Abstract

Abstract
The rapid expansion of mobile broadband networks and the proliferation of Internet of Things (IoT) applications have substantially increased data transmission and processing demands. However, the application domains of IoT-enabled models often face resource limitations, requiring rapid responses, low latency, and large bandwidth, surpassing their inherent capabilities. To address these challenges, we propose a fishnet approach-based packet scheduling and resource allocation system, termed Fishnet-6G, to optimize network resource allocation in the proposed 6G networks. Initially, we constructed a Sierpinski Triangle-based network in a 6G-IoT environment, enhancing device connectivity. We utilize the Quantum Density Peak Clustering (QDPC) algorithm to perform clustering for IoT devices, establishing Cluster Head (CH) and Substitute CH (SUB CH) based on actual metrics. Furthermore, traffic prediction is achieved through two processes, grouping, and fair queue status, using the Improved Deep Deterministic Policy Gradient (IMPDDPG) algorithm with a variable sampling rate, resulting in well-organized packet scheduling. Subsequently, we perform optimal packet scheduling by employing the Willow Catkin Optimization (WCO) algorithm, and the scheduled packets are managed within a Fishing Net Topology to reduce energy consumption and system complexity. Finally, we allocate the scheduled packets to the desired resource blocks using the Bayesian Game-Theoretic Approach (BGTA). The proposed approach is implemented using Network Simulator-3.26, and the performance of the Fishnet-6G model is evaluated based on time, transmission rate, energy efficiency, average throughput, latency, and Packet loss rate. Numerical analysis demonstrates that Fishnet-6G outperforms existing approaches across these metrics, showcasing its effectiveness in addressing the challenges of 6G-IoT networks.

Introduction

The rapid expansion of IoT devices has led to notable progress in data generation, contributing significantly to the growth of linked objects inside the IoT systems. It is essential to highlight that this development is experiencing a sustained increase as many objects and devices are interconnected in IoT applications [1,2]. As 5G networks continue to improve and expand, concerns arise about their capacity to support the constantly evolving needs of emerging IoT applications fully [3]. The impending era of sixth-generation (6G) mobile communication is marked by the widespread adoption of IoT devices and cellular networks, contributing to a substantial increase in energy consumption and network traffic [4]. The ever-growing demands of intelligent and self-sufficient systems challenge their capabilities and spark the evolution towards the 6G-IoT vision [5]. By 2025, it is projected that there will be over 25 billion IoT devices, putting immense pressure on current multiple access techniques and necessitating the development of B5G wireless systems. In light of this uncertainty, attention has turned towards the potential of 6G wireless technologies to overcome these limitations and propel the transformation of existing networks [6]. Envisioned as a vital component in the development of sustainable smart cities, the 6G-IoT vision incorporates cutting-edge elements such as intelligent edge computing, health analytics, and multidimensional design technology, with a firm emphasis on critical features such as scalability, wireless multi-access, personalized AI, machine learning, cyber security, blockchain, and augmented sensing [3,[6], [7], [8], [9]]. The strategic placement of small cells in a 6G system optimizes coverage and data transmission efficiency, while sensor nodes collect essential information communicated to IoT devices, enabling diverse applications like home automation and healthcare, showcasing the network’s sophistication [10,11]. Network architecture and administration must mitigate complexities in the 6G network to provide exemplary performance. The construction of a 6G network presents numerous advantages, encompassing enhanced capacity, extensive coverage, cost-effectiveness, load balancing, and energy efficiency [12].

Conclusion

This study proposes a fishnet technique to overcome the significant issues related to packet scheduling and resource allocation in the context of the 6G-IoT environment. The primary objective of this study is to implement the Sierpinski triangle-based network construction in a 6G-IoT environment to mitigate the issues of excessive energy consumption and communication overhead. The edge server utilizes QDPC to provide clustering for IoT devices. The suggested study aims to group IoT devices based on relevant metrics. This clustering process involves the identification of cluster heads and sub-cluster heads, which are executed by an edge server. Subsequently, the process of traffic prediction is conducted through two distinct stages, namely grouping and fair queue status assessment, employing the IMPDDPG algorithm with variable sampling rate. Packet scheduling is executed by considering the relevant metrics and employing the WCO algorithm. Following the establishment of a schedule, the management of packets is conducted inside the fishing net architecture, resulting in a reduction in energy consumption, complexity, and overall process weight. The allocation of scheduled packets to the requested resource blocks is accomplished using BGTA, while taking into consideration the operational indicators. The proposed approach was tested using Network Simulator-3.26, and its effectiveness was assessed by comparing its performance to existing methods taking into account various permanence metrics, including time, transmission rate, energy efficiency, average throughput, latency, and packet loss rate. The efficacy of our methodology is examined by quantitative analysis, which substantiates that our technique surpasses existing methodologies across all metrics. The future work will consider cases high availability in cases of Base Station failures and massive IoT V2X environment to support the next generation of wireless networks.

