FAIRNESS IN SUPPLY CHAIN COLLABORATION WITH BARGAINING

Transcription

1 AARHUS UNIVERSITY DEPARTMENT OF BUSINESS STUDIES, BUSINESS AND SOCIAL SCIENCES SEPTEMBER 2013 FAIRNESS IN SUPPLY CHAIN COLLABORATION WITH BARGAINING A LABORATORY STUDY SUPERVISOR: JENNY LI HONGYAN AUTHOR: STEPHANIE HAMMERL

2 Title... 4 Abstract Introduction Literature Review Supply Chain Collaboration Experimentation Evaluation of Experimentation Advantages Disadvantages Validity of Experimentation Internal Validity External Validity Behavioral Operations Management Origin of BOM Studies in the field Research Problem Description Analytical Background Economics Background Literature Review on Fairness Negotiation Literature Review on Negotiation Research Questions and Hypotheses Research Questions Hypotheses Experimental Design and Implementation The Design of Experiments Principles of the Design Implementation of the Experiment Experiment Experiment Experiment Experimental Results Page 2 of 69

3 5.1 Wholesale Price Order Quantity Order Quantity without the extreme low observation Declining Offers Fairness Perception Fairness Perception vs. Wholesale Price Fairness Perception vs. Order Quantity Profit Centralized Solution Results vs. Hypotheses Conclusion Managerial Impact Mayor Contribution Limitation Literature Abbreviations Figures Tables Appendix Page 3 of 69

4 Title Fairness in Supply Chain Collaboration with Bargaining: A Laboratory Study Abstract Theoretical models do not fit reality because they lack an important variable people. The human factor needs to be taken into account to improve the fit between theory and practice. Three different streams of literature build the basis for this study: supply chain collaboration, laboratory experimentation and behavioral operations management. The objective is to capture the implication of the human factor in a bargaining encounter between a supplier and a retailer. A laboratory experiment is set up to investigate the behavior of the decision makers in the Newsvendor case. The extraordinary design of this experiment captures the behavioral traits of the subjects. The Newsvendor theory is deliberately chosen because it is a very common problem a lot of companies face. Besides, it is a simple contract that does not distract the subjects. The design of the experiment moreover cuts out other factors like risk aversion and relational aspects. Thereby a clear focus on the fairness perception of the subjects is possible. The influence of fairness on the bargaining outcome is extracted by observing the negotiation process and comparing the results to the normative solution. The outcome of the experiment shows that a just environment and experienced fairness also with different partners will be reciprocated by the subjects. Besides, bargaining leads to an equal distribution of profit, however this fair solution for both parties does not maximize the overall profit of the supply chain. The combination of removed bounded rationality, experience and bargaining improves retailers performance to reach the optimal order quantity. Finally, guidelines for managers are created to attain a practical application of the results of this study in real business environment. Page 4 of 69

5 1 Introduction Optimization is one of the core drives for supply chain cooperation. A great amount of studies has been dedicated to mechanisms and models in this area. However, in business environment, cooperation is not always accomplished as predicted by theoretical models. Existing supply chain collaboration models assume to deal with completely rational subjects. They usually do not contemplate decision makers behavioral dynamics. A recent trend among economists is to enhance their rational models by adding psychological factors to align theory with reality. This study investigates a Newsvendor case, which is a very popular supply chain collaboration problem, because it captures the essential parameters of most inventory decisions. Furthermore this model is often used to gain behavioral understandings (Bolton & Katok 2008). In this study, the assumption is that the partners are subject to bounded rationality and act in accordance to their social preferences. One aim is, to clearly capture the human factor in the decision process of the Newsvendor case. A lean laboratory experiment is build up by leaving out relational aspects between the parties, as well as risk attitudes that might be caused by variations in demand. Furthermore the motivation of the subjects is very upfront and the experimental design includes feedback loops for the participants. The special characteristics of this setting allow extracting the human factor from the subjects decisions. The human factor is the core of this study. The lean experimental design visualizes the cause-and-effect-relationship between the emotions of the subjects and their decisions. The objective is to analyze the influence of fairness perception on the bargaining outcome, by observing the process and comparing the results to the normative solution. The findings of this study can be used to adjust cooperation practices. Furthermore the results can guide managers in improving their supply chain collaborations. It is the intention to display the outcome as simple as possible and make the findings of this study applicable to a wide range of companies. Page 5 of 69

