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This research aims to develop a blockchain smart contract–enabled framework to resolve power imbalance problems in construction payment.

This research adopts a design science research method to develop the blockchain smart contract–enabled framework. The authors then develop a prototype system. Finally, the authors evaluate its performance in solving power imbalance-induced payment problems.

The results show that the prototype system can resolve power imbalance problems in construction payment by allowing project participants to make transparent and decentralized decisions that are self-enforceable by blockchain smart contracts.

This study provides theoretical explanations for how blockchain smart contracts can resolve power imbalances in construction payment; based on that, it proposes a novel blockchain smart contract–enabled method to rebalance the power of stakeholders in construction payment. Thus, it contributes to the body of knowledge on blockchain technology and construction payment.

This study moves beyond a conceptual framework and develops a practical blockchain smart contract system for resolving power imbalances in construction payment, strengthening construction project members' confidence in using blockchain technology.

The proposed blockchain smart contract–enabled solution helps mitigate negative social impacts associated with late payment and non-payment. Furthermore, the research maximizes trust among participants in payment processes to inspire collaborative culture in the construction industry.

This paper introduces a novel blockchain smart contract integrated method, allowing project stakeholders to resolve power imbalance problems in construction payment through decentralized decision-making.

The purpose of this study is to analyze the relationship between public spending, budget imbalance and underground economy. In addition, this paper investigates how budget imbalance moderates the public spending–underground nexus.

By utilizing a data set spanning from 1995 to 2017 of 35 OECD countries, the study has employed Dynamic Common Correlated Effects (DCCE) approach. The study is also extended to consider the marginal effects of public spending on the underground economy at different degrees of budget imbalance.

The results indicate that an increase in public spending and budget imbalance contributes to the expansion of underground economy. Interestingly, the effects of public spending on the underground economy will enhance and intensify with a higher budget imbalance level. The results are robust to various specifications and their broader implications are discussed.

Governments should carefully implement a fiscal policy with a clear understanding that increasing public spending leads to the expansion of informality. Besides, policymakers should enforce supportive policies to boost economic growth, cooperation and cross-border trade to control the size of the underground economy.

This study stresses the role of public spending, budget imbalance on the underground economy in OECD nations. To the best of the author's knowledge, this study pioneers to explore the moderating effect of budget imbalance in the public spending–undergrround nexus.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-09-2022-0645.

Drawing on information processing theory, the linkage between buffering and bridging and the ability on the part of procurement to resolve demand–supply imbalances is investigated, as well as contexts in which these strategies may be particularly useful or detrimental. Buffering may be achieved through demand change or redundancy, while bridging may be achieved by the means of collaboration or monitoring.

This study employs a hierarchical regression analysis of a survey of 150 Finnish and Swedish procurement and sales and operations planning professionals, each responding from the perspective of their own area of supply responsibility.

Both the demand change and redundancy varieties of buffering are associated with procurement's ability to resolve demand–supply imbalances without delivery disruptions, but not with cost-efficient resolution. Bridging is associated with the cost-efficient resolution of imbalances: while collaboration offers benefits, monitoring seems to make things worse. Dynamism diminishes, while the co-management of procurement in S&OP improves procurement's ability to resolve demand–supply imbalances. The most potent strategy for tackling problematic contexts appears to be buffering via demand change.

The results highlight the importance of procurement in the S&OP process and suggest tactical measures that can be taken to resolve and reduce the effects of supply and demand imbalances.

The results contribute to the procurement and S&OP literature by increasing knowledge regarding the role and integration of procurement to the crucial process of balancing demand and supply operations.

It is widely known that fast-fashion retailers are struggling to keep up with consumer attention for quick responses within the fashion industry. With the advance of Internet and e-commerce, consumers prefer to purchase online. Online platform information has become an essential source for exploring consumer attention. However, there is often a mismatch between the information provided by retailers and the feedback received from consumers, leading to an imbalance between the supply side and demand side of online information. The purpose of this study is therefore to provide a unified approach to discover consumer attention from the design topic aspect by revealing the information imbalance between supply side and demand side.