Abstract

     We study the inter-temporally optimal innovation strategies of incumbent manufacturing firms that compete in an established market and can extend their product line through product innovation. Firms invest in production capacity and R&D knowledge stock, where the R&D knowledge stock and the current R&D investment determine the hazard rate of innovation. Our findings show that the firms’ optimal R&D strategies are driven by a subtle interplay between the relative positions of their R&D knowledge stocks and their current relative positions on the established market. First, we find that under symmetric investment costs the knowledge leader should spend more on R&D than the knowledge laggard only if it has a substantially smaller market share on the established market. If the knowledge leader’s market share is sufficiently large, its optimal investment in R&D is so small that its innovation rate is lower than the knowledge laggard’s. Second, optimal investment in R&D knowledge is negatively affected by the opponent’s production capacity on the established market if the competitor has not innovated yet. However, we find that this effect is reversed after the competitor has successfully introduced the new product on the submarket. Third, the manufacturing firm with higher costs of adjusting production capacity for the established product has a higher incentive to engage in product innovation and might even achieve a higher total discounted profit than its more efficient competitor.

Introduction

     A considerable fraction of product innovations in related submarkets is accomplished by established incumbents (e.g. Buensdorf, 2016, Chandy, Tellis, 2000, Franco, Sarkar, Agarwal, Echambadi, 2009, King, Tucci, 2002, Sood, Tellis, 2011). This observation raises the question how an incumbent’s optimal strategy in an R&D race depends on its strength on its established market. To illustrate, in 2010 when Apple and Samsung introduced Tablet PCs and thus created a new submarket that coexisted with the established market of portable computers, they had relatively small market shares on the Laptop market (3.4% and 2.8% respectively) compared to Hewlett Packard and Dell (18% and 12% respectively). Interestingly, HP and Dell entered the Tablet market much later in 2013.1 In the market for smartphones, Nokia, as the clear market leader in the early 2000s (market share 2005: 32.5%), introduced its first touchscreen phone in 2011, while its initially smaller competitor Samsung (market share 2005: 12.7%) introduced its first smartphone with a touchscreen in 2008. As a result, Samsung achieved a higher market share in 2012 and also exhibited strongly positive dynamics of profit in the smartphone market compared to Nokia.

Conclusions

     In real-world markets, action-reaction or increasing dominance patterns of innovation can be observed. Under action-reaction, the smaller incumbent manufacturing firm in the established market becomes the innovation leader in the new market. Under increasing dominance, the dominant incumbent manufacturing firm in the established market also dominates the new market. In this paper, we develop a stochastic duopoly framework in order to highlight some of the drivers that endogenously lead to one of these patterns of innovation in equilibrium. In particular, we study the factors that determine the incentives of incumbent firms to invest in the development of a new product which extends their product range. Our dynamic setting particularly emphasizes the interplay between the firms’ relative positions in terms of their R&D knowledge stocks and their relative strengths on the market for the established product. We explicitly take into account that the adjustment of production capacities as well as the build-up of R&D knowledge are costly and take time.

Abstract

     This research takes a novel approach to analyzing impact and benefit agreements (IBA) using multiple-criteria decision making (MCDM) and game theory. Local communities, which are often Indigenous communities, face with difficult decisions regarding the trade-offs of impacts vs. benefits from mineral resource development. Analyses of IBAs typically focus on their economic benefits but fail to consider environmental, socio-cultural, and other sustainability criteria. By not considering these criteria, current methods struggle to predict if an IBA is adequate or if it will be accepted. This research develops a model with MCDM that balances complex sustainability trade-offs for communities during mineral development negotiations. Bargaining positions of companies or impacted communities are also an essential, yet understudied factor in IBA analyses. Game theory is employed to show how bargaining positions can affect the compensation included in an IBA. In all, this research develops a model that can consider different criteria, value systems, and the implications of cooperation or competition to predict if an IBA will be accepted. This study provides recommendations, which can be applied other resource development projects which impact communities. The model shows the importance of flexibility in design, power dynamics in bargaining, cooperation, and knowledge sharing.

