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Economic policy uncertainty has increased around the world since the financial crisis of 2007–2008. While scholars have devoted a lot of time and energy to investigating the impact of economic policy uncertainty (EPU) on firm innovation, they have not reached consistent research conclusions. This study aimed to clarify the above research differences by exploring the impact of EPU on firms' relative exploitative innovation emphasis, so as to provide a more comprehensive and granular understanding of the relationship between EPU and firm innovation.

This study obtained 17,165 firm-year data points from 3,107 listed companies in China. It analyzed the above data with a fixed effects model. In addition, this study used an instrumental variables method to solve potential endogeneity problems.

Based on real options theory and contingency theory, the authors proposed and found that EPU has a significant positive effect on relative exploitative innovation emphasis. In addition, the authors proposed and found that this effect is more pronounced in industries with high technological uncertainty, low competitive intensity, and low state monopolization.

This study is the first to explore why firms prefer exploitative innovation over exploratory innovation from the perspective of EPU. In doing so, this study expands and enriches the EPU literature and the innovation literature. Furthermore, by introducing the moderating role of industry environment, this study deepens the authors' understanding of how complex interactions between industry and institutional environments work together to shape firm strategic choices, and especially firm innovation. Finally, the conclusions of this study have important practical implications for shareholders to take measures to balance exploitative innovation and exploratory innovation to achieve better development.

Agriculture is one sector where the Internet of things (IoT) is expected to make a major impact. Yet, its adoption in the sector falls behind expectations. The purpose of this paper is to present the state-of-the-art of IoT in agriculture and investigate its slow adoption in the sector.

The authors have undertaken a systematic review and a synthesis of 1355 relevant publications over the last decade.

This literature review reveals that the “big three” barriers for the overall sector are cost, skills and standardization. The lack of connectivity and data governance are two key reasons why most of the proposed IoT solutions are standalone systems of limited scope, while the majority of commercial IoT efforts focus on practices in the protected indoor environment. Lastly, the analysis of past research along the five layers of the IoT system architecture reveals limited attention to barriers and solutions at the business layer, which represents a research opportunity for information systems scholars.

It is possible that some of relevant publications were missed in the literature search, although the search queries were kept as broad as possible to avoid the exclusion of any relevant work. Any publication written in any other language other than English was excluded from the review. Given the geographical distribution of the reviewed English publications (see section 4.1), it is highly likely that important works written by Chinese and European scholars in their native language were overlooked.

This study provides practical insights into the technical and organisational challenges on the ground. It is the hope that this literature review lays the groundwork for IS researchers who are well positioned to investigate technology adoption challenges in the relatively understudied agriculture sector.

To the best of the authors’ knowledge, this is the first comprehensive review of adoption barriers and solutions across all five layers of the IoT system architecture.

Blockchain has the potential to facilitate a paradigm shift in the construction industry toward effectiveness, transparency and collaboration. However, there is currently a paucity of empirical evidence from real-world construction projects. This study aims to systematically review blockchain adoption barriers, investigate critical ones and propose corresponding solutions.

An integrated method was adopted in this research based on the technology–organization–environment (TOE) theory and fuzzy decision-making trial and evaluation laboratory (DEMATEL) approach. Blockchain adoption barriers were first presented using the TOE framework. Then, key barriers were identified based on the importance and causality analysis in the fuzzy DEMATEL. Several suggestions were proposed to facilitate blockchain diffusion from the standpoints of the government, the industry and construction organizations.

The results highlighted seven key barriers. Specifically, the construction industry is more concerned with environmental barriers, such as policy uncertainties (E2) and technology maturity (E3), while most technical barriers are causal factors, such as “interoperability (T4)” and “smart contracts' security (T2)”.

This study contributes to a better understanding of the problem associated with blockchain implementation and provides policymakers with recommendations.

Identified TOE barriers lay the groundwork for theoretical observations to comprehend the blockchain adoption problem. This research also applied the fuzzy method to blockchain adoption barrier analysis, which can reduce the uncertainty and subjectivity in expert evaluations with a small sample.

In past decades, artificial neural network (ANN) models have revolutionised various stock market operations due to their superior ability to deal with nonlinear data and garnered considerable attention from researchers worldwide. The present study aims to synthesize the research field concerning ANN applications in the stock market to a) systematically map the research trends, key contributors, scientific collaborations, and knowledge structure, and b) uncover the challenges and future research areas in the field.

