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Despite the considerable hype about how Big Data Analytics (BDA) can transform businesses and advance their capabilities, recognising its strategic value through successful adoption is yet to be appreciated. The purpose of this paper is to focus on the process-level value-chain realisation of BDA adoption between SMEs and large organisations.

Resource-based theory offered the lens for developing a conceptual BDA process-level value chain adoption model. A combined two-staged regression-artificial neural network approach has been utilised for 369 small, medium (SMEs) and large organisations to verify their critical value chain process-level drivers for successful organisational adoption of BDA.

The findings revealed that organisational BDA adoption success is driven predominantly by product—and service-process-level value, with distinctive discrepancies dependent on the organisation’s size. Large organisations primarily embrace BDA for their external value chain dimensions, while SMEs encompass its internal value chain cues. As such, businesses will be advised to acknowledge their organisational dynamics and precise size to develop the right strategies to adopt BDA successfully.

The study advances the understanding of the role of internal and external value chain drivers in influencing how BDA can be successfully adopted in SMEs and large organisations. Thus, appreciating the organisation’s unique attributes, including its size, will need to be carefully examined. By investigating these elements, this research has shed new light on how developing such innovative capabilities and competencies must be carefully crafted to help create a sustainable competitive advantage.

For an organisational positioning, acknowledging the role of internal and external value chain drivers is critical for implementing the right strategies for adopting BDA. For larger businesses, resources for innovation often can be widely available compared to SMEs. As such, they can manage their costs and associated risks resourcefully. By considering the identified value-chain-related adoption success factors, businesses should be better positioned to assess their competencies while being prepared to adopt BDA.

The study offers the research and business community empirical-based insights into the strategies needed to successfully adopt big data in an organisation from a process-level value chain perspective.

Blockchain technology (BCT) can play a vital role in manufacturing industries by providing visibility and real-time transparency. With BCT adoption, manufacturers can achieve higher productivity, better quality, flexibility and cost-effectiveness. The current study aims to prioritize the performance metrics and ranking of enablers that may influence the adoption of BCT in manufacturing industries through a hybrid framework.

Through an extensive literature review, 4 major criteria with 26 enablers were identified. Pythagorean fuzzy analytical hierarchy process (AHP) method was used to compute the weights of the enablers and the Pythagorean fuzzy combined compromise solution (Co-Co-So) method was used to prioritize the 17-performance metrics. Sensitivity analysis was then carried out to check the robustness of the developed framework.

According to the results, data security enablers were the most significant among the major criteria, followed by technology-oriented enablers, sustainability and human resources and quality-related enablers. Further, the ranking of performance metrics shows that data hacking complaints per year, data storage capacity and number of advanced technologies available for BCT are the top three important performance metrics. Framework robustness was confirmed by sensitivity analysis.

The developed framework will contribute to understanding and simplifying the BCT implementation process in manufacturing industries to a significant level. Practitioners and managers may use the developed framework to facilitate BCT adoption and evaluate the performance of the manufacturing system.

This study can be considered as the first attempt to the best of the author’s knowledge as no such hybrid framework combining enablers and performance indicators was developed earlier.

The main purpose of this research study is to investigate and examine the factors that might influence the intention to adopt and use mobile payment and their relationships during the COVID-19 pandemic.

This research study used both mobile payment adoption literature, The Technology Adoption Model and Unified Theory of Acceptance and Use of Technology, to propose a conceptual framework for mobile payment adoption. Quantitative method is used in which 306 participants responded to an online survey to validate the proposed conceptual framework.

The introduced integrated model embraced perceived risk, transaction transparency, mobile payment usefulness, social influence, performance expectation as independent variables and usage continuation intention to adopt mobile payment as the dependent variable. The results from data analysis have statistically revealed significant relationships and a positive impact of perceived risk, mobile payment usefulness, social influence and performance expectation. Also, the results identified a negative impact for the transaction transparency factor. As this research study is conducted at a later stage of the COVID-19 pandemic, it adds value to the existing literature by providing insights to business managers on the factors influencing mobile payment usage and other implications related to increasing the market potential for businesses in the new normality of the coronavirus pandemic.

This paper offers a combination conceptual framework of mobile payment adoption based on a literature review on mobile payment adoption from information systems perspective. It adapts integrated model to establish a more comprehensive innovation adoption framework for mobile payment.