Introduction

     The mining industry is at a crossroads. Society’s growing demand for minerals continues to push for more mines, but many projects struggle to secure permits due to opposition from impacted Indigenous communities and other stakeholders. The continued growth of the mining industry impacts communities on countless socio-environmental levels, which are not easily accounted for during project evaluation. Impact and benefit agreements (IBAs) were developed to create collaborative solutions to balance the impacts and benefits of mineral development for communities. IBAs are contracts between impacted groups and project proponents that typically outline the benefits the community will receive, in terms of compensation and economic opportunities, as well as the strategies for impact mitigation (Gibson and O’Faircheallaigh, 2015). IBAs formalize a company-community partnership with the goal of reducing project delays and disruptions from protests, blockades, or legal opposition (Ali, 2003). The issue is that it is not well understood what exactly should be included in IBAs to balance the impacts vs. benefits of a project (Cascadden et al., 2021). There are several guidelines created for IBAs, e.g., Gibson and O’Faircheallaigh (2015), but there is a critical lack of formalized methods that can incorporate community values and predict if IBAs will be accepted and successful. The main questions of this research are: How can we predict if an IBA will be accepted? What are the main factors for a successful IBA? How can IBAs be better aligned to the wants, needs, and values of communities? And, what are the sustainability challenges with IBAs for mineral development projects? To better understand these questions, this research’s goal is to create a new method to analyze IBAs.

Conclusion and recommendations

     To summarize, the following list provides several key recommendations uncovered from this research for individuals looking to develop IBAs or predict if it will be accepted:

A project which provides alternatives which can trade-off between all important criteria, can make a project more likely to be accepted.

Before negotiations, bargaining factors should be analyzed to understand which group will potentially be at an advantage. The main factors–patience, knowledge, uncertainty, and power to make proposals–should be discussed and mediated.

Mineral policies should require proponents to provide flexible alternatives for all important criteria to communities.

1- Introduction

2- Literature review

3- Variables and assumptions

4- Proposed model

5- Solution and analysis

6- Conclusion

References

Abstract

This research focuses on the problems that enterprises are affected by multiple stakeholders in the process of lean management. Based on the dynamic game theory, through the construction of a three-stage game model, the article has researched on the interaction mechanism between internal and external stakeholders that affects the implementation of enterprise lean management. Through the research, the following conclusions are drawn: (1) At the beginning of lean management, hiring the third-party consultant experts to train employees, which is conducive to improving management efficiency; (2) the effort level of middle managers has nothing to do with the control of senior managers; (3) senior managers should not be too many constraints on third-party consultant experts; (4) senior managers should strive to coordinate the interests of all parties. The purpose of the paper is aimed to provide a reference of theory and practice of the implementation of lean management for enterprises.

Introduction

Lean management is derived from lean production, a type of production management based on the prototype of Toyota production system proposed by experts and scholars, and organized in 1990 at the Massachusetts Institute of Technology after more than 90 automobile factories were investigated and a comparative analysis was carried out around the world involving 17 countries in the International Motor Vehicle Program (IMVP). Womack et al., who put in place the guiding principles of lean manufacturing at the corporate strategy level, proposed the concept of lean thinking, and use it as the foundation of lean management, which applies lean thinking to enterprise management activities (Taylor, Taylor, & Mcsweeney, 2013). Lean management takes “full participation, continuous improvement, [eliminating] waste, [creating] value and [stimulating] positive energy” as its core. Its remarkable innovation performance has demonstrated its validity and advantages, and, led by the manufacturing enterprises, has changed the trajectory of the development of enterprises over the past few decades (Jasti & Kodali, 2015). A large number of successful cases of lean management practice show that lean management plays an important role in enhancing corporate governance innovation. Lean management can, that is, help enterprises to improve their levels of management innovation Jing, Niu, & Chang, 2015). However, currently, the effects of the implementation of lean management are not ideal. Baker (2002) conducts an effect survey of enterprises implementing lean management in the United Kingdom and Australia, and shows that less than 10% of enterprises achieve a higher lean level. In an identical survey conducted in the US, only 26% of those companies implementing lean management obtain better lean implementation effects (Blanchard, 2007; Comm & Mathaisel, 2005). The lean management implementation status of enterprises is even less desirable in China; many enterprises see results after long periods of time, but it is difficult to achieve lean management in the continuing operations of enterprises and some enterprises even appear to exhibit the “retrograde step”, in which many aspects of production and operations return to the state prior to the introduction of the lean management model. There are two primary reasons why the effects of implementing lean management have not been significant: (1) knowledge of lean management is scarce, and (2) not enough staff members participate in lean management implementation.

