Search results

E-commerce companies often manipulate customer decisions through dark patterns to meet their interests. Therefore, this study aims to identify, model and rank the enablers behind dark patterns usage in e-commerce companies.

Dark pattern enablers were identified from existing literature and validated by industry experts. Total interpretive structural modeling (TISM) was used to model the enablers. In addition, “matriced impacts croisés multiplication appliquée á un classement” (MICMAC) analysis categorized and ranked the enablers into four groups.

Partial human command over cognitive biases, fighting market competition and partial human command over emotional triggers were ranked as the most influential enablers of dark patterns in e-commerce companies. At the same time, meeting long-term economic goals was identified as the most challenging enabler of dark patterns, which has the lowest dependency and impact over the other enablers.

TISM results are reliant on the opinion of industry experts. Therefore, alternative statistical approaches could be used for validation.

The insights of this study could be used by business managers to eliminate dark patterns from their platforms and meet the motivations of the enablers of dark patterns with alternate strategies. Furthermore, this research would aid legal agencies and online communities in developing methods to combat dark patterns.

Although a few studies have developed taxonomies and classified dark patterns, to the best of the authors’ knowledge, no study has identified the enablers behind the use of dark patterns by e-commerce organizations. The study further models the enablers and explains the mutual relationships.

Some enablers support the information technology (IT) enablement of supply chains. The aim of this paper is to understand mutual influences of these enablers and also to identify those enablers which support other enablers (“driving enablers”) and those which are most influenced by others (“dependent enablers”). A questionnaire‐based survey has been conducted to rank these enablers. The results of the survey and the interpretive structural modeling (ISM) methodology have been used to evolve mutual relationships among these enablers. It is observed that two enablers, namely “awareness about use of IT in supply chains” and “trust among supply chain partners”, have high driving power and therefore deserve serious attention. The study concludes with a discussion and managerial implications.

In the current era, the focus on circular economy (CE) has gained substantial momentum among the research communities across the manufacturing world. It has become the need of the hour to act fast due to the alarming issues of unsustainability such as climate change, global warming, waste generation, environmental pollution, resource scarceness and ecological degradations. This research aims to investigate and model the CE enablers in the product development process related to the moderating effect of net zero.

The significant CE enablers are identified through literature review and expert brainstorming. The Pythagorean fuzzy decision-making trial and evaluation laboratory (PF-DEMATEL) technique has been used to investigate and evaluate the significant CE enablers in product development process. PF-DEMATEL determines the inter-relationship and casual dependency among the selected CE enablers. Indian automobile small and medium enterprises (SMEs) have been considered as a case organization to demonstrate the effectiveness of the proposed method.

The findings reveal that “Top management support and clear vision towards CE adoption” is the most important enabler and “Artificial intelligence in product value chain” is recognized as the least vital enabler. This research aids the managers, decision-makers, policy planners and workforce to develop and formulating efficient blueprints for the effective adoption of CE in Indian SMEs.

This is the first kind of research that explores CE enablers in product development process for Indian SMEs.

Many European businesses are small and medium enterprises (SMEs), contributing significantly to the well-being of local economies and regions. Even so, SMEs face many challenges in fostering innovation and improving performance. Furthermore, the raw material consumption is increasing globally, necessitating the development of strategies that will reduce the number of raw materials extracted and imported while improving the sustainability of small and medium-sized businesses. Consequently, promoting circular economy (CE) strategies, such as industrial symbiosis (IS) partnerships, whereby waste products from other industries serve as a source of raw materials for companies, is critical. Identifying and analysing enablers or drivers that support IS collaborations among SMEs is necessary to achieve this goal. In this regard, the purpose of this study will explore the enablers of IS among SMEs considering sustainability dimensions (environmental, social and economic). As facing a decision-making (DM) problem, the multiple attribute decision-making (MADM) approach was applied in a hesitant fuzzy (HF) environment in this research to answer the research questions. In this regard, in phase 1, IS enablers were identified and extracted using a literature review and experts’ opinions. In phase 2, the hesitant fuzzy Delphi (HFD) method was implemented to screen and finalise the enablers identified. In phase 3, casual relations among final enablers were determined using the hesitant fuzzy ANP (HF-ANP) method. Finally, in phase 4, the relative importance of enablers was calculated using the hesitant fuzzy best–worst method (HF-BWM). Consequently, this study provided potential strategies for IS that can be implemented quickly and used by local authorities to support SMEs in achieving circular waste management.

The existing literature reflects that the connection between enablers of Industry 4.0 (I4.0), Supply Chain (SC) sustainability and reliability is understudied. To cover this gap, the purpose of this study is to identify and benchmark the enablers of I4.0 for SC sustainability to build a Reliable Supply Chain (RSC).

This study benchmarks the I4.0 enablers for SC sustainability for building a RSC and analyses them with a multi-method approach. The identified potential enablers are validated empirically. A multi-method approach of Analytical Hierarchy Process (AHP), Decision Making Trial and Evaluation Laboratory (DEMATEL) and Preference Ranking for Organization Method for Enrichment Evaluation (PROMETHEE-II) was used to investigate the influence of the identified benchmarking enablers and develop an interrelationship diagram among the identified enablers.

This study benchmarks the potential enablers of I4.0 to achieve high ecological-economic-social gains in SCs considering the Indian scenario. Digitalization of the supply chain, decentralization, smart factory technologies and data security and handling are the most prominent enablers of I4.0 for SC sustainability to build a RSC.

