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The purpose of this paper is to investigate the performance of unpaced reliable production lines that are unbalanced in terms of their mean operation times, coefficients of variation and buffer capacities.

Simulations were carried out for five‐ and eight‐station lines with various buffer capacities and degrees of means imbalance. Throughput, idle time and average buffer level performance indicators were generated and statistically analysed.

The results show that an inverted bowl allocation of mean service times, combined with a bowl configuration for coefficients of variation and a decreasing order of buffer sizes results in higher throughput and lower idle times than a balanced line counterpart. In addition, considerable reductions in average inventory levels were consistently obtained when utilizing a configuration of progressively faster stations, coupled with a bowl‐shaped pattern for coefficients of variation and an ascending buffer size order.

The results for these specific experiments imply that resources expended on trying to achieve a balanced line could be better used by seizing upon possible enhanced performance via controlled mean time, variability and buffer imbalance. Results are valid for only the line type and parameter values used (simulation results are specific and not general).

Guidelines are provided on design strategies for allocating labour and capital unevenly in unpaced lines for better performance in terms of increased throughput or lowered idle time or average buffer levels.

This paper might be viewed as one of the first simulation investigations into the performance of unpaced production lines with three sources of imbalance.

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.

The two main approaches to the analysis of technical change are impact studies which are concerned with quantitative measures of the effects of technical change and case studies which are used to develop inductive generalisations about the sources and directions of technical change. Each of these approaches has deficiencies which are widely and frankly recognised by their respective practitioners.

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.

This study investigates the issue of power in business‐to‐business relationships and constitutes an appraisal of the theory relating to issues of supply chain relationships; in which the received view from the relationship marketing literature with its emphasis on trust, dyadic symmetry and mutuality is questioned. It is contended, alternatively that other types of relationships, for example, those based on selfishness are equally relevant; and that power imbalanced business relationships are just as important to the understanding of business exchange.

Specific reference is made to power relationships in vertical food supply channels in the UK, where the majority of control lies in the hands of large multiple retailers. The paper cites case material drawn from studies into the relationships between UK‐based fresh food supplier organisations and their principal customers, the leading UK food retailers.

Specific outcomes are determined with regard to issues of power, mutuality and the nature of power‐dependent relationships. Power play is omnipresent in exchange relationships and is not always seen in a negative light. Relationship‐building is perfectly possible in asymmetric relationships and weaker parties are tolerant of power imbalance.

The study concludes that power should be a central consideration when concerned with business relationships and that imbalances in power are no specific barrier to parties entering into relationships or to their success.

Findings from chosen case studies are transferable to other vertical channel circumstances. Any future investigation should consider the expression and limits of power and the boundaries of tolerance to power imbalance.

Provides evidence of the nature of power‐dependent business relationships.

The purpose of this paper is to investigate the performance of unpaced unreliable production lines that are deliberately unbalanced in terms of their coefficients of variation (CVs).

A series of simulation experiments were carried out for five and eight station lines with mean buffer space set at one, two, four and six units. CVs were allocated in 12 different configurations for each of these lines.

The results show that the best unbalanced CV patterns in terms of throughput rates or idle times as compared to a balanced line counterpart are those where the steadiest stations are concentrated near the centre of the line. On the other hand, either concentrating the steadier operators towards the centre or close to the end of the line gives best average buffer level results.

The results provide guidelines for production line managers when designing unpaced unbalanced lines depending on their performance aims.

The investigation of the effects of unbalancing CVs in unreliable lines has not previously been studied and can provide insights into how best to place workstations with differing variability along the line.

Since 1978, China has made tremendous economic achievements through industrial upgrading. However, these achievements are accompanied by an expanding income gap between rural and urban areas. The purpose of this paper is to examine the relationship between industrial structure and urban–rural income inequality in China.

Using the fixed-effects model and provincial data for the period 1985–2019, this paper estimates a linear relationship between industrial structure and urban–rural income inequality. By decomposing total income inequality into four components, the paper then analyzes how industrial structure affects each component.