Circular economy principles aim to contribute towards sustainability and resilience through several simultaneous agendas including economic growth, social development and environmental responsibility. Stakeholders from each perspective have their own interests and priorities, which often result in conflict. There are several and varied methodologies which address the decision-making process, however in engineering spheres these techniques are usually limited to optimising resources, time or costs. Decisions that are comprehensive in scope and integrated across all affected systems are required to transition towards a circular economy, effective cross-disciplinary thinking is imperative and cooperation amongst diverse areas is essential. Game theory is a useful technique when analysing the interactions of stakeholders with multiple objectives and perspectives. This paper aims to critically review methodological approaches used in waste management practice and provide a guidance on how game theory differs from, and is complementary to, the primary decision-making tools available where cooperation is a feature too often missing. This review seeks to justify the development of game theory to complement waste management decision-making methods in civil engineering, where resource consumption and waste management is often voluminous. An application of game theory to a waste management example illustrates that this methodological approach is of complementary value. The contribution of this study to circular economy and solid waste agendas is to emphasise the capability of game theory to help facilitate conflict resolution, competition, and stakeholder consensus when capturing multiple (sometimes conflicting) values in line with circular economy principles.

Introduction

Population growth, and the concomitant increase in the demand for goods and services, is resulting in the progressive depletion of energy and resources stocks around the globe. Since the planet is experiencing a growing scarcity in resources and is becoming rich in waste, the concept of a ‘Circular Economy (CE)’ has emerged from increasing concerns over resource use efficiency, waste management and materials security (Rogers et al., 2017). CE principles aim to contribute towards sustainability and resilience through several simultaneous agendas, including economic growth, social development and environmental responsibility. Contributing towards CE transition (and certainly sustainability) implies capturing multiple types of value (Rogers, 2018). Conflict is expected to arise when stakeholder groups from different perspectives have their own interests and priorities. An integrative Decision-Making (DM) process should be able to overcome such barriers to cooperation; this is where Game Theory (GT) presents a promising potential to facilitate such an enabler to the transition towards a CE. The aim of this paper is to critically review different techniques in the CE literature. The review provides guidance on how GT differs from, is both complementary to and able to enhance, the primary DM tools available within a waste management context in civil engineering.

Data sharing techniques have progressively drawn increasing attention as a means of significantly reducing repetitive work. However, in the process of data sharing, the challenges regarding formation of mutual-trust relationships and increasing the level of user participation are yet to be solved. The existing solution is to use a third party as a trust organization for data sharing, but there is no dynamic incentive mechanism for data sharing with a large number of users. Blockchain 2.0 with smart contract has the natural advantage of being able to enable trust and automated transactions between a large number of users. This paper proposes a data sharing incentive model based on evolutionary game theory using blockchain with smart contract. The smart contract mechanism can dynamically control the excitation parameters and continuously encourages users to participate in data sharing.

Introduction

In the era of big data, digital resources have grown exponentially, and data has become an important strategic resource. At present, the big data industry faces a dilemma referred to as the problem of “data islands”. An effective solution to this problem would be to establish a reasonable and effective data sharing model [1]. Today, information disclosure and open sharing of data have become general trends in scientific, and even national, innovation. Numerous studies have shown that data sharing and reuse are conducive to accelerating the dissemination of data resources, by improving the efficiency and quality of work, and enhancing the potential for innovation [2–5]. Although data sharing has varying applications, it provides significant convenience in daily life. However, in the process of data sharing, there are still three problems to be solved: unwillingness to share, fear of sharing, and inability to share [6]. The unwillingness of users to share data is affected by the formation of mutual-trust relationships and the economic utility of data sharing. However, little research has been conducted on data sharing in the big data era and with the support of blockchain technology. In this paper, we perform an evolutionary game equilibrium analysis of data sharing in the era of big data, and explore and analyze the evolutionary game process of data sharing. Finally, we obtain four constraints with which to design an adaptive smart contract mechanism that can be used to motivate more users to participate in data sharing.