To provide a comprehensive appraisal of the extant literature, the study adopted the mixed approach of quantitative (bibliometric analysis) and qualitative (intensive review of influential articles) assessment to analyse 1,483 articles published in the Scopus and Web of Science indexed journals during 1992–2022. The bibliographic data was processed and analysed using VOSviewer and R software.

The results revealed the proliferation of articles since 2018, with China as the dominant country, Wang J as the most prolific author, “Expert Systems with Applications” as the leading journal, “computer science” as the dominant subject area, and “stock price forecasting” as the predominantly explored research theme in the field. Furthermore, “portfolio optimization”, “sentiment analysis”, “algorithmic trading”, and “crisis prediction” are found as recently emerged research areas.

To the best of the authors’ knowledge, the current study is a novel attempt that holistically assesses the existing literature on ANN applications throughout the entire domain of stock market. The main contribution of the current study lies in discussing the challenges along with the viable methodological solutions and providing application area-wise knowledge gaps for future studies.

To promote the success of peer-to-peer accommodation, this study examines the effects of online host–guest interaction as well as the interaction's boundary conditions of listing price and reputation on listing popularity.

Using 330,686 data collected from Airbnb in the United States of America, the authors provide empirical evidence to answer whether social-oriented self-presentation and response rate influence listing popularity from the perspective of social exchange theory (SET). In addition, the authors investigate how these two kinds of online host–guest interactions work with listing price and reputation to influence listing popularity.

The results reveal the positive association between online host–guest interaction and listing popularity. Notably, the authors find that listing price strengthens but listing reputation weakens the positive effects of online host–guest interactions on listing popularity in peer-to-peer accommodation.

This study is the first attempt to adopt SET to explain the importance of online host–guest interactions in influencing listing popularity as well as examine the moderating role of listing price and reputation on the above relationship.

The authors examine the association between two important audit partner characteristics and the readability of key audit matters (KAMs) disclosed in the audit reports. Specifically, the authors examine how the readability of KAMs is associated with audit partner tenure and workload.

The authors conduct the study in the audit context of Norway and applied the Flesch reading ease scale to measure the readability levels of reported KAMs in the audit reports of companies listed on the Oslo Stock Exchange. Panel data estimation techniques are applied in estimating how partner tenure and workload are associated with the readability of KAMs. In addition, several robustness tests including different measures of KAMs readability and subsample analyses are performed.

The authors find that audit partner tenure and workload have significant associations with the level of KAMs readability. Specifically, the results show that the reported KAMs become more readable as the audit partner tenure increases but are less readable for partners with more workload. These results appear stronger in subsamples of KAMs typically noted to be more complex and associated with higher risks.

As KAMs represent the most significant issues in financial statements audit, these results provide important insights to stakeholders on the potential impact of audit partner tenure and workload on KAMs readability. Less readable KAMs could derail stakeholders' desire to bridge the information gap between auditors and users of the audit report. The uniqueness of this study lies in its focus on audit partner characteristics as opposed to the audit firm.

Excessive audit partner workload impairs KAMs readability.

As KAMs represent the most significant issues in financial statements audit, these results provide important insights to stakeholders on the potential impact of audit partner tenure and workload on KAMs readability. Less readable KAMs could derail stakeholders' desire to bridge the information gap between auditors and users of the audit report. The uniqueness of this study lies in its focus on audit partner characteristics as opposed to the audit firm.

Prefabricated construction is an innovative technique for decreasing carbon emissions in the construction industry. However, as the investors of housing projects, a majority of developers are unwilling to adopt prefabricated housing in practice. To promote prefabricated housing, this study aims to develop an integrated framework of fuzzy-decision making trial and evaluation laboratory (fuzzy-DEMATEL) and system dynamics (SD) to understand the underlying influencing mechanism of developers' willingness.

Through literature review, a total of 17 influencing factors were identified. Then, the interrelationships among the factors were evaluated by 10 experienced professionals, and the impacts given and received by each factor were further analyzed through fuzzy-DEMATEL. Based on the technology acceptance model (TAM), a SD model was developed to explore the influencing mechanism.

The major cause factors were identified, including mandatory implementation policies, economic incentive policies, environmental protection policies, component standardization and developers' economic strength. This group of factors was expected to be given priority attention in the case of limited resources. On the other hand, the results indicated that economic incentive policies and mandatory implementation policies could affect the developers' willingness via perceived usefulness, while the others mainly influenced perceived ease of use.

