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The purpose of this study is to determine whether the fine wine market is efficient between homogeneous lots and heterogeneous lots.

Auction price data for homogeneous (or solid) lots of fine wines was analyzed to create price prediction models. Those models were used to predict the expected auction price for the bottles within heterogeneous lots. Lastly, models were created to explain and predict the differences between expected and realized prices for heterogenous wine lots.

The results show that large inefficiencies exist. The more complex and expensive the heterogeneous lot, the greater the discount relative to what would have been realized if the bottles had been sold individually. This discount can exceed 50% of the expected auction price.

Heterogeneous lots may arise as a practical requirement from the auction house. Restaurant buyers probably have little interest in such lots because of the inclusion of wines the restaurant will be unable to sell. Collectors may be uniquely positioned to benefit from this price discount.

These results are unique in the literature, because the price dynamics of heterogeneous (or mixed) lots of fine wines have not previously been studied.

The scaling of social initiatives is of immense importance for public innovation. A key factor in the scaling process is the institutional environment, which plays a key role in supporting and coordinating the scaling process. This environment can be either homogeneous or heterogeneous, i.e. dominated by a few or a variety of public organizations, norms and practices. Both environments have advantages and challenges. However, the academic understanding of the influence of the institutional environment is limited, and a systematic empirical assessment of the relationship between the type of environment and scaling is lacking. The purpose of this paper is to explore the influence of homogeneous and heterogeneous environments on the scaling process of social initiatives.

A comparative qualitative study was conducted in the Dutch social sector, including 48 in-depth interviews with initiators and organizations operating in three different domains – a homogeneous, heterogeneous or mixed environment – in the public sector.

Homogeneous environments are less supportive of scaling social initiatives than heterogeneous environments, as sharing best practices and collaborating with less-familiar organizations is not common. Heterogeneous environments, however, do not provide coordination of the scaling process, with the risk that public funds are used less efficiently and effectively. Institutional environments with a balance of homogeneity and heterogeneity are best suited for scaling social initiatives.

By combining literature from different research areas and conducting extensive empirical research in different domains of the social sector, the authors' study provides a broad and nuanced picture and brings precision to the authors' understanding of the relationships between the institutional environment and scaling in the public sector.

This study aims to minimize the transportation-related cost in distribution while utilizing a heterogeneous fixed fleet to deliver distinct demand at different geographical locations with a proper workload balancing approach. An increased cost in distribution is a major problem for many companies due to the absence of efficient planning methods to overcome operational challenges in distinct distribution networks. The problem addressed in this study is to minimize the transportation-related cost in distribution while using a heterogeneous fixed fleet to deliver distinct demand at different geographical locations with a proper workload balancing approach which has not gained the adequate attention in the literature.

This study formulated the transportation problem as a vehicle routing problem with a heterogeneous fixed fleet and workload balancing, which is a combinatorial optimization problem of the NP-hard category. The model was solved using both the simulated annealing and a genetic algorithm (GA) adopting distinct local search operators. A greedy approach has been used in generating an initial solution for both algorithms. The paired t-test has been used in selecting the best algorithm. Through a number of scenarios, the baseline conditions of the problem were further tested investigating the alternative fleet compositions of the heterogeneous fleet. Results were analyzed using analysis of variance (ANOVA) and Hsu’s MCB methods to identify the best scenario.

The solutions generated by both algorithms were subjected to the t-test, and the results revealed that the GA outperformed in solution quality in planning a heterogeneous fleet for distribution with load balancing. Through a number of scenarios, the baseline conditions of the problem were further tested investigating the alternative fleet utilization with different compositions of the heterogeneous fleet. Results were analyzed using ANOVA and Hsu’s MCB method and found that removing the lowest capacities trucks enhances the average vehicle utilization with reduced travel distance.

The developed model has considered both planning of heterogeneous fleet and the requirement of work load balancing which are very common industry needs, however, have not been addressed adequately either individually or collectively in the literature. The adopted solution methodologies to solve the NP-hard distribution problem consist of metaheuristics, statistical analysis and scenario analysis are another significant contribution. The planning of distribution operations not only addresses operational-level decision, through a scenario analysis, but also strategic-level decision has also been considered.

The planning of distribution operations not only addresses operational-level decisions, but also strategic-level decisions conducting a scenario analysis.

This research aims to propose a model for the complex decision-making involved in the ecological restoration of mega-infrastructure (e.g. railway engineering). This model is based on multi-source heterogeneous data and will enable stakeholders to solve practical problems in decision-making processes and prevent delayed responses to the demand for ecological restoration.