6 2 Literature Review This study is relevant with the three streams of literature supply chain collaboration, experimental economics and behavioral operations management. 2.1 Supply Chain Collaboration The main objective of supply chain collaboration is to optimize the processes and workflows along the value chain far beyond the boundaries of the individual enterprise. This optimization can only be realized, when the companies work together in a goal-focused and problem solving manner. While cooperating, it is essential that each firm keeps its own area of governance and all partners should be equal within the network. In supply chain management and collaboration the companies are linked in real-time and can take decisions promptly. According to Voigt et al. (2013) the global purpose of supply chain management is to optimize the overall efficiency and services of the supply chain regarding the invested capital. In supply chain collaboration the parties join the network voluntarily to follow their common goal, however there is no central unit that takes decisions and directs the network. Without this central control unit, the companies decide individually what to improve and when to adjust and upgrade their processes. As a result, the supply chain will not be optimized uniformly with completely streamlined processes. In this form of network the parties are highly involved. They proactively contribute to the collaboration and simultaneously follow their own interest. The decentralized steerage increases the implementation time for improvements and optimization. Therefore in the collaboration the benefit from optimization will materialize later than in the centralized setting. Small, decentralized units control the processes in a defined area. These entities also design processes, optimize and administrate data for their limited ranges. The parties share information that is necessary for the planning and operation of the processes. Global sharing of all data between all parties is not needed, only required information will be pooled. A long-term cooperation is the aim, but it is not a duty. Entry and exit barriers, prevalent in supply chain management, are lower in the cooperation arrangement. Main investments for example are not undertaken by the parties of the Page 6 of 69

7 chain. The network turns to external service providers for keeping a low level of commitment and increasing flexibility for the participants. Collaboration between companies can occur in different areas. It is not limited to the operational order-fulfillment processes. It rather starts already in research and development and it can be extended to the transport and retail processes. Furthermore, the depth of collaboration can follow the objective of the companies. Having a clear common objective is the main factor, which decides about the success of collaboration. That is essential when a strict framework for the companies is missing. Besides, each party must have a defined area in which it can design, improve and optimize the workflow. The optimization effect will be leveraged, when the potential and competencies of each party are clearly stated. For sustainable success, a consequent, continuous and lean communication as well as an agreement of all parties is essential. The partners need to respect and honor the mutual decision areas. Only a truly equal collaboration can reach sustainable and successful optimization in the network (Günthner n.d.). The supply chain collaboration is supported by the loose coupling theory defined by Liu, Huang, Luo, & Zhao (2012). Loose coupling means, that the elements in a system are delimited and at the same time linked to each other. Coupling implies being committed to joint objectives and sharing resources to reach the goals. When transferring this theory to supply chains the firms are the independent elements, however they are linked through information, resources, technology and mutual projects. The individual companies which are separate in the system join forces to fulfill their tasks in the supply network. Companies in these relationships share knowledge, process data as well as tactical information. The elements in the set can adjust their resource and commitment level individually. Liu et al. (2012) claim that the level of coupling in the network increases when the parties have the same level of fairness perception. 2.2 Experimentation In experimental economics experimental methods are applied to investigate economic questions. An article by Roth (2010) wraps up of the long path of experimental economics. In the beginning no laboratories and nor experimental economists were placed at the faculties, and then a sporadic application of experiments followed. Today, experimental economics is even on its way to becoming a part of the philosophy of science. Theory has evolved to disregard Nash s bargaining game. Page 7 of 69

8 Subjects do not reach the perfect equilibrium and their intentions seem to be more diverse than covered by the model. Researchers include subjects experience and undertake experiments. Also the topic of this study, fairness in bargaining, gathered attention in experimental economics. The scientists dismiss self-regarded profit maximizing functions and other-regarding preferences make their way into experimental economics. Every new theory causes new experiments and their outcome reshapes the theoretical models, which leads again to new experiments. All in all it is a vital operating cycle. Overall the author states that laboratory experiments are very useful when control is needed. However, a combination of tools provided in experimental economics is the best. The article résumés that experimental economics fulfills its promises even though experimental economists are still in the learning phase (Roth 2010). In experimental work, the experimental result is compared to theoretical models. This research discovers if certain factors are missing in the theoretical model. The researcher s discovery is the fundament for adjustments in the rational model. Laboratory experiment with human subjects is one popular research tool in behavioral research. As Elena Katok (2011a) describes, laboratory studies complement other methods by bridging the gap between analytical models and real business problems. A good experiment is characterized by a simple environment that captures the quintessence of the actual problem while leaving distraction out. The theory which grasps a feature that is both real and interesting, as well enhances the information about the real world, should be experimentally tested (Katok 2011b). The scientist has a very high degree of control over the experimental setting. She interferes with the setting by systematically altering her variables of interest and observing the effects on the subjects being studied (Blumberg et al. 2005). The isolation of cause-and-effect-relationships between the variables becomes visible in this artificial setting while it might rarely happen in the natural environment. A precise measurement of the effect depends on the level of control that the researcher has over the environment (Tokar 2010). The variables in the experimental setting are either independent or dependent. The independent variable causes the dependent variable to happen. The correlation between the two variables is important as the presence or the absence of one variable is associated with the presence or absence of the other. Both variables either happen at the same time or the independent variable occurs before the dependent variable. The cause-and-effect-relationship between the factors is only valid if they are responsible for the outcome (Blumberg et al. 2005). Page 8 of 69