1- Introduction

2- Game Theory

3- Research Method

4- Results

5- Synthesis of Best Practice

6- Discussion

7- Conclusions

References

Abstract

Systems-of-systems (SoS) are becoming increasingly common in more and more domains, spreading from the initial focus on government-controlled areas such as defense to open market industries. This implies that collaborative SoS are becoming more important, where the constituents need to be given incentives to join and remain within the SoS. Game theory has been proposed as a framework to model and analyze such SoS mechanisms. It aims at providing incentives to the independently operated and managed constituents. This paper presents a systematic literature review on the applications of game theory to SoS engineering, together with a synthesis aiming at capturing the best practices for doing such an analysis. The main conclusions are that game theory can be applied to SoS in a wide range of application areas, and deal with problems related to acquisition, design, and operations. In particular, the operational formation of SoS are well suited for this kind of analysis, and it often requires the use of simulation techniques. However, most results in the field lack a validation in practice.

Conclusions

This paper has provided a systematic literature review in the area of GT applied to SoSE, and this contributes a starting point for researchers wishing to delve deeper into the area. It has also synthesized a best practice process for applying GT to more complex and realistic SoS problems, and this contribution is more towards practitioners. The main conclusions are that GT can be fruitfully applied to SoS in a wide range of application areas, and deal with various problems related to acquisition, design, and operations. In particular, problems related to the operational formation and dissolution of SoS are well suited for this kind of analysis. Due to the nature of SoS, that involve a large number of heterogeneous constituent systems that have a wide range of possible actions at their disposal, analytic techniques for evaluation do not suffice, but instead simulations are necessary. In particular, agent-based simulations are frequently used in the analysis. Unfortunately, the reported results from applying GT are not validated in practice in the literature, and there is still some way to go to properly ascertain the value of this as a practical SoSE technique. As for future research, a few areas would benefit from attention. One is to lower the threshold of experimenting with modeling and simulation. This could require better tools, but it could also take the form of libraries for those who wish to retain the flexibility of ordinary programming languages. On the theoretical side, deeper investigations into mechanism design as a technique of synthesizing the rules of an SoS would be highly interesting, as would the application of meta-game theory to the engineering processes that result in the SoS.

1- Introduction

2- Literature review

3- Model description and setup

4- The overall game: sequence of decisions and assumptions

5- Results: calibrations of trade-offs

6- Discussion and implications

7- Limitations and future research

References

Abstract

The consolidation of retailers across markets has considerably altered the competitive dynamics between leading brand manufacturers and retailers. The era in which brand manufacturers dictate the game to compliant retailers is long gone. Nowadays, with more equal negotiation power retailers are no longer just channel partners but rather business partners with whom to build business-to-business relationships. This has become apparent especially since retailers have developed their own private label brands (PLB) and actively seek brand manufacturers to supply them. For brand manufacturers supplying PLB may bring potential benefits but may also harm profits. Thus, this research investigates conditions under which a leading brand manufacturer would be better or worse off in terms of profitability producing PLB for retailers. Using a game theoretic model, we calibrate the trade-offs between the shelf space devoted by the retailer to the manufacturer brand and the amount of profit required from supplying the PLB necessary to counteract cannibalization and to generate profits for the manufacturer, under different levels of uncertainty regarding the availability of alternative suppliers. Calibrating these trade-offs provides brand manufacturers clear guidelines for negotiations with retailers regarding shelf space allocation and wholesale prices to be profitable supplying PLB.

