from 105 international sales managers representing five B2B companies.

The study extends the TAM framework with three additional constructs derived from mobile technology and sales force automation literature, namely personal innovativeness in the domain of IT, perceived risk, and perceived reachability. The model demonstrates that personal innovativeness and perceived reachability have significant effects on the TAM framework.

The relatively small sample size limits the generalization of the results.

Sales managers’ intention to adopt mobile CRM can be explained by the extended TAM framework. Understanding the key factors that influence intention to adopt a mobile CRM system will aid companies in implementing it among their sales force. Companies willing to foster adoption of a mobile CRM system among the sales force could focus on communicating the usefulness of using the system and benefits gained from enhanced reachability. Recruiting sales people with strong personal innovativeness is beneficial.

This study responds the calls for studies on mobile platforms and on the use of mobile B2B applications in sales force management. It is among the first attempts to incorporate variables derived from mobile technology acceptance literature among the sales force into the TAM framework, to better explain acceptance of mobile CRM systems.

The study explores the potential barriers to data science (DS) implementation in organizations and identifies the key barriers. The identified barriers were explored for their interconnectedness and characteristics. This study aims to help organizations formulate apt DS strategies by providing a close-to-reality DS implementation framework of barriers, in conjunction with extant literature and practitioners' viewpoints.

The authors synthesized 100 distinct barriers through systematic literature review (SLR) under the individual, organizational and governmental taxonomies. In discussions with 48 industry experts through semi-structured interviews, 14 key barriers were identified. The selected barriers were explored for their pair-wise relationships using interpretive structural modeling (ISM) and fuzzy Matriced’ Impacts Croise's Multiplication Appliquée a UN Classement (MICMAC) analyses in formulating the hierarchical framework.

The lack of awareness and data-related challenges are identified as the most prominent barriers, followed by non-alignment with organizational strategy, lack of competency with vendors and premature governmental arrangements, and classified as independent variables. The non-commitment of top-management team (TMT), significant investment costs, lack of swiftness in change management and a low tolerance for complexity and initial failures are recognized as the linkage variables. Employee reluctance, mid-level managerial resistance, a dearth of adequate skills and knowledge and working in silos depend on the rest of the identified barriers. The perceived threat to society is classified as the autonomous variable.

The study augments theoretical understanding from the literature with the practical viewpoints of industry experts in enhancing the knowledge of the DS ecosystem. The research offers organizations a generic framework to combat hindrances to DS initiatives strategically.

The purpose of this paper is to identify and develop a hierarchical model for the barriers affecting the growth of mobile commerce (M-commerce). Based on the model developed, the authors’ objective is to identify those variables that are of strategic nature and are the root cause of the issue.

Variables considered as barriers are identified, and utilizing the interpretive structural model approach, a relationship model is developed. Further, the impact matrix cross-reference multiplication applied to a classification approach is used to analyze the effect and dependence among these factors.

The research in the area of M-commerce in the Arab world and related to the strategic aspect is limited in the extant literature. The present study tries to fill this gap by investigating the variables that inhibit the growth of M-commerce in Qatar. The research shows that there exists a group of barriers having a high driving power and low dependence requiring maximum attention and of strategic importance, while another group consists of those variables that have high dependence and are the resultant actions.

There is a growing concern that although the numbers of mobile subscribers are increasing at a very fast pace in Qatar, actual M-commerce activities in the country remain low. The findings of this study can be used to understand the differences between the independent and dependent variables and their mutual relationships. The study would also help the policy makers to develop suitable strategies to facilitate growth of M-commerce in the country.

This research was the first attempt to investigate the relationships among the variables inhibiting the growth of M-commerce in a fast-growing economy in a Gulf Cooperation Council (GCC) region. Given that there is limited research on M-commerce in the GCC context, the study can be viewed as an investigation that provides a good understanding of the variables and their interrelationships affecting M-commerce proliferation.

The purpose of this study is to examine the determinants impacting consumer behaviour in organic food consumption in Bangladesh. This study aims to identify the key factors facilitating organic food consumption and establish a framework by analysing their contextual relationships.

The study used interpretive structural modelling (ISM), relying on expert perspectives from experienced academicians and marketing professionals. A Matrice d'Impacts Croisés Multiplication Appliqués à un Classement (MICMAC) analysis was performed to assess the driving forces and interdependencies among these determinants.

The MICMAC analysis grouped determinants influencing organic food purchases into four categories. The dependent factors, like attitude and food safety, showed moderate driving forces and high dependence. Linkage determinants, such as environmental concern and price, exerted considerable influence with moderate dependence. Independent variables, especially knowledge about organic food, had a strong impact with relatively low dependence.