9 Field or laboratory experiments are distinct by the level of control the researcher has. In the laboratory the settings can be steered closely, whereas field experiments do not allow that firm level of control, yet field experiments have a higher level of external validity (Katok 2011b) Evaluation of Experimentation Experimentation, one form of data collection, has several advantages as well as some disadvantages Advantages The primary aim of experimentation is proving a cause-and-effect relationship between variables. The researcher can control the conditions in the experimental setting very well, which makes the experiment more elaborate and obliging (Bachrach & Bendoly 2011). It likewise allows precise surveillance and alteration of the variables. The researcher can for example alter the independent variable which will influence the outcome for the dependent variable. The amount of change in the independent variable and its consequences for the dependent variable is important. In the experiment, the scientist can also very well identify and cut out the distraction from irrelevant variables. Furthermore it is a very convenient form of data collection, as the scientist can flexibly schedule the timing of the experiment. Besides, the selection and the adjustment of the variables can be done flexibly and the quality of the data retrieved is very high. The researcher can use all her creativity when designing the experiment and she can establish conditions, which might not be observed routinely in the natural setting. The experimental setting can furthermore be adjusted flexibly. For some experiments for example it might be adequate to establish a control group parallel to the experimentation group, or to measure certain factors before and after the experimentation for estimating the influence of the experiment. Another benefit is the possible replication of the experiment. With clear conditions, other researchers can execute the exact same experiment with different subject groups or under slightly altered conditions, which will still lead to the same effect of the independent variable on average (Blumberg et al. 2005) Disadvantages There are also some drawbacks in experimentation which need to be considered. First and most important the scientist always needs to be aware that she does observe the subjects in an artificial Page 9 of 69

10 environment. Thus the observations are not 100% natural behavior. Even in a field experiment, the subjects behave differently when they are aware that they are being investigated. It is important not to frame or anchor the subjects before the experiment on a specific topic or amount. This could happen through the design of the experiment or in the introduction to the topic. Therefore the emphasis should be to keep the invitation and the introduction to the experiment as neutral as possible. The scientist could by instance avoid mentioning the main topic during the whole process from the invitation until the end of the experiment. One objective of the researcher should always be to experiment with an average crowd of unbiased subjects. She has to consider that subjects who volunteer for participating in an experiment might be extremely interested in the topic and therefore already biased before the experiment starts. One suggestion in this case is to avoid mentioning the exact topic of the experiment when inviting the subjects. Another challenge in experimentation is to transfer the obtained results to another group of people. An experiment can for example be conducted among students whereas the group of interest might be managers. Therefore it is necessary to clarify already in the early stages of experimental design to which extent the results obtained should be generalized. Last but not least the limited number of variables that can be investigated in the experiment might be unfavorable. By contrast in a survey a high number of variables can be included. So, when the problem at hands involves many influential factors, conducting an experiment might not be the best approach. Furthermore experiments are not the optimal tool to investigate the past or to predict the future. They are most valid for current problems or issues in the very close future. (Blumberg et al. 2005) Validity of Experimentation Validity is a tool that ensures a high quality in knowledge-creating. It evaluates whether the results are accurate and the chosen measure achieves the intentions (Arbnor & Bjerke, 2009). The two different forms of validity internal and external are characterized below Internal Validity When investigating internal validity, the researcher has to consider if she draws the right conclusions from the cause-and-effect-relationship investigated in the experiment (Blumberg et al. 2005). Page 10 of 69