Little research has focused on the interrelationships among the influencing factors of developers' willingness to adopt prefabricated housing. This study contributed to understanding the mechanism of developers' willingness from a systematic view and providing the priority of influencing factors. Several strategies were proposed to improve the practical implementation of prefabricated housing development.

The development of the digital age has made data and information more transparent, enhancing the strategic perspectives of both buyers (strategic waiting) and sellers (price fluctuations) in their decision-making. This research investigates the optimal dynamic pricing strategy of the content product developer in relation to their consideration of consumer fairness concerns to elucidate the impact of consumer fairness concerns on the dynamic pricing strategy of the developer.

This paper assumes that monopolistic content developers implement a dynamic pricing strategy for the content product. Through constructing a two-period dynamic pricing game model, this research investigates the optimal decisions of the content developer, contingent upon their consideration or disregard of consumer fairness concerns. In the extension section, the authors additionally account for the influence of myopic consumers on these optimal decisions.

Our findings reveal that the degree of consumer fairness concerns significantly influences the developer’s optimal dynamic pricing decision. When a developer offers content products with lower depth, there is a propensity for the developer to refrain from incorporating consumer fairness concerns into a dynamic pricing strategy. Conversely, in cases where the developer offers a high-depth content product, consumer fairness concerns benefit the developer. Furthermore, our analysis reveals a consistent benefit for the developer from the inclusion of myopic consumers.

Few studies have delved into the conjoined influence of consumer fairness concerns and strategic behavior on dynamic pricing strategy. Our findings indicate that consumer fairness concerns can enhance the efficiency of the value chain for content products under specific conditions. This paper not only enriches the existing literature on dynamic pricing by incorporating consumer fairness concerns theoretically but also offers practical insights. The outcomes of this research can guide content product developers in devising optimal dynamic pricing strategies.

The study examines the impact of socio-economic governance on economic growth in Nigeria. It measures socio-economic governance from the perspective of fiscal policy, using indicators such as investment in education, research and development (R&D) and health.

This study employs the Autoregressive Distributive Lag (ARDL) Bound Testing method to achieve its objective.

The study finds that socio-economic policies aimed at increasing investment in education are crucial for Nigeria’s long-term economic growth. Additionally, investment in R&D positively impacts economic growth. However, the study reveals that investment in health negatively affects economic growth in Nigeria in the long run. This suggests that if a country overinvests in health, it may divert resources from other vital sectors such as education, infrastructure and R&D, which can hinder overall economic growth. The short-run parameter is, however, not statistically significant in this study.

The study’s originality lies in its exploration of the relationship between socio-economic governance and economic growth in Nigeria, specifically from a fiscal policy perspective. It highlights the importance of investing in education and R&D for long-term economic growth. Additionally, the finding that overinvestment in health may have a negative impact on long-term economic growth provides valuable insight for policymakers in Nigeria and other developing countries. Overall, this study’s findings can be beneficial for policymakers and researchers interested in the intersection between socio-economic governance and economic growth in developing countries.

The main purpose of this study is to provide a comprehensive review and critical insights of the application of fuzzy methods in modeling, assessing and understanding the various aspects of green and sustainable supply chains (SSCs).

The present study conducts a systematic literature review (SLR) and bibliometric analysis of 252 research articles. This study employs various tools such as VOSviewer version 1.6.10, Publish or Perish, Mendeley and Excel that aid in descriptive analysis, bibliometric analysis and network visualization. These tools have been used for performing citation analysis, top authors' analysis, co-occurrence of keywords, cluster and content analysis.

The authors have divided the literature into seven application areas and discussed detailed insights. This study has observed that research in the social sustainability area, including various issues like health and safety, labor rights, discrimination, etc. is scarce. Integration of the Industry 4.0 technologies like blockchain, big data analytics, Internet of Things (IoT) with the sustainable and green supply chain (GSC) is a promising field for future research.

The authors' contribution primarily lies in providing the integrated framework which shows the changing trends in the use of fuzzy methods in the sustainability area classifying and consolidating green and sustainable supply chain management (SSCM) literature in seven major areas where fuzzy methods are predominantly applied. These areas have been obtained after the analysis of clusters and content analysis of the literature presenting key insights from the past and developing the conceptual framework for future research studies.