مفاهیم سود از جهت های نوع دوستانه در مقابل خودخواهانه برای مبادلات بنگاه به بنگاه

This study significantly expands upon previous research by Hill and Watkins [Hill, Ronald Paul and Watkins, Alison, (2007), “A Simulation of Moral Behavior within Marketing Exchange Relationships,” Journal of the Academy of Marketing Science, 35 (2), 417–429] involving business-to-business exchanges through the use of a more sophisticated simulation and a different theoretical orientation. Profitability implications for sellers and firms in the context of information sharing and dynamic firm adaptation are explored using q-learning evolutionary models and embedded artificial trading agents in a competitive environment. This method allows buyer agents to react to complex and evolving circumstances based on historical information about seller agents. The results suggest that sellers with more cooperative strategies are more profitable in the long run, especially when firms employ multiple agents.

The strategic connection between the treatment of customers and organizational success has been discussed by marketing scholars for decades (Corfman & Lehmann, 1993). From production models to the marketing concept to relationship marketing (RM) paradigms, academics and practitioners have evolved behavioral expectations of sellers to meet the rising requirements of buyers (Morgan & Hunt, 1994). The underlying premise is that RM leads to the development of mutually-rewarding and long-term associations among trading partners. While disagreements persist, marketers concur that trust and subsequent loyalty garnered from relationship marketing results in a more profitable and stable client base (see Garbarino & Johnson, 1999). Recent evidence using simulated business-to-business (B2B) agents2 lends additional credibility to these findings (Hill & Watkins, 2007). Of course, such behaviors may be characterized in a number of ways, including how relationship marketing significantly overlaps with ethical business practices (Murphy, Laczniak, & Wood, 2007). Using this perspective, organizations as well as individuals seek trustworthy partners who are committed to fair transactions, i.e., dealings that are in the best interest of their firms while also serving the needs of customers (Watkins & Hill, 2005). Research in business ethics has shown the positive impact of developed moral reasoning capability, which provides employees with the skills to navigate such competing demands (Monga, 2007). At a fundamental level, competitive and cooperative tactics exemplify business-to-business transactions. These orientations impact financial success in both positive and negative manners (Lou et al., 2007 and Lou et al., 2006). Lehmann (2001) suggests that envy and altruism are essential ingredients to these approaches, and appreciably influence the payoffs of exchange participants. Under conditions of envy, parties place little emphasis on others' transactional needs, except to the extent necessary to encourage further negotiations. On the other hand, fairness, duty, or obligation may arise among a subset of firms and individuals because of altruistic belief systems, described by Fehr and Schmidt (1999, p.819) as “self-centered inequity aversion”. The purpose of our investigation is to examine different seller-firm emphases on transactional fairness, payoff functions, and profit implications within the context of information sharing and dynamic firm adaptation. Simulation is conducted using a hybrid evolutionary model and embedded artificial trading agents in a competitive environment, as recommended by Midgley, Marks, and Cooper (1997). This method allows buyer agents to react to a complex and evolving set of marketplace circumstances based upon historical information about seller agents. A primary benefit of this technique is its capacity to look at cumulative profits over a very large number of competitive and cooperative interactions. The configuration used here is consistent with the model by Raju and Roy (2000) that examines the impact of pricing strategies and information on profitability across a limited number of firms. The next subsection presents details on the simulation's parameters and the nature of buyer–seller interactions, their consequences, and their potential implications. We then present the simulation results, followed by a discussion of marketing practice and theory.

The true value of any simulation model is in its ability to capture the essence of important constructs and their impact upon key marketing outcome measures. In this research, we used a q-learning approach with dynamic adaptation capacity to create an artificial B2B transactional environment. Agents were characterized from most selfish to most selfless in the following order: egoists who sought to maximize their gain from every transaction, realists who tried to extract as much as possible without completely jeopardizing future opportunities, loyalists who placed their firms' reputations above all else in marketplace interactions, and altruists who sought to meet the needs of exchange partners as their first priority. The more intricate models present the most interesting conclusions. For example, the delay in the ability of buyer agents to comprehend the culture of competition versus cooperation within multi-agent mixed firms because of the heterogeneity of seller types is an interesting, albeit not completely surprising, result. The change in leadership with regard to profitability beyond some point by loyalists over altruists under the added condition of dynamic adaptation was unexpected. A possible explanation is that loyalists take into account the needs of both exchange partners in their decision heuristic, ultimately making them the most successful. At some level, their influence determines the blend of seller types in both adaptation scenarios. Another interesting consequence of allowing for seller firm adaptation to the evolving preferences of buyer firms is the final combinations of orientations in and of themselves. The uniform model with dynamic adaptation ended with each seller firm maintaining the dominance of original orientations, while the mixed model seemed to allow realists to grow disproportionately within more altruistic firms. One rationalization for the former is that the market rewards different behaviors at various stages in its development, permitting different types to prosper over time. On the other hand, the latter finding may be a function of the ability of more selfish sellers to prosper in firms that are more diverse and whose corporate cultures are more difficult to ferret out. It might be optimal for firms to allow a few egoistic sellers among its altruistic and loyalist sales organization to increase profits. Additional research is clearly warranted. Altruistic sellers lower their price offerings to buyers over the course of the simulation, while egoistic sellers raise their prices. However, the average price drops considerably as the market develops, signaling the growing power of buyers to discriminate among sellers in their search for better values. Interestingly, as the simulation gets more complex and moves to larger seller firms with greater diversity, egoist sellers complete more transactions and mean prices increase. When dynamic adaptation is added to the equation, nearly stable prices across simulation runs clearly advantage sellers relative to buyers.