This study’s insights offer valuable guidance for managers in the organic food industry, providing strategies to address consumer behaviour. Prioritising education on environmental benefits, transparent pricing, collaborating on policies, ensuring food safety and understanding determinants impacting purchase intent can aid in designing effective marketing strategies and product offerings aligned with consumer needs, ultimately promoting sustainability.

To the best of the authors’ knowledge, this study is the first to investigate the interconnections and relative significance of determinants influencing organic food purchases, using the ISM approach and MICMAC analysis. It delves into the previously unexplored territory of understanding the relationships and hierarchical significance of these determinants in shaping consumer behaviour towards organic food purchases.

With the surge in public procurement, especially in developing countries, ensuring fair competition in procurement has assumed paramount importance. Academic endeavors in the domain of competition issues have often lacked the views of field-level functionaries. This study aims to involve a large number of expert practitioners in India to identify the significant contemporary competition risks in public procurement from the procurer and supplier sides and develop a model depicting the hierarchy of competition-restrictive actions (CRAs) in procurement based on their mutual interactions.

The significant CRAs along the procurement cycle are identified through literature survey, interactive workshops and expert interviews. A questionnaire survey covering 143 respondents from 12 public organizations is used to evaluate their impact. Considering the complex causal interactions involved, interpretive structural modeling followed by MICMAC (Iimpact matrix cross-reference multiplication applied to a classification analysis is used to develop a hierarchical model of competition risks in procurement.

Tailor-made contracts, splitting of a project below competition thresholds, restrictive selection criteria and awarding the contract on nomination emerge as CRAs with the highest driving power. Horizontal collusion among vendors strongly depends on practices followed in the procuring organization.

The survey data and the experts’ opinions emanate from practitioners in India, which is a limitation. However, with necessary contextual calibrations, the study is of high functional utility to policymakers and practitioners.

The research facilitates a comprehensive understanding to procurement managers/policymakers of the CRAs along the procurement cycle and their interdependencies. It offers valuable insights for improving competition, which is foundational for optimal procurement outcomes.

The study enriches the public procurement domain knowledge by identifying and assessing the significant contemporary CRAs, examining their mutual interactions and developing an interpretive structural model. Although contributing to the body of knowledge, the study is unique in being grounded in field realities.

The purpose of this paper is to identify the factors influencing investment decisions in mobile services for profitablity and to become a global leader in mobile services sector.

A two-stage methodology is followed. In the first stage, factors are identified from literature, and are validated with telecommunication domain experts using the t-test. In the second stage, interpretive structural modeling (ISM) is used to understand the complex interrelationships among various factors. Further, MICMAC analysis is performed to analyze the indirect relationships and their effect on different factors by stabilizing the rank based on driving and dependence power. Based on MICMAC analysis, four clusters are identified to aid the policy- and decision-makers.

The major contribution of this research is imposing directions and dominance of various factors to make informed decision-making for investment in mobile services to meet the upcoming demand for mobile services in Indian telecommunication sector.

The applicability of these research findings is limited to emerging telecommunication market.

This paper forms the basis for identifying various factors that act as the driving force for the Indian telecommunication operators to pay special attention toward mobile services, with telecommunication data analytics and developing context-aware services. This paper will aid policy-makers in the government, managers in telecommunication companies and other stakeholders such as content providers, channel partners and application developers to take a lead role in developing appropriate mobile services to meet local needs of Indian users. It will help in developing strategies to collaborate and motivate other stakeholders, including device manufacturers to understand and work collaboratively to become world leader in mobile services.

This paper provides a framework for understanding the various factors that encourage telecommunication companies to establish and invest in mobile services and setup a separate vertical in their organization with a focus on mobile services to meet the future demands of Indian market. Appropriate utilization of telecommunication data analytics, personalization of services, customization in local languages and support for convergent services would encourage adoption of mobile services.

Due to the growing dominance of the millennials in the secondhand clothing (SHC) market, it is crucial to understand the dynamics of their SHC buying behavior. Despite such significance, it has yet to be explored in the current literature. To address such a gap, this paper aims to explore the antecedents of the SHC buying behavior of millennials.

A purposive survey is conducted to establish relationships between the antecedents. As such, the interrelationships of the antecedents are modeled using the interpretative structural modeling (ISM) approach.

Results reveal that SHC antecedents exhibit several characteristics depending upon their characterization of being driving, dependence, linkage and autonomous variables.