To address the issue of online information imbalance and discover consumer attention, this study proposed an approach that focuses on the design topic perspective. The design topic is a collection of design elements that represent a clothing-design feature more comprehensively and accurately compared to a single design element. The proposed approach begins with generating design topics through topic modeling based on online information provided by retailers on e-commerce platforms. Two indicators, influence degree and attention degree, are then used to quantify the intensity of supply information and consumer attention related to design topics. Finally, design topic strategy diagrams are constructed to reveal information imbalance and discover consumer attention.

The experimental case demonstrates the existence of information imbalance, indicating that the intensity of supply information and consumer attention from the perspective of design topics is not uniform, although both follow the Pareto principle. The results of consumer attention distribution with heavy power-law tails are consistent with current research findings. This further demonstrates that the proposed approach is capable of discovering consumer attention in the design topic strategy diagrams.

The issue of information imbalance between retailers and consumers poses a challenge in keeping up with customer attention. The proposed approach offers a practical solution by visually identifying the symptoms of information imbalance and discovering consumer attention through design topic strategy diagrams. This approach provides fast-fashion retailers with a valuable reference to seize market opportunities, improve product design and adjust marketing or management strategies.

This study proposes a novel approach to disclose the issue of information imbalance between supply side and demand side and therefore to discover consumer attention from the perspective of design topics. In addition, guidelines for applying the proposed approach for fast-fashion marketing and management are presented.

Grounded on resource dependence theory, the authors explored how power and dependence affect sustainability adoption in an artisanal fishing supply chain (AFSC) in Mexico.

An in-depth longitudinal case study was conducted to identify relationships among fishers, a cooperative and intermediaries using a content analysis of data gathered from a combination of interviews, focus groups, observations, participatory workshops and secondary data.

As a result of the existing power imbalance among AFSC members, mediated forces (e.g. rewards for intermediary–fishers relationship) were the most prominent observed. In addition, a close and high dependence on resources affecting supply chain sustainability (SCS) adoption was identified. For example, within intermediary–cooperative relationships, a power imbalance caused mostly by financial resource dependence generated a negative impact on economic sustainability related to unfair prices and unfair trade. The results, thus, showed the detrimental influence of intermediaries among AFSC members on SCS adoption.

A greater understanding of power imbalance and dependence can help AFSC members to identify their weaknesses and develop actions to adopt sustainability.

Unlike previous research, the authors go beyond the often positive research focus of SCS studies and provide, through the resource dependence theory, a longitudinal view on how power imbalance negatively affects SCS adoption.

Participation in global value chains (GVCs) is increasingly related to the economic growth of any country. The conceivable beneficial impact of GVCs on economic growth differs across countries and could be modified with the countries' domestic institutional arrangements. However, ignoring the complementarity between the components of institutional quality led to ignorance of the institutional imbalance present in the country. Hence, the primary purpose of this study is to examine the role of institutional imbalance as a moderating variable between GVC participation and economic growth from 2000 to 2018.

To address the issue of endogeneity among the variables in the model, the study employs the generalized methods of moments (GMM) as an econometric analysis method.

The study finds that well-functioning domestic institutions facilitate the positive impact of GVC participation on economic growth. Conversely, an increased institutional imbalance harms the relationship between GVC participation and economic growth. These findings emphasize a balanced portfolio of institutional components. It advocates the holistic development of each component to reap greater benefits for GVC participation for any country. The study highlights that the weakness in one of the components must be addressed rather than substituted by increasing the strength of another component.

The policies should be framed to improve the weakest component first, followed by other components of institutional quality. Simultaneous reforms involving all the dimensions of institutional quality would smoothen the path of transforming GVCs trade to the country's economic development. Additionally, the high institutional imbalance can provide a bird's eye view to policymakers to work on specific aspects of institutional quality more rigorously.

The existing literature has used a combined measure of institutional quality as a mediator variable while measuring the impact of GVC participation on economic growth. While using a combined measure, it ignores the complementarity among its components. Assuming substitutability among various components may lead to an incorrect estimation. Using the arguments proposed by Bolen and Sobel (2020), the present study considers the existence of complementarity among various components of institutional quality. It calculates the institutional imbalance used as a moderating variable while estimating the impact of GVC participation on economic growth.

Leveraging Emerson’s theory of power and motivated reasoning, this study aims to explore how the net power of an individual and actual, instead of perceived, vulnerability results in asymmetric trust and distrust development in a dyadic relationship.

Based on extant literature and gaps in the literature, this conceptual paper hypothesises and proposes trust formation based on power dynamics and vulnerability.