Based on the principle of complexity degradation, this research collects and brings together multi-source heterogeneous data, including meteorological station data, remote sensing image data, railway engineering ecological risk text data and ecological restoration text data. Further, this research establishes an ecological restoration plan library to form input feature vectors. Random forest is used for classification decisions. The ecological restoration technologies and restoration plant species suitable for different regions are generated.

This research can effectively assist managers of mega-infrastructure projects in making ecological restoration decisions. The accuracy of the model reaches 0.83. Based on the natural environment and construction disturbances in different regions, this model can determine suitable types of trees, shrubs and herbs for planting, as well as the corresponding ecological restoration technologies needed.

Managers should pay attention to the multiple types of data generated in different stages of megaproject and identify the internal relationships between these multi-source heterogeneous data, which provides a decision-making basis for complex management decisions. The coupling between ecological restoration technologies and restoration plant species is also an important factor in improving the efficiency of ecological compensation.

Unlike previous studies, which have selected a typical section of a railway for specialized analysis, the complex decision-making model for ecological restoration proposed in this research has wider geographical applicability and can better meet the diverse ecological restoration needs of railway projects that span large regions.

Online collaboration in today's world is a topic of genuine interest to Internet researchers. The purpose of this paper is to explore the role of group knowledge heterogeneity (GKH) in open collaboration performance using the mediating mechanisms of group cognition (GC) and interaction to understand the determinants of the success of online open collaboration platforms.

Study findings are based on partial least squares structural equation modeling (PLS-SEM), the formal mediation test and moderating effect analysis from Wikipedia's 160 online open collaborative groups.

For online knowledge heterogeneous groups, open collaboration performance is mediated by both GC and collaborative interaction (COL). The mediating role of GC is weak, while the mediating role of COL is strengthened when knowledge complexity (KC) is higher. By dividing group interaction into COL and communicative interaction (COM), the authors also observed that COL is effective for online open collaboration, whereas COM is limited.

These findings suggest that for more heterogeneous large groups, group interaction would explain more variance in performance than GC, offering an in-depth understanding of the relationship between group heterogeneity and open collaboration performance, answering what determines the success of online open collaboration platforms as well as explaining the inconsistency in prior findings. In addition, this study expands the application of Interactive Team Cognition (ITC) theory to the online open collaboration context.

Considering the vital role of resource-constraint innovation in developing countries, the aim of the study is to examine the mechanism of internal and external heterogeneous knowledge sharing (HKS) in the relationship between sustainable leadership (SL) and frugal innovation (FI). The social exchange theory was used to develop a research framework.

This study employed Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis to examine the relationship among several latent factors based on 263 participants from Pakistani SMEs.

The current findings support the significant positive impact of SL on both internal and external HKS. Moreover, this study also confirms the mediating effect of both types of HKS in the relationship between SL and FI.

To delve further into the benefits and vital role of HKS, it is recommended to conduct further research that would examine the potential impact of heterogeneous knowledge sources on the “SL–FI relationship” and to apply the presented research methodology in other countries and organizations beyond Pakistani SMEs.

This study is one of the first documented attempts to demonstrate HKS as a mechanism in the relationship between a specific type of leadership and FI.

The purpose of this study is to explain the opinions of the Muslim jurists available in the fiqh books so that they may be compared with the approaches adopted by modern scholars for defining the concept of riba. It is argued that the method of jurists was different from the one adopted by the modern Muslim jurists and Islamic economists. The new method dichotomizes riba into those of the Quran and Sunnah. On the contrary, jurists of four Sunni schools considered the Quran and Sunnah in this regard as a single whole, and they saw Sunnah as the elaboration of riba. By explaining the similarities shared by different fiqh schools, it is explained that there is no need for a definition of riba.

The paper uses the method of content analysis. The authors have consulted the authentic fiqh manuals of the four Sunni fiqh schools to substantiate the objectives.

One of the major findings of this paper is that interest charged in loan transactions, including bank loans, is riba according to the four Sunni fiqh schools. Moreover, the paper also shows that the similarities among the four Sunni fiqh schools are far more significant than the often-highlighted disagreements among them regarding the concept of riba. The methodology adopted by modern Muslim scholars seems to add confusions around the concept of riba.

The paper discusses views of only four Sunni fiqh jurists.