11 The design of laboratory experiments is so flexible, that the scientist can influence all aspects at all stages. This high internal validity of the laboratory experiment is reached when there is a close similarity between the experiment and the underlying analytical model (Katok 2011b). However there some threats that might harm internal validity, such as the timeline of the experiment for example. If events occur simultaneously with the execution of an experiment, the examined relationship can be confused. Changes within subjects can also be a source of error, a hungry subject with a decreased blood sugar level shows abnormal behavior. Other factors like tiredness or boredom can even have a higher influence on subjects performance in the experiment (Wang & Dvorak 2010). This experiment takes only 40 minutes, this short time should limit abnormal behavior due to internal changes in the subjects. Besides, if an experiment has many sessions, the subjects might unintentionally learn from the previous sessions and thereby act abnormal in the later treatments. The design of this experiment prevents subjects from gathering experience as they face different settings in each session. Besides, there is a risk of information transfer. When an experiment takes place over several days the subject might talk to each other outside the laboratory. The same risk is faced when many different groups participate in the same experiment at different times. In this experiment, two groups participate on two different days, however to decrease information exchange they are not allowed to take any written documents out of the laboratory. The personal conditions of the scientist can also threaten the internal validity of the experiment. Internal conditions of the subjects, like tiredness and hunger can also apply to the scientist. Besides, routine in executing the experiment could also affect the outcome. On the other hand employing different researchers for observing and leading the experiment could in the same manner generate biased results. When selecting the subjects for an experiment it is important to find unbiased participants. Only they can form homogenous groups, which increase internal validity. Random group assignment of the subjects can support this aim. (Arbnor & Bjerke 1997). The goal of the subjects is very important for the validity of the experiment, this was already stated by Smith in The high degree of freedom in experimental design allows aligning the goal of the subject with the goal of the scientist. The scientist wants to create a setting close to reality. The common aim in a real business environment is to achieve the highest profit, which should also become the goal of the participants. Introducing a reward medium ensures that the goals of both parties are aligned. When the subjects are rewarded based on their achievement, they will pursue their Page 11 of 69

12 personal goal of a high performance. In order to motivate the subjects to maximize the profit during the experiment, it should be linked to their real payment. According to (Kagel & Roth 1997) this link will increase the intentions of the subjects to perform well in the experiment. However the payment of the subjects should not be entirely based on performance. A strictly performance based reward system might only attract a very competitive type of participant, who is convinced to win the game. In order to gather a homogenous crowd of subjects, the payment structure should include a participation fee for all participants. A monetary reward above the participation fee for the top performers can stimulate great performance in the experiment. A similar effect can be expected, when the profits earned in the experiment would be directly transformed into real payment for the participants. For this experiment the participation fee plus the reward for top performers is best suitable. The minimum payment to attract participants is set to dkk120 per person and the price per top performer is at dkk250. As the overall available funding for the experiment is at dkk6000, the number of participants can be set at External Validity To defend the external validity of the experimental results, the observed causal relationship between the variables must be possible to generalize across people, settings and times. As the environment in the laboratory experiment is artificial, this kind of research is in general low on external validity (Katok 2011b). Besides that general category, there are some explicit threats to external validity. First of all the subjects should be totally unbiased when participating in the experiment. However, when inviting them to the task, when introducing them to the topic or when holding a pre-questionnaire, they might already get framed towards the topic. This harms the unbiased minds of the participants. Only an overall careful design can eliminate this threat. In this experiment the word fairness, which is the main topic, is avoided throughout the whole process. External validity might also be threatened when the subjects of the experiment are different than the target group for applying the results. The concept of parallelism states, that the experimental behavior of the subjects can be carried over to micro economies with equal conditions (Katok 2011b). In this experiment, the subjects are students and the group of interest is managers. After the decision for conducting an experiment, it is important to decide who should participate in the investigation. The paring of two humans in the experiment is necessary to investigate behavioral aspects. If humans and computers are matched in an experiment Page 12 of 69

13 the aspect of fairness can never be investigated adequately, because humans do not experience the same amount of emotions when dealing with a computer. The optimal participants for this setting would be real managers of suppliers and retailers, who deal with the supply chain collaboration in their daily work. The differences in the backgrounds and experiences of the managers however might not allow creating a homogenous group. Besides establishing contact and raising interest of suitable candidates can be challenging. The matter might be further complicated by finding a suitable date where all subjects can participate. As full-time employees have work obligations the participation will need to be approved by their company as well. All those issues complicate setting up the experiments with subjects from the industry. Recruiting subjects from the students community might be adequate and according to Bolton, the use of students in the Newsvendor setting instead of managers is just as valid. (G. E. Bolton, Ockenfels, & Thonemann, 2012) By recruiting students for an experiment, the scientist is aware that the subjects might have no professional background at the topic. The researcher always aims for a homogenous pool of subjects therefore the subjects should have a uniform knowledge about the experimental topic. To support the aspect of homogeneity, the subjects are only recruited among students enrolled in bachelor or master programs at Aarhus University. Furthermore students are more flexible regarding the timing of the experiment and they might be easier motivated by monetary rewards to participate in the experiment. All in all recruiting students as subjects in this experiment is the preferable and most feasible solution. 2.3 Behavioral Operations Management Origin of BOM Operations Management (OM) studies the design and the control of the production process. Efficient business operations should use a level of resources as low as possible and at the same time effectively meet the customer s desires. Based on the field processes of interest, OM builds scientific theory and validates it with data from the business process. (Loch & Wu 2007) Behavioral OM represents the interface of OM with Organizational Behavior (OB), decision science, and psychology. All these intersections generate a huge amount of theories and observations of Page 13 of 69