The results show that industrial structure imbalance and industrial upgrading are positively associated with urban–rural income inequality. The positive effect of industrial imbalance mainly comes from widening the wage gap, while that of industrial upgrading mainly comes from aggravating business income inequality and property income inequality. Moreover, industrial balance and upgrading are conducive to increasing the share of wage income at the cost of property income.

By progressively examining the total inequality and the inequality of income components, this paper provides a better understanding of how industrial structure affects urban and rural income inequality. The findings of this study highlight the “inequality cost” associated with industrial structure adjustment, which provide policy-related insights on the balance development of urban and rural areas.

Credit scoring datasets are generally unbalanced. The number of repaid loans is higher than that of defaulted ones. Therefore, the classification of these data is biased toward the majority class, which practically means that it tends to attribute a mistaken “good borrower” status even to “very risky borrowers”. In addition to the use of statistics and machine learning classifiers, this paper aims to explore the relevance and performance of sampling models combined with statistical prediction and artificial intelligence techniques to predict and quantify the default probability based on real-world credit data.

A real database from a Tunisian commercial bank was used and unbalanced data issues were addressed by the random over-sampling (ROS) and synthetic minority over-sampling technique (SMOTE). Performance was evaluated in terms of the confusion matrix and the receiver operating characteristic curve.

The results indicated that the combination of intelligent and statistical techniques and re-sampling approaches are promising for the default rate management and provide accurate credit risk estimates.

This paper empirically investigates the effectiveness of ROS and SMOTE in combination with logistic regression, artificial neural networks and support vector machines. The authors address the role of sampling strategies in the Tunisian credit market and its impact on credit risk. These sampling strategies may help financial institutions to reduce the erroneous classification costs in comparison with the unbalanced original data and may serve as a means for improving the bank’s performance and competitiveness.

In the euro’s initial years, Greece, Ireland, Italy, Portugal and Spain observed capital flow bonanzas and credit-booms, two cycles known to precede banking crises. Domestic banks fuelled those cycles via funding obtained from foreign financial institutions. Yet, these countries’ banking and financial crises have unfolded in different modes. In Ireland and Spain, credit-booms propelled real-estate bubbles, which dragged banks into crises, with governments’ accounts later being affected when rescuing banks (Spanish regional banks, and all Irish major banks). In Greece and Italy, extra monetary means perpetuated government imbalances (e.g. debt levels above 100% of GDP, large yearly deficits). More severely in Greece, banks were brought into crises by sovereign crises. In Portugal, a mixture of private and public sector–led crises have occurred. Our comparative study finds that these crises: (1) are connected to shocks and imbalances caused by dangerous banking sector cycles during the monetary integration process; (2) were not mere expansions of the US subprime crisis; (3) were not only caused by country-specific features and institutions; and (4) followed distinct paths, therefore, a uniform model encompassing all post-euro crises cannot exist.

The purpose of this paper is to investigate and compare the impact of order imbalance on returns, liquidity and price volatility in agricultural futures markets on an intraday basis. The authors examine whether order imbalance is more powerful to explain variations in asset prices compared to other indicators of trading activity, particularly trading volume.

Using Chicago Mercantile Exchange best bid best offer data, the impact of order imbalance is analyzed via regression analyses. The analyses are carried out for corn, wheat, soy, live cattle and lean hogs in March 2008 and March 2016.

Results confirm the positive relation between order imbalance and returns as well as between order imbalance and price volatility as suggested by market microstructure models. Order imbalance, however, does not generally outperform trading volume as an explanatory variable.

For some contracts, returns can be predicted using lagged order imbalance. This offers the opportunity to derive profitable trading strategies.

This paper is one of the first attempts to explore the relationship between order imbalance and returns, liquidity and volatility for agricultural commodity futures on an intraday basis, accounting for the increased trading volume and for the high speed at which new information enters the market in an electronic trading environment.