The paper explains the common methodology followed by the jurists for understanding riba, the significant similarities resulting from their common method, the link between the concept of riba and different types of financial transactions within the framework of the jurists and that combining several fiqh schools at a time is a contradiction-ridden methodology.

This paper aims to evaluate Ghana's National Health Insurance Scheme (NHIS) on healthcare utilisation by exploring its heterogeneous effects based on residential status and wealth.

The study used the Ghana Socioeconomic Panel Survey (GSPS) datasets. An instrumental variable strategy, specifically the two-stage residual inclusion (2SRI), was employed to control endogenous NHIS membership.

Generally, the results show that NHIS improves healthcare utilisation (i.e. visits to a health facility and formal care). Concerning the heterogeneous effects of health insurance on healthcare utilisation, the results revealed that NHIS members are more likely to seek care, irrespective of their residence status. The results further indicate that the probability of visiting a health facility and utilising formal care increases for the poorest NHIS participants. Based on these, the authors conclude that NHIS provides equitable healthcare access and utilisation for its vulnerable populations, who are beneficiaries.

To the best of the authors' knowledge, this paper is the first to explore the heterogeneous effects of NHIS on healthcare utilisation across residential and income subpopulations. Splitting the dataset by residential status to examine healthcare utilisation inequality is worthwhile. In addition, analysing utilisation in terms of health care type would show whether Ghana's NHIS may be viewed as welfare-enhancing through increased formal health care utilisation.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-05-2023-0330

Company goals, behaviors and decision-making processes may differ depending on whether a given company decides to engage in the Industry 4.0 (I4.0) paradigm as a user or as a provider of I4.0-based solutions. This paper will consider this question in-depth by focusing on the extent to which the innovation ecosystem (IES) affects these two main strategies.

The authors' study aims to analyze the impact of a regional information and communication technology (ICT) ecosystem on the implementation of I4.0 at the company level by using an original survey of 123 companies that are already engaged in the implementation of I4.0 in the French region of Hauts-de-France and by employing two sets of logit models.

The authors' empirical study demonstrates the importance of an innovation ecosystem-based strategy for I4.0 technology provision. It reveals that engaging in R&D collaborative projects and developing links and complex relationships with heterogeneous actors within the IES positively affects the likelihood of a given firm deciding to implement I4.0 as a provider of I4.0-based solutions. This does not, however, affect the probability of a firm engaging in the I4.0 paradigm as a user of technologies and solutions.

From a policy standpoint, this study could encourage decision-makers to engage with I4.0 development by developing policies targeting the reinforcement of IES at a regional level. This could also help to accelerate the adoption of I4.0 technologies by manufacturing companies and foster the development of I4.0-based solutions through specific company-targeted policies. The authors' study supports the need for manufacturing company managers to evaluate and identify the best technological strategies related to the I4.0 paradigm that meet their specific needs.

The authors' study shows that the decision to engage in the I4.0 paradigm as providers of I4.0-based solutions is more likely to rely on an innovation ecosystem-based strategy, while the decision to engage in the I4.0 paradigm as users of I4.0-based solutions is more likely to rely on the company’s internal strategies, resources and demand-side benefits.

Due to their limited capacity for adaptation and dependence on natural resources for economic growth, developing countries (DCs) tend to be more prone to climate change. It is argued that climate finance (CF) is a significant financial innovation to mitigate the negative effects of climate variation. However, the heterogeneous impacts of CF on environmental sustainability (ES) and social welfare (SW) have been masked. Thus, this study aims to investigate the heterogeneous effects of CF on ES and SW in 80 CF receipt DCs from 2002 to 2018. This study also aims to investigate the effects of CF on ES and SW based on population size, income heterogeneity and the type of CF.

The method of moments quantile regression (MMQR) with fixed effects is utilized. Alternatively, the fully modified least square (FMOLS) and dynamic least square (DOLS) estimators are used for the robustness test.

The findings revealed that DCs with the lowest and middle quantiles of EF, carbon dioxide (CO₂) emissions and human development exhibit large beneficial impacts of CF on ES and SW. In contrast, the positive effects of CF on ES breakdown for countries with the largest distributions of EF and CO₂ emissions. Besides, the impacts of CF on ES and SW depend on income heterogeneity, population size and the type of CF.

This study calls for a framework to integrate CF into all economic development decisions to strengthen climate-resilient SW and ES in DCs.

To the best of the authors’ knowledge, this is the first study to investigate the effects of CF on ES and SW in a wide range of DCs. Thus, it complements existing related literature focusing on the effects of CF on ES and SW.