Introduction

The growth and consolidation of retailers across a large number of countries has reshaped the retail scenario and significantly altered the competitive dynamics between retailers and brand manufacturers. For many years, retailers were spectators in a market dominated by powerful brand manufacturers. Retail fragmentation and media concentration contributed to brand manufacturer’s growth (Kumar & Steenkamp, 2007). Today the situation has changed considerably. The process of consolidation of the retailing system has reinforced retailers’ competitive position (AIM, 2016) leading to an increase in their bargaining power with brand manufacturers, enabling retailers to control a number of key business decisions related to logistics, marketing and sales (Pauwels & Srinivasan, 2004; terBraak, Deleersnyder, Geyskens, & Dekimpe, 2013). For example, retailers strongly influence decisions about product assortments, shelf space allocation, shelf positioning, and the number and typologies of brands to be offered. This consolidation represents an important shift in the relationship between brand manufacturers and retailers (Sutton-Brady, Taylor, & Kamvounias, 2017). Retailers are no longer channel partners but rather business partners with whom to build business-to-business relationships. A key consequence from the consolidation of retailers is the development of their own brands. Relying on their bargaining power and scale, retailers have successfully developed private label brands (PLB) as a core strategy (Kim, Jung, & Park, 2015), becoming direct competitors to manufacturer brands (Pauwels & Srinivasan, 2004). The level and extent of PLB penetration across countries, industries and product categories presents a significant challenge to brand manufacturers. Specifically, they are present in more than 90% of consumer-packaged goods categories (Euromonitor, 2010; terBraak, Deleersnyder, et al., 2013). Market shares in the United States and across Europe have reached on average 16.7% (Nielsen, 2018) and 17.1%, (IRI, 2016), respectively, but enjoy higher shares in countries such as Spain (52%), Switzerland (50%), UK (46%), Germany (45%) and France (32%) (PLMA, 2017).

This study investigates the strategic effect of return policies in a dual-channel supply chain, in which a manufacturer can sell products directly to end customers and indirectly via an independent retailer. The manufacturer decides whether to implement a return policy in either the direct or indirect channel, or in both channels. When the return policy is provided, the cost of returned products has to be covered by the corresponding channel. We consider four possible strategies, including full refund in the direct channel only, full refund in the indirect channel only, full refund in both channels, and no refund in both channels. Given the equilibrium pricing strategies of firms, the preferences of firms over different strategies are determined by comparisons between the anticipated return rate of customers and their perceived value of the return policy. The manufacturer prefers the full-refund policy in both channels when the return rate of the customer is low. Otherwise, the dominant strategy for the manufacturer is the no-refund policy in both channels. The retailer benefits more from the full-refund policy in the indirect channel alone when the return rate is low; otherwise, the retailer prefers the full-refund policy in the direct channel only.

Introduction

Customers in numerous industries have a legal right to return their purchased products to original manufacturers or retailers for any reason (Mostard and Teunter, 2006). In 2007, the U.S. electronics industry spent approximately $13.8 billion on returned products, in which only approximately 5% were defective (Lawton, 2008). According to Jim Brill, the reverse logistics marketing manager for UPS, the expense of returns processing ranges from 20% to 65% of the cost of goods sold1 . This phenomenon is even more prevalent in online shopping because e-commerce products are usually characterized by substantial numbers and high return rates (Choi et al., 2004; Disney et al., 2004; Cho et al., 2008; Yang et al., 2015). Based on an Invesp infographic of online return rates, online shoppers return at least 30% of their purchases, whereas in physical stores less than 9% return their purchases2 . The preceding discussion explicitly shows that product return is no longer a small part of the business process. Numerous scholars (Chen and Bell, 2011; Chen and Grewal, 2013; Chen and Bell, 2013) also indicate that offering a return experience should be viewed as an important weapon for a firm to build a competitive advantage in the marketplace. Although a substantial body of literature discusses the manner in which return policies affect the profits of channel members (Chen and Bell, 2011; Chen and Bell, 2013), most studies focus on a traditional supply chain setting where manufacturers sell products via retailers exclusively. Nonetheless, an increasing number of manufacturers now have an attempt to sell to customers via retailers and e-commerce. For example, Dell, which is known for its success in the direct-selling mode, announced that it would build an indirect selling channel to take the advantage of both channels and to stimulate sales.