14 human behavior, which can be united with the understanding of employee performance in operational processes. Loch & Wu (2007) describe Behavioral Operations Management as a multi-disciplinary branch of OM that explicitly considers the effects of human behavior in process performance, influenced by cognitive biases, social preferences, and cultural norms. One form of cognitive bias is bounded rationality. According to Loch & Wu (2007) a bounded rational person oversees information when she is engaged. She impulsively simplifies complicated underlying influences and projects data without having the necessary input. A bounded rational person might overestimate the outcomes in areas she feels confident and underestimate outcomes in less confident areas. For example she underestimates the duration of decisions within a project. Another characteristic is that she avoids topics with uncertain outcomes and uncertain probabilities as well as complex issues. Additionally, bounded rational people are easily anchored, conform to peer pressure and hold a huge bias towards previous information and own experience. Besides bounded rationality, social goals and social preference are also deeply rooted in the person s psychology. Those aspects of a person can be shaped by culture. Culture is comprehension and skills of an individual, that are obtained through both, individual learning as well as social learning within a group. The experience of the group over time shapes the culture, which will be assimilated into a groups behavior. Accordingly, collective behavior can be seen as the result of culture spread and evolution. In the beginning OM was treated like a natural science of operating systems with mathematical models. Operations Research (OR) techniques were used to solve problems. During the 1960/70 its main focus was on formulating problems with a single objective and a set of constraints. The aim was to obtain results, which will optimize the whole system. Relevant streams of research from the operational side, such as TQM, JIT and MRP left OM out. Finally in the 1980s OM became recognized as a functional field of management. In the 1990s companies were built up more and more decentralized, and models such as game-theory, decentralized decision making and strategic interaction emerged. This development in the business world also influenced the evolution of OM. The strategic perspective of the supply chain gained importance because companies moved their focus to value chain optimization and incentive alignment within the supply chain. During this phase OM began to extend its collaboration with other disciplines (Loch & Wu 2007). Behavioral research is a growing field in supply chain management which transmits irrationality into the rational models. Behavioral economics and behavioral operations management are closely Page 14 of 69

15 entangled. In psychology a theory is a verbal construct or theme which organizes experimental regularity, however for an economist scientific tools and formulas build the theory. OM is still at square one when it comes to integrating people issues into the discipline. Behavioral economics already had a triumphal procession in other fields such as marketing and finance. Theoretical models that include behavioral aspects eliminate the incorrect assumption that people can be integrated like machines into a manufacturing setting. BOM includes both, the theory described in models and empirical methods such as experiments for testing the models. For further development in the field, both, models and experiments are needed Studies in the field Even though the combination of supply chain collaboration and behavioral factors is quite recent, some research has already been conducted. Academics develop models as well as work on the theoretical examination and clarification of concepts. Furthermore scientists conducted experiments and surveys to examine the topic. Tokar (2010) provides a framework for behavioral research in logistics and supply chain management. The experimental study is given a special credit in this field of research. In 2000, Schweitzer & Cachon conducted the first experimental study based on the Newsvendor case and numerous others follow their lead. Those studies with this basic model of supply chain contracting make explicit and implicit assumptions about human behavior. The first investigation found that when the subjects are anchored at a certain order quantity, they will stick to that amount, even if it does not prove optimality. Su (2008) for example investigates the classic Newsvendor model where the decision maker is subject to bounded rationality, which results in ordering a non-optimal quantity. In the Newsvendor setting the pull-to-center bias has been discussed extensively in the literature. Several explanations and implications have been suggested to this day and a listing of the findings is displayed in E. Katok & Wu, (2009). G. E. Bolton, Ockenfels, & Thonemann (2012) discover that the direction of performance of students and managers in a Newsvendor experiment are equal, however the scale of the manager s performance is bigger. This grants permission to transfer the outcome of this experiment with students to the real world. Moritz, Hill, & Donohue (2013) investigate the performance in a simple Newsvendor scenario as they build up the relation between cognitive reflection and the Newsvendor decision making behavior. Page 15 of 69

