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This paper aims to identify and provide a theoretical explanation for the barriers that hinder the adoption of emerging technologies in the architecture, engineering and construction industry, irrespective of the company’s size, specialization or geographical location. In addition, the paper proposes potential areas for future research in this domain.

A list of barriers hindering the adoption of emerging technologies was identified and clarified using a systematic literature review of various scientific sources.

Twenty-five barriers were recognized and explained and some suggestions for future research studies were provided.

The barriers related to a specific country or region or to a specific technology were excluded.

By providing a deeper comprehension of the barriers hindering the adoption of emerging technologies, this review is expected to encourage their adoption in the industry. Furthermore, it could prove valuable in devising effective strategies for the successful implementation of these technologies.

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.

With growing concern about sustainable development and increased awareness of environmental issues, digital technologies (DTs) are gaining prominence and becoming a promising trend to improve productivity, sustainability and project performance in the construction industry. Nonetheless, the uptake of DTs in the construction industry has been limited and plagued with roadblocks. This study aims to identify critical barriers for construction organisations to adopt DTs and to demonstrate relationships between organisational characteristics and the perceived DTs adoption barriers.

This study adopted an explanatory sequential design by combining the advantages of quantitative and qualitative data. Data collection methods include literature review, a pilot study, questionnaire survey, and semi-structured interviews. Questionnaire data were analysed by using SPSS and multivariate regression technique. The interview data were processed by using content analysis to validate and supplement findings from the questionnaire.

Based on the survey and interview results, eight critical barriers were identified: the three top critical barriers are (1) “status quo industry standards”, (2) “lack of client interest” and (3) “lack of financial need/drive for using DTs”. The eight critical barriers were further classified into technical, environmental, and social dimensions to determine the major constructs that hinder DTs adoption. A theoretical framework articulating critical barriers with underlying components and root causes was also proposed. Furthermore, by using multivariate regression analysis, a model was developed to link the organisational characteristics with barriers to DTs adoption.

By referring to the framework and the model developed, academics, industry practitioners, and decision makers can identify pivotal areas for improvement, make informed decisions and implement remedial measures to remove the barriers to digitalisation transformation.

This study contributes to the literature on construction innovations by investigating barriers to DTs adoption holistically as well as perceptions of the impact of organisational attributes on these barriers. It establishes the groundwork for future empirical research into the strategic consolidation of movement of DTs adoption and diffusion.

This article aims at - exploring and prioritizing the barriers to adoption of digitization in supply chains (SCs), categorizing them into sustainability triple bottom line (STBL) based upon their direct impact and suggesting digital technologies to address each barrier.

A five-phase methodology is used which consists of an exploration of 44 barriers to the adoption of digitization in SCs, analysis of 44 barriers for mean, standard deviation and Cronbach alpha based on questionnaire-based feedback of 25 experts, extraction of 10 most significant barriers through 05 experts, followed by categorization of the barriers into STBL referring to their direct impact on STBL, prioritization of ten barriers using Fuzzy Technique for Order Performance by Similarity to Ideal Solution and recommendation of digital technologies to address each barrier.

While all the barriers considered in this study significantly impede the adoption of digitization in SCs, lack of top management commitment (B1) is found to be most crucial while lack of culture toward use of information and communication technology required for digitization (B3) has minimum impact. Large investment in digital infrastructure (B6), difficulty in integration of cyber physical systems (CPSs) on varied platforms (B8) and lack of experts having knowledge of digital technologies (B2) are equally important barriers requiring more attention while adopting digitization in SCs.

This study is mainly based on feedback from 25 seasoned experts; a wider cross section of experts will give more insight.

The outcomes are very significant for organizations looking to adopt digitization in their SCs. Simultaneous consideration to all the barriers becomes impractical hence prioritization of same will be useful for the SC managers to benchmark their preparedness and decide strategies for the adoption of digitization with due consideration toward the impact of barriers on STBL. The digital technologies recommended will further aid in planning the digital strategies to address each barrier.

A unique approach to explore, analyze, prioritize and categorize the barriers to adoption of digitization in SCs is used to provide a deeper understanding of factors deterring the same. It implies that a supportive top management along with systematic allocation of finances plays a crucial role. The importance of availability of digital experts for integrating CPSs on a single platform is also highlighted. The digital technologies recommended will further assist the organizations toward adoption of digitization in SCs with due consideration to STBL.

The research explores the shift to Quality 4.0, examining the move towards a data-focussed transformation within organizational frameworks. This transition is characterized by incorporating Industry 4.0 technological innovations into existing quality management frameworks, signifying a significant evolution in quality control systems. Despite the evident advantages, the practical deployment in the Indian manufacturing sector encounters various obstacles. This research is dedicated to a thorough examination of these impediments. It is structured around a set of pivotal research questions: First, it seeks to identify the key barriers that impede the adoption of Quality 4.0. Second, it aims to elucidate these barriers' interrelations and mutual dependencies. Thirdly, the research prioritizes these barriers in terms of their significance to the adoption process. Finally, it contemplates the ramifications of these priorities for the strategic advancement of manufacturing practices and the development of informed policies. By answering these questions, the research provides a detailed understanding of the challenges faced. It offers actionable insights for practitioners and policymakers implementing Quality 4.0 in the Indian manufacturing sector.