This paper explores the Adoption of Technological Innovation (ATI) in the healthcare industry. It investigates how the literature has evolved, and what are the emerging innovation dimensions in the healthcare industry adoption studies.

We followed a mixed-method approach combining bibliometric methods and topic modeling, with 57 papers being deeply analyzed.

Our results identify three latent topics. The first one is related to the digitalization in healthcare with a specific focus on the COVID-19 pandemic. The second one groups up the word combinations dealing with the research models and their constructs. The third one refers to the healthcare systems/professionals and their resistance to ATI.

The study’s sample selection focused on scientific journals included in the Academic Journal Guide and in the FT Research Rank. However, the paper identifies trends that offer managerial insights for stakeholders in the healthcare industry.

ATI has the potential to revolutionize the health service delivery system and to decentralize services traditionally provided in hospitals or medical centers. All this would contribute to a reduction in waiting lists and the provision of proximity services.

The originality of the paper lies in the combination of two methods: bibliometric analysis and topic modeling. This approach allowed us to understand the ATI evolutions in the healthcare industry.

Climate change and global warming are two major ecological concerns. The construction industry has instituted measures such as green construction in response to these concerns. This study seeks to assess the views of architects on the uptake of innovative green construction (IGC) in Ghana.

This study applies the theory of planned behaviour (TPB) to investigate the factors influencing the decision to adopt IGC. A structured questionnaire was issued to architects who have been involved in the design and/or construction of green buildings in Ghana. The dataset was obtained from 61 architects and Partial Least Square was used to analyse the data.

The results show that two TPB factors; attitude and perceived behavioural control are significant determinants of architects’ intention to adopt IGC. Interestingly, behavioural intention did not have an influence on the actual behaviour of IGC adoption. However, perceived behavioural control had a significant impact on the actual adoption of IGC.

Contextually, the findings of the study will contribute to policy development and marketing of various green innovation within the Ghanaian construction milieu. Practically, the study will help encourage stakeholders to build the right capacity and competence towards the uptake of IGC.

The originality of the paper is based on the theoretical framework underpinning the study and the target respondents as the unit of analysis in assessing the full uptake of IGC in Ghana. The utility of the model evinces the role behavioural changes play in ecological development.

The authors study how cloud adoption decision making unfolds in organizations and present the dynamic process leading to a decision to adopt or reject cloud computing. The authors thus complement earlier literature on factors that influence cloud adoption.

The authors adopt an interpretive epistemology to understand the process of cloud adoption decision making. Following an empirical investigation drawing on interviews with senior managers who led the cloud adoption decision making in organizations from across Europe. The authors outline a framework that shows how cloud adoptions follow multiple cycles in three broad phases.

The study findings demonstrate that cloud adoption decision making is a recursive process of learning about cloud through three broad phases: building perception about cloud possibilities, contextualizing cloud possibilities in terms of current computing resources and exposing the cloud proposition to others involved in making the decision. Building on these findings, the authors construct a framework of this process which can inform practitioners in making decisions on cloud adoption.

This work contributes to authors understanding of how cloud adoption decisions unfold and provides a framework for cloud adoption decisions that has theoretical and practical value. The study further demonstrates the role of the decision-leader, typically the CIO, in this process and identifies how other internal and external stakeholders are involved. It sheds light on the relevance of the phases of the cloud adoption decision-making process to different cloud adoption factors identified in the extant literature.

The technology acceptance model (TAM) is a widely used framework explaining why users accept new technologies. Still, its relevance is questioned because of evolving consumer behavior, demographics and technology. Contrary to a research paper or systematic literature review, the purpose of this critical reflection paper is to discuss TAM's relevance and limitations in hospitality and tourism research.

This paper uses a critical reflective approach, enabling a comprehensive review and synthesis of recent academic literature on TAM. The critical evaluation encompasses its historical trajectory, evolutionary growth, identified limitations and, more specifically, its relevance in the context of hospitality and tourism research.

TAM's limitations within the hospitality and tourism context revolve around its individual-centric perspective, limited scope, static nature, cultural applicability and reliance on self-reported measures.

To optimize TAM's efficacy, the authors propose several strategic recommendations. These include embedding TAM within the specific context of the industry, delving into TAM-driven artificial intelligence adoption, integrating industry-specific factors, acknowledging cultural nuances and using comprehensive research methods, such as mixed methods approach. It is imperative for researchers to critically assess TAM's suitability for their studies and be open to exploring alternative models or methods that can adeptly navigate the distinctive dynamics of the industry.