16 A different form of supply chain contracts is the basis for the experiment of Davis (2012) who shows that regret is partly the reason why the participants change their behavior after having paid out bonuses to their opponents. As already mentioned before, humans cannot be viewed as efficient and error free as machines, because the human information processing has limited capacity. This limitation becomes visible when subjects perform computational steps. Braisby, N., Gellatly (2005) found out that the human brain uses opportunities in the structure or environment of the experiment which will determine their performance. Davis (2012) evaluates three different pull contracts in a laboratory setting, he concentrates on the coordinating parameter and the implied behavioral assumptions, but he does not include the fairness concern. Integrating fairness into the investigation of supply chain contracts is important. Fairness is proven to be the main reason for terminating negotiations between supply chain partners (Katok & Pavlov 2013). Elena Katok & Pavlov (2013) took up a general view on the supply chain and identified three main causes of channel inefficiency in their experiment, which are fairness, error making and incomplete information about the counterpart s fairness perception. In their study of 2004, Buchan, Croson, & Johnson draw the line between the fairness concern and the behavior in the bargaining game. Distributional fairness investigated recently by Ho, Su, & Wu (2012) is also a topic investigated in this experiment. Fairness in supply chain context is also investigated by Haitao Cui, Raju, & Zhang (2007). They develop a model where both partners in a conventional dyadic setting are concerned about fairness. The partners deal with symmetric information and the effects of fairness on channel coordination are examined. The channel is coordinated by a very simple wholesale pricing contract just above the supplier s marginal costs. The authors introduce two fairness parameters, µ and γ, into the utility functions to bring the model closer to reality. Their conclusion is that fairness is an automated, nonstrategic behavior. Page 16 of 69

17 3 Research Problem Description 3.1 Analytical Background The global optimum is the maximum profit that can be achieved in a supply chain. An unbiased decision maker sees the two parties of supplier and retailer as one entity which means as two parts of one organization. This decision maker identifies the best approach for the whole supply chain by maximizing the overall profit and ignoring money transfers between the companies. The difficulty of this approach is that both parties need to surrender and hand over the power to the unbiased decision maker, which is unrealistic in a real business situation. Supply chain contracts can replace this unbiased decision maker and they even allow allocating the profit between the two parties. If the supply chain contract is designed correctly, no partner can increase her profit by deviating from the optimal solution secured through the contract. Therefore both partners will stick to their settlement. (Simchi-Levi & Kaminsky 2009) One and probably the simplest case of the supply chain contracts is the Newsvendor model. It is used commonly for various business contexts and is has been a research topic for more than 100 years. This model the inventory control focusses of a single item with stochastic demand over a single period. The name of the problem stems from a newsvendor who has to decide how many newspapers to stock on a newsstand before she witnesses demand. If demand falls below the order quantity, the newsvendor has to dispose excess inventory. On the contrary, if the demand exceeds the order quantity the newsvendor faces a loss in sales and thereby a loss in profit. Those are the overage and underage costs the seller fears. The challenge is in ordering the right amount for the whole season in one single order. This order is placed before demand occurs while the newsvendor is threatened by overage and underage costs. According to Gallego (1995) the problem is of particular interest for items with substantial uncertainty in demand and large overage and underage costs. Characteristically for this model is that it is mostly applied for short lifecycle products, where a too high purchase causes the retailer to suffer high costs. In the marginal analysis of the Newsvendor case the expected contribution of the decision to buy one more unit is investigated. If this net gain is positive, one more unit product should be bought. Otherwise the newsvendor should refrain from buying. The last incremental unit that has a Page 17 of 69

18 positive expected contribution is put on stock. The unit to be purchased has to be increased by 1 unit until the expected net gain is zero and no further improvement can be achieved. In order to introduce the common mathematical structure of all Newsvendor cases, Hill (2011) describes the variables as following. is the decision variable, and is the aim to find its optimum for a one-time decision. quantity and denotes the optimal value for. is the order The second variable is demand which occurs to be random and its distribution can be Normal, Poisson or Uniform distribution with specific distribution parameters like mean and standard deviation. There is a difference between the continuous and the discrete demand case in the theoretical model. In the discrete case, the demand only takes on integer values, that is the situation investigated in this study. The unit overage cost is the cost for buying one unit more than occurred demand during the oneperiod selling season. In the standard retail context, the overage cost is the unit cost less the unit salvage value. For this case the values are and. Therefore the overage cost are: In contrast to the overage cost are the unit underage cost, also called stock-out cost. This is the cost of buying one unit less than the level demand during the selling season. The underage cost is computed as the unit price less the unit cost.. For this case the values are and. Therefore the underage cost are: With order quantity and specific demand, the cost for the one-period selling season is ( ) { ( ) ( ) Page 18 of 69