Employing Interpretive Structural Modelling and Matrix Impact of Cross Multiplication Applied to Classification, the authors probe the interdependencies amongst fourteen identified barriers inhibiting Quality 4.0 adoption. These barriers were categorized according to their driving power and dependence, providing a richer understanding of the dynamic obstacles within the Technology–Organization–Environment (TOE) framework.

The study results highlight the lack of Quality 4.0 standards and Big Data Analytics (BDA) tools as fundamental obstacles to integrating Quality 4.0 within the Indian manufacturing sector. Additionally, the study results contravene dominant academic narratives, suggesting that the cumulative impact of organizational barriers is marginal, contrary to theoretical postulations emphasizing their central significance in Quality 4.0 assimilation.

This research provides concrete strategies, such as developing a collaborative platform for sharing best practices in Quality 4.0 standards, which fosters a synergistic relationship between organizations and policymakers, for instance, by creating a joint task force, comprised of industry leaders and regulatory bodies, dedicated to formulating and disseminating comprehensive guidelines for Quality 4.0 adoption. This initiative could lead to establishing industry-wide standards, benefiting from the pooled expertise of diverse stakeholders. Additionally, the study underscores the necessity for robust, standardized Big Data Analytics tools specifically designed to meet the Quality 4.0 criteria, which can be developed through public-private partnerships. These tools would facilitate the seamless integration of Quality 4.0 processes, demonstrating a direct route for overcoming the barriers of inadequate standards.

This research delineates specific obstacles to Quality 4.0 adoption by applying the TOE framework, detailing how these barriers interact with and influence each other, particularly highlighting the previously overlooked environmental factors. The analysis reveals a critical interdependence between “lack of standards for Quality 4.0” and “lack of standardized BDA tools and solutions,” providing nuanced insights into their conjoined effect on stalling progress in this field. Moreover, the study contributes to the theoretical body of knowledge by mapping out these novel impediments, offering a more comprehensive understanding of the challenges faced in adopting Quality 4.0.

The adoption of value management (VM) has been on the rise in the construction industry, especially in the past decade. Identifying the main barriers to VM application for ameliorating poor project outcomes in the construction industry is a critical step to overcoming its implementation. Therefore, this study aims to examine the barriers to the adoption of VM in the Nigerian construction industry (NCI) and identify the critical ones for action by construction stakeholders.

This study used two rounds of Delphi surveys with 15 VM experts to identify the challenges to VM adoption. The Delphi team’s data from the two surveys were analysed using descriptive statistics. The mean-mode approach was used to identify ten major barriers to VM adoption based on the consensus reached during the second round of the Delphi poll. Following that, an interrater agreement analysis and a significant level analysis were performed.

Of the 17 identified barriers hindering VM adoption on construction projects in the NCI, 10 were found to be profound after statistical analyses of the data. The significant barriers include communication gap between the client and other consultants; delay in design preparation because of VM services; the pretext that VM is being practiced in the construction industry; and the wrong perception of VM by construction professionals, among others. The findings of the analysis suggested that the lack of VM adoption in the NCI was because of inadequate knowledge of VM techniques among construction professionals. Therefore, it is advised that VM training be provided for concerned stakeholders to address the lack of VM expertise among Nigerian construction professionals.

This study investigated the barriers to VM adoption on construction projects in the NCI with the opinions of a few construction professionals who can be regarded as VM experts. As against obtaining large survey responses from construction professionals who may not be knowledgeable in the VM process, the limited VM experts’ responses provide a valid basis for drawing inferences and making recommendations to address the paucity of VM applications in the NCI.

Through a Delphi survey, the critical barriers to the adoption of VM were identified, along with recommendations to help construction stakeholders overcome the challenges and enjoy the benefits of VM.

This research aims to evaluate the impact of barriers experienced by Brazilian companies in adopting Industry 4.0 (I4.0).

As a methodological approach, the survey method was used, adopting the use of the questionnaire for data collection. From the feedback of 99 companies (with an index of 80%), quantitative analyzes of the data were carried out with the aid of factor analysis and linear regression to validate the proposed structural model.

The barriers construct does not impact the I4.0 adoption construct. Directly evaluating the effect of the variables that make up the barriers construct in the I4.0 adoption construct, it was observed that three barriers affect effectively the adoption of I4.0: technological infrastructure; financial constraint and lack of understanding of the benefits of I4.0.

As a limitation, the research was conducted only in the Brazilian context, requiring the development of future studies in other countries that can strengthen the findings of this research.