This research extends the knowledge base by exploring the role of actual vulnerability over perceived vulnerability in trust formation and distrust formation.

The research propositions imply that the dyadic trust formation process is not rational, and trust itself is not symmetrical but asymmetrical. The net power possessed by one individual over the other drives trust. Net power balance determines the actual vulnerability of the focal individual, and then the individual, through motivated reasoning, trusts or distrusts another individual. Scholars, going forward, could explore how trust formation varies at group and firm levels.

Extant literature has not explored the role of power imbalance in determining actual (versus perceived) vulnerability that influences trust formation between parties. The conceptual paper fills this gap.

Recommendation systems are a fundamental tool for hotels to adopt a differentiating competitive strategy. The main purpose of this work is to use machine learning techniques to treat imbalanced data sets, not applied until now in the tourism field. These techniques have allowed the authors to analyse the influence of imbalance data on hotel recommendation models and how this phenomenon affects client dissatisfaction.

An opinion survey was conducted among hotel customers of different categories in 120 different countries. A total of 135.102 surveys were collected over eleven quarters. A longitudinal design was conducted during this period. A binary logistic model was applied using the function generalized lineal model (GLM).

Through the analysis of a representative amount of data, the authors empirically demonstrate that the imbalance phenomenon is systematically present in hotel recommendation surveys. In addition, the authors show that the imbalance exists independently of the period in which the survey is done, which means that it is intrinsic to recommendation surveys on this topic. The authors demonstrate the improvement of recommendation systems highlighting the presence of imbalance data and consequences for marketing strategies.

The main contribution of the current work is to apply to the tourism sector the framework for imbalanced data, typically used in the machine learning, improving predictive models.

Traditional Chinese medicine (TCM) prescriptions have always relied on the experience of TCM doctors, and machine learning(ML) provides a technical means for learning these experiences and intelligently assists in prescribing. However, in TCM prescription, there are the main (Jun) herb and the auxiliary (Chen, Zuo and Shi) herb collocations. In a prescription, the types of auxiliary herbs are often more than the main herb and the auxiliary herbs often appear in other prescriptions. This leads to different frequencies of different herbs in prescriptions, namely, imbalanced labels (herbs). As a result, the existing ML algorithms are biased, and it is difficult to predict the main herb with less frequency in the actual prediction and poor performance. In order to solve the impact of this problem, this paper proposes a framework for multi-label traditional Chinese medicine (ML-TCM) based on multi-label resampling.

In this work, a multi-label learning framework is proposed that adopts and compares the multi-label random resampling (MLROS), multi-label synthesized resampling (MLSMOTE) and multi-label synthesized resampling based on local label imbalance (MLSOL), three multi-label oversampling techniques to rebalance the TCM data.

The experimental results show that after resampling, the less frequent but important herbs can be predicted more accurately. The MLSOL method is shown to be the best with over 10% improvements on average because it balances the data by considering both features and labels when resampling.

The authors first systematically analyzed the label imbalance problem of different sampling methods in the field of TCM and provide a solution. And through the experimental results analysis, the authors proved the feasibility of this method, which can improve the performance by 10%−30% compared with the state-of-the-art methods.

While prior research has established that traumatic brain injury (TBI) is a risk factor for violent offending, there is little understanding of mechanisms that may underpin this relationship. This is problematic, as a better understanding of these mechanisms could facilitate more effective targeting of treatment. This study aims to address these gaps in the extant literature by examining TBI as a predictor of violent offending and test for mediation effects through cognitive constructs of dual systems imbalance and hostility among a sample of justice-involved youth (JIY).

The Pathways to Desistance data were analyzed. The first three waves of this data set comprising the responses of 1,354 JIY were analyzed. Generalized structural equation modeling was used to test for direct and indirect effects of interest. A bootstrap resampling process was used to compute unbiased standard errors for determining the statistical significance of mediation effects.

Lifetime experience of TBI was associated with increased violent offending frequency at follow-up. Hostility significantly mediated this relationship, but dual systems imbalance did not. This indicated that programming focused on reducing hostility among JIY who have experienced TBI could aid in reducing violent recidivism rates.

To the best of the author’s knowledge, this study was the first to identify significant mediation of the relationship between TBI and violent offending through hostility.