19 The value ( ) is called the critical ratio or critical fractile and is always between zero and one. For this case the critical ratio is This fractile can be used to classify products. For a high-profit product the following equation is true High-profit products like books and bicycles are characterized by a high profit margin. On the other hand computers for example are distinguished by a low profit margin and a low salvage value as defined by Schweitzer & Cachon (2000). The value of classifies the product in the experiment as a high-profit product. The centralized order quantity ( ) and Under the perfect cooperation, the supplier should offer a wholesale price equal to her cost, and the retailer earns all supply chain profit. However, in reality, most supply chain partners are independent from each other. Demand and cost information are private. This leads to the decentralized solution below, given the supplier s wholesale price, the retailer s optimal order quantity is ( ) Page 19 of 69

20 In reality, the cooperation is usually obtained by direct bargaining between supplier and retailer over wholesale price and order quantity. The bargaining is necessary because a lack of trust between the parties keeps both from revealing cost information. In this experiment a dyad supply chain channel cooperation problem is investigated. The supplier makes a decision on the wholesale price for the unit product. Given supplier s production cost. Based on the wholesale price offered by the supplier, the retailer determines an order quantity. Retailer price is fixed at. The retailer incurs a production cost. The demand during the selling season is uniformly distributed ( ). Excess quantity will be disposed without cost. The excess demand is considered as lost sales. The supplier s profit is described by ( ) The retailer s profit is { } ( ) The centralized profit of the supply chain is In this experiment it is not the aim to study the risk attitude of the subjects. Therefore it is necessary to exclude the randomness that is brought in by the random demand. A different profit approach that excludes uncertainty is the expected profit which is more suitable in this case. In general the expected value of a function is generated by ( ) In this experiment the expected value ( )consists of two parts ( ) { ( ) ( ) Page 20 of 69

21 If the demand is equal or below the order quantity then the retailer can satisfy this demand with her production. The outcome for the case is thereby the mean of the uniform distribution ( ) that matches the range of. If the demand is above the order quantity, then the retailer can only satisfy the part of the demand until the order quantity. The outcome for the case is thereby. The probability for each part is generated by ( ) where is the lower end and is the upper end of the range where occurs. and are the starting and the ending point of the uniform distribution ( ) When calculating the expected profit and including the demand, the retailer s expected profit is: ( ) ( ) With the obtained values for the mean and the demand of the end customer ( ) the formula for calculating the retailer s expected profit is: ( ) ( ) The centralized profit is described as Page 21 of 69

22 with ( ) The formula for the centralized profit is ( ) ( ) ( ) With the values for the demand distribution and the mean this leads to the formula ( ) ( ) The range of a possible wholesale price is $4 and $11. The mean wholesale price of this range is The optimum order quantity for this and with ( ) is ( ) ( ) ( ) ( ) 3.2 Economics Background Literature Review on Fairness Game theory is the perfect foundation for building up experiments. Game theory already delivers exact rules about the setting of the experiment and the interactions and it can be used to explain the behavior of subjects in experiments (Bendoly, Donohue, & Schultz, 2006). The standard economic game theory claims that in a bargaining situation, any amount of profit is better than no profit. The economically rational seller should therefore demand the smallest amount over zero which is described as the sub-game-perfect-equilibrium of the ultimatum game (Ruffle 1998). However reality does not support this theory. Real buyers generously offer 40% of the surplus, much more than Page 22 of 69

23 suggested in the model, but still they are often rejected by the sellers. The reason for rejection can be found in the fairness concern. As fairness has such a big influence on bargaining, it is necessary to define the word and reveal why it appears to be deeply rooted in humans. The word fairness stems from the old gothic word fagrs which stands for suitable. It is furthermore used for the words justice, modesty, equality or peacefulness. A fair person sticks to rules and stays honest, even under tough conditions. Fair people do not cheat or use small tricks, even if they are tempted to do so. Colloquially fairness means to be free from favoritism, self-interest or preference in judgment. According to Kahneman, Knetsch, & Thaler, (1986) the context in which a decision is presented and the framing significantly influence the belief of fairness. Furthermore humans see unfavorable actions that lack proper explanation and justification as unfair (Buchan et al. 2004) & (Smolinski 2012). There are different reasons for people to behave fair, and similarly there are also diverse forms of fairness. The motive fairness which steers the behavior in the bargaining game deserves an accurate analysis. Intentional fairness means that a person will treat another person fair but only if the counterpart also showed fair behavior beforehand. This positive exchange implies that the utility and the payoff of one person increase when she experiences fair treatment. As a conclusion, in the neutral state no one cares for one another. However this theory could not be proven by Bereby-Meyer & Niederle (2005). Two models from the category of intentional fairness explain the fair behavior of humans. Inequality aversion is the first model in the category of intentional fairness and the second one is the reciprocity model. Inequality aversion on the one hand states that the desire for fairness comes from the fact that people do not like outcomes that show inequality of any kind. According to Loewenstein, Rick, & Cohen (2008), people that behave fair are concerned about their own payoff and either the difference between their own payoff and others payoffs or the difference between their own payoff and what they perceive as a fair payoff. The reasons for people to reject low offers can be different depending on the model. According to the inequality-aversion model responders reject the unfair offer because they perceive the proposed inequality as hurting. Page 23 of 69