In addition, the results achieved provide relevant insights into public policymakers and business managers, helping them to deeply understand the barriers that impact the adoption of I4.0. This facilitates the propagation of I4.0 concepts in the context of Brazilian companies and in the formulation of public policies adapted to each sector, allowing a more assertive action in the face of the types of barriers experienced by organizations during the adoption of I4.0.

The findings can help practitioners and policymakers to understand in detail this new industrial model and the difficulties that prevent its implementation.

From an extensive literature review, no studies were identified that statistically validate which barriers effectively affect the adoption of I4.0. This research is a pioneer in proposing a structural model to analyze the barriers experienced by workers during the adoption of I4.0, exploring Brazilian companies, from different economic sectors and sizes. It is noteworthy that the literature still focuses efforts on manufacturing companies.

This paper aims to investigate the effect of factors that inhibit adoption of mobile payments service in India.

Based on the extant literature on mobile payment service and other related literature, factors were identified that drive consumer resistance toward its adoption. It engaged “innovation resistance theory” framework for understanding consumer resistance. The framework addressed five categories of barriers, namely, usage, value, risk, image and tradition that lead to negative perception of innovation, and therefore, induces positive impact on its resistance. Additionally, the study considered a few lesser investigated barriers (habitual use of cash, surveillance, technology) for the study, thus extending the existing theoretical framework. Hypotheses were framed, field data were collected and then analyzed using multivariate techniques.

Few interesting observations were made from the study. Usage, image and value barriers hindered adoption of mobile payment service. In case of men, usage, value and image were the primary barriers. For women, usage, image, habitual use of cash and technology acted as barriers that curbed mobile payments service adoption. Additionally, except risk, tradition and surveillance barriers, relationships of all other constructs with adoption intention were moderated by gender.

This research was limited to the views of the urban population in India who used mobile payments service. The results may vary across geographical contexts because of culture or socioeconomic differences.

The growth of mobile payment service has remained sluggish in India despite high levels of digitization. The study results will offer valuable insights to the Indian business managers and policymakers to identify what action plan needs to be instituted to make mobile payments service more attractive and acceptable to users.

This empirical study extended and tested the classical innovation resistance theory framework by adding three less studied barriers (surveillance, habitual use of cash and technology) in a developing nation, thus enriching the current literature on consumer resistance toward mobile payments. It also examined the moderating effect of gender on mobile payments service adoption.

Rapid innovation and developments in personal electronic technology have encouraged users to change users' devices more frequently than ever, which has resulted in creating a massive increase in the amount of electronic waste. The study focuses on identifying the barriers to closed-loop supply chain (CLSC) in the electronic industry.

A framework for analyzing the relationships among CLSC adoption barriers is designed. The authors adopted the decision-making trial and evaluation laboratory (DEMATEL) technique to determine the critical barriers of electronic CLSC from the opinion of experts in the field.

The outcome from the analysis suggests that cost barriers, financial barrier, process barriers and supplier-side barriers are the main causal factors that prevent the adoption and implementation of e-waste CLSC. The causal relationship indicates that financial barrier is the most influential factor, while phycological barrier is the most flexible barrier to the adoption of e-waste CLSC.

This study is restricted to CLSC adoption barriers in the electronic industry by evaluating 36 sub-barriers grouped into 8 main dimensions related to different members of the supply chain.

Closed-loop adoption barriers have been proposed to understand the crucial barriers to implementation of CLSC in the electronic industry. The cause-and-effect relationship indicates the critical factors to be improved to increase adoption of e-waste CLSC, helping managers and regulatory bodies to mitigate the problem areas.

This study contributes to the literature on CLSC by adopting a multi-criteria decision-making (MCDM) technique which captures the critical barriers of e-waste CLSC adoption in Indian scenario.

Purpose: Nowadays, many terms like computer vision, deep learning, and machine learning have all been made possible by recent artificial intelligence (AI) advances. As new types of employment have risen significantly, there has been significant growth in adopting AI technology in enterprises. Despite the anticipated benefits of AI adoption, many businesses are still struggling to make progress. This research article focuses on the influence of elements affecting the acceptance procedure of AI in organisations.

Design/Methodology/Approach: To achieve this objective, propose a hierarchical paradigm for the same by developing an Interpretive Structural Modelling (ISM). This paper reveals the barriers obstructing AI adoption in organisations and reflects the contextual association and interaction amongst those barriers by emerging a categorised model using the ISM approach. In the next step, cross-impact matrix multiplication is applied for classification analysis to find dependent, independent and linkages.

Findings: As India is now focusing on the implementation of AI adoption, therefore, it is essential to identify these barriers to AI to conceptualise it systematically. These findings can play a significant role in identifying essential points that affect AI adoption in organisations. Results show that low regulations are the most critical factor and functional as the root cause and further lack of IT infrastructure is the barrier. These two factors require the most attention by the government of India to improve AI adoption.

Implications: This study may be utilised by organisations, academic institutions, Universities, and research scholars to fill the academic gap and faster implementation of AI.