چگونه توصیه های شفاهی در نظر گرفتن محصول و کیفیت انتخابی : انگیزشی برای پردازش دیدگاه اطلاعات
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|4931||2010||9 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Business Research, Volume 63, Issues 9–10, September–October 2010, Pages 1041–1049
A laboratory experiment examines the effects of electronic word-of-mouth (e-WOM) on consumer consideration and choice of an experience product. Specifically, we manipulated the number of consumer recommendations and the optimality of the recommended product in a realistic online shopping environment. The results indicate that e-WOM is likely to result in more time considering the recommended product. For consumers more motivated to process information, e-WOM recommendations lead to more time spent on the choice task overall. Further, consumers with less motivation to process information make suboptimal decisions based on e-WOM recommendations. Consumers with a high motivation to process information are willing to accept recommendations and switch from declared attribute preferences, but choose only optimal products.
Some researchers consider consumer-to-consumer communication such as word-of-mouth (WOM) to be a strong and credible influence on consumer behavior. Although WOM has traditionally been studied from the perspective of face-to-face communication (Bansal and Voyer, 2000 and Brown and Reingen, 1987), it is becoming prevalent in online shopping environments as well (e.g. Dellarocas, 2003). Consumers may be exposed to electronic WOM (e-WOM) through websites, blogs, chatrooms, or email (Hennig-Thurau et al., 2004). Many of the largest online retailers, including Office Depot, Amazon, Home Depot and Macy's, encourage e-WOM by allowing online reviews on the products they offer. It is important, however, to understand the salient differences between electronic and traditional WOM. Given that the conditions associated with each are different, pertinent theoretical and practical questions are different as well. In this research, we identify several salient differences between traditional and e-WOM contexts and use these as the underlying framework to develop our hypotheses and experimental methodology. Generally, traditional WOM has been conceptualized and explored as interpersonal informational exchange between individuals familiar to each other (Brown and Reingen, 1987). An implicit assumption is that the receiver has inherent belief in the value of the WOM provider's information, either due to perceived similarities (Gilly et al., 1998) or perceived product or service category knowledge (Bansal and Voyer, 2000). In the online context, there is typically no familiarity between senders and receivers of e-WOM. This lack of familiarity between e-WOM receivers and senders may heighten the potential for the posting and use of fraudulent e-WOM as well. Further, given that overall information search and dissemination costs are lower online than offline (Bakos, 1991); it is more likely that consumers in online buying environments are simultaneously exposed to an abundance of both e-WOM and extensive objective product information. These fundamental differences between traditional and online contexts warrant further investigation into the extent and manner of e-WOM usage. It is from the baseline suggested by these differences that this research investigates unexplored boundary conditions associated with e-WOM usage. We use a laboratory experiment to examine the effects of e-WOM recommendations from strangers on consideration and preference for a laptop computer. The experiment was conducted in a hypothetical yet realistic online shopping environment that provided significant amounts of objective product information and interactive tools to manage that information. Specifically, we manipulated the presence and extent of consumer recommendations on products that represented either optimal or suboptimal choices. Our results indicate that e-WOM recommendations do influence consideration and choice. For consumers lower in motivation to process information, e-WOM seems to serve as a heuristic cue on the basis of which they are willing to make suboptimal choices. On the other hand, consumers higher in motivation to process information tend to use e-WOM as an argument. They choose the recommended product configuration, as long as they can obtain an optimal product. Further, consumers lower in motivation to process information redirect their limited search and consideration efforts towards the recommended product, whereas consumers with higher motivation search more overall and around the recommendation.
