Explore the vast range of titles available.

From a framework of this study, modern society may be explained by a paradigm transformation from ascription to achievement and also from particularism to universalism. According to this hypothesis, Japanese university society has not developed successfully for more than the closed society to the opened society. This paper intends to deal with the Japanese differentiated university stratification, which has been formed among these value orientations by connection of particularism and ascription, with a focus on its effects on the academic productivity including both research and teaching productivity. For example, a closed academic structure with an inbreeding mechanism in tate-shakai (vertical society) has brought about a great deal of effect either positively or negatively on academic system throughout the country. We intend to discuss the reasons behind the declining symptom of academic productivity of the university on the basis of the given framework.(HRK / Abstract übernomen)

Many real matching markets are subject to distributional constraints. These constraints often take the form of restrictions on the numbers of agents on one side of the market matched to certain subsets on the other side. Real-life examples include restrictions on regions in medical matching, academic master's programs in graduate admission, and state-financed seats for college admission. Motivated by these markets, we study design of matching mechanisms under distributional constraints. We show that existing matching mechanisms suffer from inefficiency and instability, and propose a mechanism that is better in terms of efficiency, stability, and incentives while respecting the distributional constraints.

Firms routinely engage in relationship marketing (RM) efforts to improve their relationships with business partners, and extant research has documented the effectiveness of various RM strategies. According to the perspective proposed in this article, as customers migrate through different relationship states over time, not all RM strategies are equally effective, so it is possible to identify the most effective RM strategies given customers' states. The authors apply a multivariate hidden Markov model to a six-year longitudinal data set of 552 business-to-business relationships maintained by a Fortune 500 firm. The analysis identifies four latent buyer-seller relationship states, according to each customer's level of commitment, trust, dependence, and relational norms, and it parsimoniously captures customers' migration across relationship states through three positive (exploration, endowment, recovery) and two negative (neglect, betrayal) migration mechanisms. The most effective RM strategies across migration paths can help firms promote customer migration to higher performance states and prevent deterioration to poorer ones. A counterfactual elasticity analysis compares the relative importance of different migration strategies at various relationship stages. This research thus moves beyond extant RM literature by focusing on the differential effectiveness of RM strategies across relationship states, and it provides managerial guidance regarding efficient, dynamic resource allocations.

Online peer-to-peer lending (P2P lending) has emerged as an appealing new channel of financing in recent years. A fundamental but largely unanswered question in this nascent industry is the choice of market mechanisms, i.e., how the supply and demand of funds are matched, and the terms (price) at which transactions will occur. Two of the most popular mechanisms are auctions (where the “crowd” determines the price of the transaction through an auction process) and posted prices (where the platform determines the price). While P2P lending platforms typically use one or the other, there is little systematic research on the implications of such choices for market participants, transaction outcomes, and social welfare. We address this question both theoretically and empirically. We first develop a game-theoretic model that yields empirically testable hypotheses, taking into account the incentive of the platform. We then test these hypotheses by exploiting a regime change from auctions to posted prices on one of the largest P2P lending platforms. Consistent with our hypotheses, we find that under platform-mandated posted prices, loans are funded with higher probability, but the preset interest rates are higher than borrowers’ starting interest rates and contract interest rates in auctions. More important, all else equal, loans funded under posted prices are more likely to default, thereby undermining lenders’ returns on investment and their surplus. Although platform-mandated posted prices may be faster in originating loans, auctions that rely on the crowd to discover prices are not necessarily inferior in terms of overall social welfare. This paper was accepted by Chris Forman, information systems .

Building on the promise of open data, government agencies support a continuously growing number of open data initiatives that are driven mainly by expectations of unprecedented value generation from an underutilized resource. Although data, in general, have undoubtedly become an essential resource for the economy, it has remained largely unclear how, or even whether, open data repositories generate any significant value. We addressed this void with a study that examines how sustainable value is generated from open data. Subsequently, we developed a model that explains how open data generate sustainable value through two underlying mechanisms. The first, the information sharing mechanism, explicates how open data are beneficial to forging informational content that creates value for society through increased transparency and improved decision-making. The second, the market mechanism, explicates how open data are beneficial as a resource in products and services offered on the market, as well as how open data are used to make processes more efficient or to satisfy previously unmet needs. We tested and validated the model using PLS with secondary quantitative data from 76 countries. The study provides empirical support to the conjecture that openness of data as well as the digital governance and digital infrastructure in a country have a positive effect on the country's level of sustainable value. Overall, the study provides empirical evidence in favor of nurturing open data culture and insights about the conditions that support turning it into sustainable value for the benefit of citizens, business organizations, and society at large.

Energy storage and virtual power plant technologies have been developed and become important technical means to enhance power system stability and reduce real-time dispatching costs. In this study, the dispatching capability and dispatching cost characteristics of the virtual power plants are analysed firstly in detail, as well as the dispatching difficulties under the traditional market modes. Hence, virtual power plant real-time bidding package model and virtual auction-based real-time power market mechanism are proposed. Data-driven virtual power plant real-time packing method and bidding package model integrated virtual Vickrey–Clarke–Groves auction model are put forward. Finally, the feasibility and validity of the proposed mechanism and method are verified by case studies and result in analyses of the IEEE-30 bus test system with multiple virtual power plants, providing a scientific foundation and a practical solution to the real-time power market.