This critical reflection paper prompts a profound exploration of technology adoption within the dynamic hospitality and tourism sector, makes insightful inquiries into TAM's future potential and presents recommendations.

This paper aims to identify potential barriers to Industry 4.0 adoption for manufacturers and examine the changes that must be made to production processes to implement Industry 4.0 successfully. It aims to develop technology by assisting with the successful implementation of Industry 4.0 in the manufacturing process by using smart system techniques.

Multiple case studies are used in this paper by using the smart system and Matlab, and semi-structured interviews are used to collect qualitative data.

Standardization, management support, skills, and costs have been cited as challenges for most businesses. Most businesses struggle with data interoperability. Complexity, information security, scalability, and network externalities provide challenges for some businesses. Environmental concerns are less likely to affect businesses with higher degrees of maturity. Additionally, it enables the Technical Director’s expertise to participate in the measurement using ambiguous input and output using language phrases. The outcomes of the numerous tests conducted on the approaches are extensively studied in the provided method.

In this research, a multiple-case study aims to carry out a thorough investigation of the issue in its actual setting.

This study aims to provide insights into the possible impact of International Public Sector Accounting Standards (IPSAS) on public governance and perceived levels of corruption in developing countries.

Through a multivariate analysis on panel data applied to 36 countries in the Middle East and North Africa (MENA) region and sub-Saharan Africa over the period 2010–2020, the authors test the impact of IPSAS adoption on transparency, accountability and perceptions of less corruption. The authors examine the moderating role of transparency and accountability in the strength of the relationship between IPSAS and perceived corruption.

The main results show that IPSAS adoption promotes an increase in transparency and accountability and leads to the perception of less corruption. Additional tests show that transparency and accountability strengthen the effect of IPSAS adoption and experience on perceived corruption.

The first limitation may be the use of the Transparency International CPI to measure the level of perceived corruption. Probably, the CPI does not reflect the actual levels of corruption in countries while the literature argues that these two measures are related. Also, the lack of data on the status and level of adoption of IPSAS by governments may be one limitation of the sample.

The study may help public authorities in their decision to adopt IPSAS. In light of the findings, standard-setting bodies could be encouraged to strengthen the disclosure requirements of IPSAS that make governments more transparent and accountable to limit perceptions of corruption.

This study may also help citizens understand the benefits of such reforms in protecting public assets and how such standards may help improve social welfare.

To the best of the authors’ knowledge, this is one of the few studies that examines the impact of IPSAS on good governance by combining the dimensions of transparency, accountability and perceptions of corruption in DCs. It also provides insights into the moderating role of public governance pillars. Finally, it includes the IPSAS experience of the country, which has been little tested previously.

Despite the growing interest in blockchain technology (BCT) applications in the agri-food industry, evidence of their economic and strategic implications remains scarce. This study aims to contribute to filling this gap by jointly investigating how BCT adoption affects transactional relationships, and how it contributes to the firm’s strategic resources.

An explanatory case study is conducted based on a theoretical framework grounded on transaction cost economics and the resource-based-dynamic capabilities view. Six BCT implementations by agri-food firms are studied. Data were collected through semi-structured interviews and analysed using thematic analysis.

Findings reveal that BCT benefits depend on how companies integrate technology across their supply chains. In fact, the results suggest that overall transaction efficiency within the supply chain is enhanced only for those firms prioritising stakeholder engagement during technology implementation and leveraging existing trust relationships with economic agents. Moreover, the results suggest that BCT is not yet perceived as a strategic resource, but rather that it has the potential to enhance firms’ operational-adaptive, absorptive and innovative capabilities. When all supply chain actors clearly understand blockchain’s functionality and value, the development of these capabilities becomes more pronounced.

The study identifies two BCT adoption configurations. One primarily focuses on enhancing supply chain efficiency and transparency (dynamic BCT), while the other uses BCT mainly for marketing purposes (static BCT). These configurations lead to varied possibilities for leveraging BCT’s potential advantages. Furthermore, they show how a mismatch between a strategic approach and its chosen configuration could work against any positive impact and lead to disillusionment with the BCT. Thus, managers should assess carefully the impact of such different configuration choices on performance.

To the best of the authors’ knowledge, this is the first study to attempt to analyse the economic implications of adopting BCT in the food sector from both a firm and supply chain perspective. Additionally, it shows how interpreting these impacts is contingent on the diverse modalities for embedding BCT into existing supply chains.