Business practices have demonstrated that a contract manufacturer (CM) can introduce an own-label product and thus compete with its original equipment manufacturer (OEM), i.e., factory encroachment, which has not been obtained much attention in literature. Considering a three-level outsourced supply chain consisting of a CM, an OEM, and a retailer, this paper analyzes the impact of factory encroachment on players’ gains. We show that factory encroachment could implement Pareto improvement, i.e., all supply-chain players’ gains increase under encroachment. We also demonstrate that factory encroachment always offers more surplus to the entire supply chain and the consumer. In addition, the most preferred channel for the supply-chain players, the entire supply-chain system, and the consumer are investigated. We find that an encroachment strategy could be simultaneously favored by all involved parties, provided there is no integration between the OEM and the retailer. However, if the OEM and the retailer act as a single entity, only the no-encroachment strategy could be favored by all parties simultaneously.

Introduction

As outsourcing becomes more persuasive in business practices, many contract manufacturers (CMs) start to encroach on original equipment manufacturers’ (OEMs) territories and produce and sell its own-label products that compete with its customer OEMs’ brand. Recently, for example, a project titled ‘China Quality Manufacturing (CQM)’, which is developed by the giant electronic retailer TAOBAO, aims to push tens thousands of Chinese CMs (factories) onto online platforms for selling its own-label products (Alibaba, 2016). Thus, in the following years, the involved factories (CMs) will serve both offline customer firms (OEMs) and online consumers. It indicates that CMs’ encroachment could emerge as an important business model in O2O outsourcing practices. CMs’ managers may face two strategic decisions with regard to producing and selling own-label products. The first one is the strategic decision on introducing own-label products; and the other one is which market a CM should enter. Two markets that a CM can encroach on are obvious: the wholesale market vs. the retail market. For example, some CMs involved in the CQM project sell their products through an online wholesale market (see http://www.1688.com); while the other enterprises establish web shops on a direct-to-consumer platform (see http://q.taobao.com). These practices imply that the CMs should simultaneously satisfy the offline demand incurred by its customer firms (OEMs) and the online demand incurred by consumer or online retailers. Thus, traditional CMs with single offline channel are involved in strategic decisions of multi-channel operations.

This study investigates the optimal level of transfer prices chosen by managers in a divisionalized firm when they are evaluated based on a balanced scorecard. A unique assumption of our model is that transfer prices are unobservable to a competing firm’s managers. In contrast to the findings in several studies that examine strategic transfer pricing, this research shows that a manager who is evaluated using a balanced scorecard chooses a transfer price that exceeds marginal cost given a market competitor in a specific economic environment. This result is caused mainly by our model’s assumption that a manager considers the competitor’s profit in his/her in decision-making when the objective is to maximize long-term profit. This study makes a significant contribution to the strategic transfer pricing literature by showing that even if the transfer price is unobservable to rivals, the optimal transfer price exceeds marginal cost when the final product market is characterized by price competition, something not shown in previous analytical accounting research.

Introduction

Transfer prices have become a critically important issue for multidivisional firms. For example, General Motors and Panasonic use effective transfer prices to optimize their divisional operations and profits based on the principle that transfer prices allow managers to evaluate a manufacturing division’s performance as a profit center. While multidivisional firms attempt to effectively and accurately evaluate divisional performance using transfer pricing, it is often difficult to determine the optimal transfer price in a firm composed of multiple divisions. Because firms are generally interested in determining the optimal transfer price in a specific economic environment, transfer pricing is a practical and important issue in contemporary management accounting. Further, methods of optimal transfer pricing have been explored and proposed in empirical research from a management accounting perspective because selecting the optimal transfer price can improve a firm’s profit (e.g., Chan & Lo, 2004; Tang, 1992). Economic analysis of transfer pricing from a managerial viewpoint dates back to Hirshleifer (1956), who advocates that an internal transfer price equal to marginal cost alleviates any attendant double marginalization problem. Since Hirshleifer’s (1956) work, other management accounting studies have analyzed optimal transfer prices using market competition models (e.g., Alles & Datar, 1998; Arya & Mittendorf, 2007; Autrey & Bova, 2012; Fjell & Foros, 2008; Göx, 2000; Hamamura, 2018; Matsui, 2011, 2012, 2013; Narayanan & Smith, 2000; Schjelderup & Sørgard, 1997; Shor & Chen, 2009). Most studies in this research stream examine which cost-based transfer price, other than marginal cost, is desirable for a firm (e.g., Alles & Datar, 1998; Matsui, 2011, 2013). Tang (1992) provides important empirical evidence on transfer pricing practices, specifically the relationship between transfer prices and the use of a cost accounting system. Tang (1992) describes only a few situations where firms choose marginal cost to set transfer prices, which is unlike the theoretical insight gained in Hirshleifer (1956).