نتیجه گیری انگلیسی
The findings reported here shed light on some important boundary conditions associated with the impact of e-WOM recommendations on search and evaluation behavior and product choice. We find that e-WOM is likely to result in more time analyzing information overall and more time considering the recommended product for consumers who are more motivated to process information. Specifically, as the strength of e-WOM recommendations increases, consumers with high motivation to process information tend to spend more time shopping overall and more time considering the recommended product. Interestingly, low NFC respondents do not seem to search and analyze more overall due to e-WOM recommendations. Instead, they tend to include the recommended product as part of their limited search and consideration efforts. Thus, e-WOM recommendations seem to serve to redirect the limited search and consideration efforts of consumers with lower motivation to process information. These results seem largely consistent with predictions made by elaboration-likelihood (ELM) and heuristic–systematic (HSM) models. Further, the results show that participants with less motivation to process information are willing to make suboptimal decisions based on e-WOM-recommendations. Surprisingly, a single e-WOM recommendation incidence is sufficient to trigger a suboptimal choice. On the other hand, higher NFC consumers do not make suboptimal choices based on e-WOM recommendations. This finding has important implications both for retailers and public policy makers in that even single instances of e-WOM recommendations may drive individuals with low information processing motivation to make suboptimal choices. In this study, participants with a high motivation to process information were also affected by e-WOM, albeit in a different way. In the presence of e-WOM, high NFC participants tend to switch (and acknowledged doing so) from their attribute preferences towards the recommended attribute profile, although they are still likely to choose an optimal product option. Perhaps the recommendation(s) served as valuable information regarding the experience attributes associated with the product. Thus, high NFC consumers seem willing to sacrifice some of their own preferences to pick a recommended product, as long as there is no compromise on the optimality of that product on other attributes. Thus, they seem to value the certainty of an e-WOM recommendation on an experience product over their own stated attribute preferences. From the managerial perspective, there are several insights that may be gleaned from these results. Consumers with low motivation to process information tend to treat even single instances of e-WOM as decision heuristics. Thus, such consumers may make decisions that they feel good about at the time but are suboptimal from an objective perspective. Such a decision at the time of purchase could potentially lead to dissatisfaction during product consumption. As a first step to alleviate this problem, managers could attempt to assess the motivation to process information of customers searching for experienced products. Here, we used NFC as a proxy for motivation to process information, but other variables that affect ability and/or motivation to process information, including time pressure, age, personal involvement, etc., are likely to function in a similar manner. Next, for consumers identified as likely to be less motivated to process information, the availability of buying guides, live chat representatives and other tools to simplify the decision could encourage a more need-based rather than heuristic based decision process. Further, e-retail managers for complex products could ensure that certain parameters are satisfied before e-WOM is provided. Since the e-WOM sources, their motivations and expertise are largely unknown, one could ensure that several e-WOM instances are accumulated before being provided to potential customers. This would provide additional validity to the arguments, for or against, made by any one e-WOM provider. Further, more elaborate descriptions of experience with various attributes of the product, as a reflection of the accuracy of their eventual recommendation, could be sought from e-WOM providers to alleviate the potential problem of frivolous recommendations based on limited utilization of the product in question. The consumer with a higher motivation to process information seems better able to make good decisions. A fairly obvious conclusion from these results is that full, complete and accurate attribute information coupled with interactive tools to manage that information should assist them in making good choices. However, it is important that they too were susceptible enough to abandon their own attribute profile preferences and choose the recommended profile. Although they continued to make optimal choices, they nonetheless were influenced enough to sacrifice their own preferences. Thus, the strategies of either releasing e-WOM only when there is a larger number to recommendations (to validate any one position) or requesting elaborate descriptions of product related experience from the recommender(s) may help to ensure that choice decisions made by higher need for cognition consumers will be more likely to result in their future satisfaction with the product. The evidence that even limited amounts of e-WOM may lead to suboptimal decisions and influence choice also raises an interesting question from an ethical perspective. Some marketers may pay consumers to post information online about products (Frazier, 2006). What is the responsibility of an e-retailer to ensure that e-WOM is truthful information from legitimate customers rather than fraudulent reviews from someone with a vested interest? Should there be checks in place to prevent these postings? Should a disclaimer be required? To the extent that legitimate e-WOM sources are likely to provide more accurate information and therefore lead consumers to better decisions, it may be of strategic interest for e-retailers to put in place mechanisms to ensure that all e-WOM posting are indeed from genuine past users of the products they market. 5.1. Avenues for future research These results also open up several interesting avenues for future research. E-WOM is a relatively new phenomenon and there are vast areas still to be explored in this area. For instance, we focused very specifically on positive e-WOM recommendations and did not include negative e-WOM information, e-WOM information that is ambiguous, or a combination of various reviews, which may be present on many sites that track and provide e-WOM recommendations. Further, we tested only one product category. Rich insight can be gleaned by studying various product categories, including search products or very high risk experience or credence products and services and study the behavior of consumers reading e-WOM associated with such products. Future research might also expand this study to a non-student sample. Although laptop computers are salient to student consumers, it is important to acknowledge that such a sample restricts the external validity of the study. Further, although considerable effort and care was taken to ensure strong external validity as far as the website, product alternatives and interactive tools were concerned, we still could not mimic real shopping conditions. It is also important to note that in experiments, where product choices and “purchases” are purely hypothetical, we assume away rich hybrid or subcontracted decision strategies (e.g. Rosen and Olshavsky, 1987) that very likely occur in the choice and purchase of such high risk products. Thus, although we are able to demonstrate and contribute to the literature in several ways, particularly by revealing some salient ways in which consumers high and low in motivation to process information differ in their search, consideration and choice behavior for an experience product, in the presence of e-WOM, we acknowledge the limitations that are associated with experimental research.