There is consensus in the marketing literature that market knowledge and cross-functional collaboration are two fundamental resources for successful product innovation. However, few studies examine the dimensions or characteristics of market knowledge and how and why these resources influence product innovation performance. Drawing on contingency theory and the knowledge-based view of the firm, the authors argue that knowledge integration mechanisms may account for the effects of market knowledge dimensions (i.e., breadth, depth, tacitness, and specificity) and cross-functional collaboration on product innovation performance. They find that market knowledge specificity and cross-functional collaboration affect product innovation performance through knowledge integration mechanisms. In contrast, whereas the effect of market knowledge depth is partially mediated, market knowledge breadth has a direct, unmediated effect on product innovation performance. A test of an alternative moderating perspective shows that the effects of market knowledge depth and cross-functional collaboration on product innovation are negatively moderated by knowledge integration mechanisms. By showing the differential effects of market knowledge dimensions on product innovation performance, the authors provide a more refined understanding of the interplay among market knowledge, its integration, and the firm's performance in product innovation. The authors also conclude that by overlooking the role of knowledge integration mechanisms, previous research may have provided an overly optimistic view of the value of cross-functional collaboration in product innovation.

This study proposes a multi-agent-based framework for Peer-to-Peer (P2P) power trading in a locality electricity market (LEM) for self-interested smart residential prosumers. In LEM, prosumers may sell (buy) their excess generation (demand) at a profitable market prices compared to utility prices to achieve a win–win outcome. In LEM, three agents namely locality electricity trading system (LETS), utility and prosumer act together to achieve P2P power trading in a day-ahead market. LETS computes the internal market prices employing any one of the market-clearing mechanisms and broadcasts it to the prosumers. Prosumers optimise the generation-demand schedule for the next day using residential energy management and trading system to achieve minimum electricity bill. The performance of the proposed framework is validated through different case studies on a residential locality with ten prosumers. The simulation is carried out using MATLAB parallel computation tool box and the load data is collected from the residential locality of National Institute of Technology Tiruchirappalli, India. It is evident from the simulation results that all the participants are economically benefited by P2P power trading. It is also found that the SDR mechanism in P2P outperforms and reduces the locality electricity bill by 27–68% under different operating conditions.

Electricity generation using distributed renewable energy systems is becoming increasingly common due to the significant increase in energy demand and the high operation of conventional power systems with fossil fuels. The introduction of distributed renewable energy systems in the electric grid is crucial for delivering future zero-emissions energy systems and is cost-effective for promoting and facilitating large-scale generation for prosumers. However, these deployments are forcing changes in traditional energy markets, with growing attention given to transactive energy networks that enable energy trading between prosumers and consumers for more significant benefits in the cluster mode. This change raises operational and market challenges. In recent years, extensive research has been conducted on developing different local energy market models that enable energy trading and provide the opportunity to minimize the operational costs of the distributed energy resources by promoting localized market management. Local energy markets provide a stepping stone toward fully transactive energy systems that bring adequate flexibility by reducing users’ demand and reflecting the energy price in the grid. Designing a stable regulatory framework for local electricity markets is one of the major concerns in the electricity market regulation policies for the efficient and reliable delivery of electric power, maximizing social welfare, and decreasing electric infrastructure expenditure. This depends on the changing needs of the power system, objectives, and constraints. Generally, the optimal design of the local market requires both short-term efficiencies in the optimal operation of the distributed energy resources and long-term efficiency investment for high quality. In this paper, a comprehensive literature review of the main layers of microgrids is introduced, highlighting the role of the market layer. Critical aspects of the energy market are systematically presented and discussed, including market design, market mechanism, market player, and pricing mechanism. We also intend to investigate the role and application of distributed ledger technologies in energy trading. In the end, we illuminate the mathematical foundation of objective functions, optimization approaches, and constraints in the energy market, along with a brief overview of the solver tools to formulate and solve the optimization problem.

The European electricity market model continues to evolve in the face of new challenges. This systematic literature review aims to assess the status of research and discussion on the current model and its market mechanisms. In addition, it aims to ascertain the kinds of modelling tools that have been used to model market mechanisms or formulate proposals for changes to current practice. The results show that the challenges of individual market mechanisms have been identified quite extensively in the research sample. However, the number of papers identified for inclusion in the systematic literature review was quite small, from which it can be concluded that there are surprisingly few publications focusing on this particular topic. Nevertheless, the trend indicates a probable increase in research in the subject area in the future. The general standpoint among researchers seems to be that the goals set by the EU are, as it were, a law of nature that cannot be deviated from. Consequently, new radical change proposals or comprehensive reforms were not encountered in the study. In addition, it was found that optimization was the most commonly used modelling method in the papers included in the literature review.