This paper examines cooperative advertising strategies in O2O supply chain made of a seller and an online platform agent. The seller sells a product to end market through both offline and online channels, the latter operated by the agent. We consider cooperative advertising strategies for this O2O supply chain and investigate three models, namely, Integration Model (I-Model), Unilateral co-op advertising model (U-Model), and Bilateral co-op advertising model (B-Model). We derive the optimal decisions of advertisement levels and participation rates between the supply chain members, and explore how they are linked to the interrelationship between the O2O channels and other system parameters. In addition, we provide comparison results among the three models and find that B-model can lead to significant benefits to the seller and the entire channel compared to U-model, especially under a high online profit share for the agent. However, the agent is undermined under B-model as he is obliged to share a portion of offline advertisement expenditure without corresponding compensations, which explains the difficulty in adopting bilateral advertising cooperation in real practice.

Introduction

Statistics show that 40 percent of worldwide internet users have bought products or goods online via desktop, mobile, tablet or other online devices. This amounts to more than 1 billion online buyers and is projected to continuously grow (Statistics.com, 2016). The transformative power of the internet continues to revolutionize industry, with new and better ways of doing business emerging on a daily basis. One such, O2O (Online to Offline), is making a splash in China and also flourishing globally as an innovative business mode. To simply put, O2O provides information, services, and booking discount to Internet users, who in return will be converted into the customers of the particular offline business partners. This business mode is particularly suitable to consumer goods and services, such as food and beverage, fitness, movies and beauty salon. A typical example is Meituan-Dianping, the largest group deals site in China, selling online vouchers of a wide range of services/entertainment products with the cooperation of providers conducting the services offline. In addition, many firms initiate the consolidation of their supply chains by developing online trading platforms and offline experience shops. For example, SAIC, an automobile manufacturing group in China, implements its O2O business model by integrating 4S shops with the website, www.chexiang.com, an online platform used to communicate with his consumers and facilitate e-payments. In western countries, O2O has not only been evolved by groupbuying websites like Groupon and LivingSocial but also traditional brick and mortar retailers like Nordstrom and Walmart.

Game theory provides valuable tools to examine expert multi-agent systems. In a cooperative game, collaboration among agents leads to better outcomes. The most important solution for such games is the Shapley value, that coincides with the expected marginal contribution assuming equiprobability. This assumption is not plausible when externalities are present in an expert system. Generalizing the concept of marginal contributions, we propose a new family of Shapley values for situations with externalities. The properties of the Shapley value offer a rationale for its application. This family of values is characterized by extensions of Shapley’s axioms: efficiency, additivity, symmetry, and the null player property. The first three axioms have widely accepted generalizations to the framework of games with externalities. However, different concepts of null players have been proposed in the literature and we contribute to this debate with a new one. The null player property that we use is weaker than the others. Finally, we present one particular value of the family, new in the literature, and characterize it by two additional properties.

Introduction

There are many successful applications of game theoretical tools to study expert or intelligent multi-agent problems (see for instance Parsons and Wooldridge, 2002; Pendharkar, 2012). The classic model of games with transferable utility has been thoroughly studied and today it is a theory with solid foundations. It has been widely applied to economic, social, or political problems binding the gap between these fields and mathematics. In particular, it has endowed social sciences with a formal framework in which meaningful statements can be done. One of the main research questions is how to distribute the gains obtained by a given group of agents. In this regard, the Shapley value (Shapley, 1953) is probably the most popular solution and has been used to study a variety of expert systems (Alonso-Meijide and Carreras, 2011; Torkaman et al., 2011). It is defined as the average contribution of a player to its predecessors in a permutation and supported by appealing axiomatic characterizations. The characterizations provide a normative foundation of the value and play an important role in its applications. Most of the contributions in the literature overlook a key fact in today’s globally interconnected societies, decisions within a group of agents can affect the outcomes of other groups of agents. Thrall and Lucas (1963) devised the partition function to incorporate coalitional externalities to classic cooperative games.