This work pioneers the identification of SHC buying behavior antecedents specifically for the millennial market, as well as in the provision of a holistic analysis of the complex contextual relationships of these antecedents. The findings of this work provide insights that are crucial to the extant literature in developing theoretical frameworks and paradigms that help in understanding the dynamics of the SHC buying behavior. Moreover, such results are beneficial to marketing managers and practitioners in innovating their strategies to capture the millennial market better.

The premature closure of microbusinesses has become a prevalent issue that demands to be seriously addressed due to the major economic contribution it provides particularly to developing countries. The purpose of this paper is to establish and further analyze a set of critical failure factors (CFFs) that aid in the systematic decision-making and strategic management of stakeholders.

A comprehensive literature review is conducted to gather CFFs and an interpretative structural modeling with Matriced’ Impacts Croise’s Multiplication Appliquée a UN Classement (ISM-MICMAC) analysis is applied to investigate the structural relationships among CFFs by extracting its inherent characteristics according to its driving power and dependence power. In order to illustrate the methodology, a case study is conducted in Cebu, the Philippines.

It is interesting to note that inadequate government programs information is deemed the most crucial for stakeholders to tackle as its impact on the premature closure of microbusinesses is highly significant. Drawing from this key result, directions for potential strategies for getting evidence-based research into policy and practice in the micro-business sector in the Philippines, and other developing countries, are provided.

Lastly, the contribution of this work is two-fold and is aimed at policymakers and managers. For one, this study is the first to establish a set of CFFs specifically aimed at the level of microbusinesses, a seriously under-researched business sector, which can aid and influence domestic policymakers. For another, a framework that will facilitate business managers in carrying out organization’s strategy development process have been provided.

The purpose of this paper is to identify and analyse the interactions among various enablers which are critical to the success of big data initiatives in operations and supply chain management (OSCM).

Fourteen enablers of big data in OSCM have been selected from literature and consequent deliberations with experts from industry. Three different multi criteria decision-making (MCDM) techniques, namely, interpretive structural modeling (ISM), fuzzy total interpretive structural modeling (fuzzy-TISM) and decision-making trial and evaluation laboratory (DEMATEL) have been used to identify driving enablers. Further, common enablers from each technique, their hierarchies and inter-relationships have been established.

The enabler modelings using ISM, Fuzzy-TISM and DEMATEL shows that the top management commitment, financial support for big data initiatives, big data/data science skills, organizational structure and change management program are the most influential/driving enablers. Across all three different techniques, these five different enablers has been identified as the most promising ones to implement big data in OSCM. On the other hand, interpretability of analysis, big data quality management, data capture and storage and data security and privacy have been commonly identified across all three different modeling techniques as the most dependent big data enablers for OSCM.

The MCDM models of big data enablers have been formulated based on the inputs from few domain experts and may not reflect the opinion of whole practitioners community.

The findings enable the decision makers to appropriately choose the desired and drop undesired enablers in implementing the big data initiatives to improve the performance of OSCM. The most common driving big data enablers can be given high priority over others and can significantly enhance the performance of OSCM.

MCDM-based hierarchical models and causal diagram for big data enablers depicting contextual inter-relationships has been proposed which is a new effort for implementation of big data in OSCM.

The purpose of this study is to identify and analyze the barriers associated with the adoption of Industry 4.0 technologies in agricultural supply chains.

The study initially identified thirteen barriers by conducting a literature review and semi-structured interviews with key stakeholders. Subsequently, these barriers were validated and modeled using an integrated Fuzzy Delphi-ISM approach. Finally, MICMAC analysis was employed to categorize the barriers into distinct clusters.

The results provide considerable insights into the hierarchical structure and complex interrelationships between the barriers as well the driving and dependence power of barriers. Lack of information about technologies and lack of compatibility with traditional methods emerged as the two main barriers which directly and indirectly influence the other ones.

The robust hybrid Fuzzy Delphi and ISM techniques used in this study can serve as a useful model and benchmark for similar studies probing the barriers to Industry 4.0 adoption. From a theoretical standpoint, this study expands the scope of institutional theory in explaining Industry 4.0 adoption barriers.

The study is timely for the post-COVID-19 recovery and growth of the agricultural sector. The findings are helpful for policymakers and agriculture supply chain stakeholders in devising new strategies and policy interventions to prioritize and address Industry 4.0 adoption barriers.

It is the first comprehensive, multi-country and multi-method empirical study to comprehensively identify and model barriers to Industry 4.0 adoption in agricultural supply chains in emerging economies.