24 The reciprocity theory by contrast incorporates the intentions and explains the behavior as the act of rewarding people who are helping and punishing people who are hurting. As a result the behavior of the individual is not led by one s interest in fairness but by the desire to remedy harms and recompense decent behavior (Glimcher et al. 2009). This theory states that people enjoy returning intentional kindness with kindness, and expected unkindness with unkindness (Loewenstein et al. 2008). When relying on reciprocity-based models people think that if they treat others fairly, they will be treated justly themselves (Buchan et al. 2004). In case of rejections in the reciprocity approach, unkindness can be followed back to previous unkind treatment by the counterpart. (Loewenstein et al. 2008) Even though humans strive for fairness as an ideal, self-interest is still first priority. This is reflected by distributional models where one subject only cares about her own share and the overall distribution of payoffs among the others. Unlike in the intentional fairness model her preference is independent of the source that caused the result (Bereby-Meyer & Niederle 2005). The measurement is relative in nature as the own payoff is compared to the other subject s payoff. Furthermore a reference point is used to define the fair solution (Bolton & Ockenfels 2005). A different approach to describe fairness is undertaken by Liu et al. (2012). They split fairness into four different form of justice - distributive justice, procedural, interpersonal and informational justice. The first two forms of justice are concerned with the structural aspects. Distributive justice is worried about a fair result. In this form the partners only look at the justice of the outcomes. They evaluate their own result in comparison to the outcomes of the counterparts. The second form, procedural justice is concerned about the fairness in the process which leads to the result. The procedures before accomplishing the bargaining result or solving an argument are in focus. The parties will perceive the justice in this form as high when they feel they have control over the process. When the buyer has the impression that the supplier has a high standard in these structural forms of justice then she is more willing to build up a long term relationship. Procedural fairness is an indicator for a company s overall fairness as it reflects its established process and policies. The latter two forms of justice embody the social aspects of justice, where interpersonal treatment and communication during companies interactions are relevant. Informational justice is focused on information exchange and open communication, whereas interpersonal justice refers to the way people are treated in business. Liu et al. (2012) found out that the relationship of the entities will be Page 24 of 69

25 prosperous and stable if all parties simultaneously observe high levels of justice. A high level of justice at all four categories creates a transparent and cooperative atmosphere which improves the quality of the parties teamwork. Luo (2005) identifies perceived justice as a robust predictor for reactions. However the investigation of Liu et al. (2012) indicates that a company should put most focus on procedural and informational justice. Those two forms have the greatest impact on coupling behavior and relationship performance. Studies prove that if one party perceives the behavior of a counterpart as unfair she will sacrifice optimizing her own income and even spend money on punishing the other party (Roth 2010). This behavior is called altruistic punishment. Findings prove that punishing people who have violated social norms activates the same reward circuitries as processing primary rewards. All in all engaging in altruistic punishment is gratifying even if it is pricy (Fehr & Gachter 2000). Fairness behavior has been investigated in numerous experiments. In the simplest experimental setting parties share an amount of money (Loewenstein et al. 2008). One of the first experiments in the area was conducted in 1982 by Guth, Schmittberger and Schwarz as an ultimatum game in which the parties needed to share 100 Deutsche Mark between each other. Since then countless variations of this game have been investigated. Du (Du et al. 2012) introduced the parameter λ, the so-called fairness concern parameter. This represents the natural trait of the supply chain partner which shows her level of fairness concern. In case the parameter is very high the partner is more affected by fairness than for example by the level of payment. This parameter is detached from the company s position in the collaboration. Besides economic experiments that investigate fairness, also the neuroeconomic sector undertakes experiments with the ultimatum game (Glimcher et al. 2009). The aim of these experiments is to uncover why subjects would reject unfair offers. Rejection leads to zero profit for both parties, whereas accepting an unfair offer would still lead to a positive earning. Loewenstein et al. (2008) explain the general desire for fairness in humans with evolution. They discover that the immediate response to unfair offers is rejection. However this reflex can be overridden by an area in the human s brain which allows ignoring this urge and accepting biased offers. Innovative studies have been done in the field. Xiao, E. and Houser (2005) for example studied the role of emotion expression in costly punishment decisions. Humans express emotions whenever Page 25 of 69