The findings from the study may benefit managers, practitioners, specialists, researchers and policymakers interested in I4.0 sustainability applications.

International experience (IE) has been acknowledged to be the most useful method for developing global leaders. However, not everyone benefits equally from IE. During the last two decades, our understanding of why this is the case and how global leaders learn from IE has rapidly increased. Several individual and organizational enablers facilitating global leader learning from IE have been identified in the literature, as have learning mechanisms that make such learning possible. However, the literature remains fragmented, and there is a great need to integrate the findings in the field. Therefore, the present paper systematically examines peer-reviewed studies on global leaders' learning from IE published between 1998 and 2019. The study contributes to the extant literature by identifying and integrating individual enablers, organizational enablers, and key learning mechanisms from global leaders' IE and by suggesting topics for future research.

This study aims at modelling the enablers of dairy supply chain (DSC) in Indian context.

Interpretive structural modelling (ISM) approach has been used to model the enabler of dairy supply chain. The opinion has been taken from the industry experts and experienced academicians. Further, Matrix Cross-Reference Multiplication Applied to a Classification (MICMAC) used to classify the enablers based on driving and dependence power.

Findings show that stakeholder trust and top management support/leadership are the very crucial enablers in dairy supply chain; they are at a lower level of hierarchical structure and work as primary enablers to development of DSC. While customer satisfaction and financial performance are at top of the digraph, it shows these enablers are the outcome of a smooth supply chain. The MICMAC analysis suggests that the identified enablers are largely classified into dependent and independent enablers; there are no autonomous enablers in the dairy supply chain.

The study can aid businesses in the dairy processing industry in managing demand fluctuations, enhancing product quality, implementing effective information systems and adapting procedures, thereby enhancing supply chain performance.

There is very limited study on enablers of the dairy supply chain in general, while in the Indian context, there is no specific study on modelling the enablers of dairy supply chain.

Today’s marketplace has witnessed intense competitive pressures and high levels of uncertainty and disruption. Therefore, supply chains require agility to obtain a sustainable competitive advantage and cope with uncertainties as well as disruptions. Although a wide range of studies exists on supply chain agility (SCA) from the perspective of antecedents or consequences, there is little research on the investigation of enablers of SCA and their relations among them. Furthermore, the literature has investigated proactive and reactive enablers for enhancing SCA, but most studies have not sufficiently framed their analysis of both aspects synchronically. This paper aims to find out the interrelationships among the proactive and reactive enablers for enhancing SCA.

An extensive literature review has been conducted to identify SCA enablers and a Delphi study has been performed to elucidate SCA enablers in the manufacturing industry in Turkey. Interpretive structural modeling (ISM) has been used to identify the contextual relationship among the SCA enablers, and the model has been validated based on Matriced Impact Croises Multiplication Appliquee a un Classement (MICMAC) analysis.

On theoretical and practical levels, the proposed ISM model in this study can help organizations analyze and interpret interrelationships among enablers of SCA. For managers, it can provide better insights and understanding of the facilitators of SCA to enhance the effectiveness of the supply chain and cope with uncertainties and turbulence. According to results, enhancing “supply and demand side competency”, “delivery speed” and “strategic sourcing” are the most significant enablers of SCA.

The study extends the existing literature related to the enablers of SCA by modeling the proactive and reactive enablers of SCA based on the Al Humdan et al. (2020) classification. Arranging the enablers of SCA in a hierarchy and classifying the enablers into different levels with the help of the ISM-MICMAC approach is an exclusive effort to achieve successful management of the supply chain.

The study aims to explore and classify the key enablers of the Association to Advance Collegiate Schools of Business (AACSB) implementation in business schools.

By applying the Interpretive Structural Model (ISM) approach, it builds a hierarchical model of the identified enablers of AACSB implementation. Additionally, the Fuzzy Matriced Impacts Croises-Multiplication Applique and Classement (FMICMAC) technique is used to classify and determine the influence of these enablers on the implementation of AACSB accreditation.

The paper presents an ISM model of the identified enablers and draws managerial insight from it. Categorization of the key enablers into four groups helps in understanding the relative influence of each group of enablers on the AACSB implementation in business schools.

The proposed model of the key enablers will help business schools that are pursuing AACSB accreditation prioritize the enablers. As the study has considered experts' opinions to establish the model, some amount of bias cannot be discounted.

The development of the ISM model of the key enablers of AACSB implementation in business schools is a unique attempt. The findings will help business schools focus on the key enablers that influence implementation of AACSB standards.

This study aims to identify the enablers of supply chain resilience (SCR) through a literature review and expert panel input in the context of Pakistan and the post-pandemic era. This study also aims to categorize and rank the identified enablers using expert panel input.

A review of the extant literature was conducted to investigate and identify the factors that contribute to SCR. The relative ranking of the enablers was carried out by a group of industry and academic experts. The expert panel was convened to compare the main categories and each enabler in pairs and to score the enablers using triangular fuzzy numbers.

This study identified 16 critical SCR enablers. Using the fuzzy analytic hierarchy process (AHP), these enablers were divided into three groups and analyzed. The results show that financial enablers, technology enablers and then social enablers are prioritized when it comes to SCR in emerging markets. The robustness of the ranking of enablers is tested through sensitivity analysis.

The results shall be helpful for policymakers and managers to understand the important enablers and also help allocate resources to important enablers. Managers will be able to formulate strategies to achieve SCR in an uncertain environment.

This is one of the first attempts to identify and rank the enablers of SCR in an emerging economy context.