- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
Word of mouth (WOM), long recognized as a highly influential source of information, has taken on new importance with the proliferation of online WOM. Research in online environments has focused on individuals who actively participate in generating WOM. However, over 90% of those that read WOM are non-participants, commonly called “lurkers.” This paper develops and tests a thematic discrepancy analysis (TDA) approach that combines commonly available information on Views and Replies with content analysis to provide new insights into differences between WOM participants and lurkers. TDA provides managers with market-sensing information to identify hidden opportunities and threats, as well as to test for non-response bias. Given the lack of approaches to address non-response bias due to lurkers, TDA represents a significant contribution to research methodology. We demonstrate the efficacy of TDA by applying it to a large scale WOM dataset containing over 80,000 messages from a brand-specific online forum.
Thematic discrepancy analysis involves three stages: (1) collecting and analyzing counts of Views (WOM impact) and of Replies (WOM activity); (2) collecting and analyzing the content of the population of messages to identify themes using non-dictionary or dictionary based approaches; and (3) identifying themes which show discrepancies between WOM activity (Replies) and WOM impact (Views). Thus, TDA reveals topics of greater interest to lurkers. In doing so, it provides vital insights for both marketing managers and researchers. To date, there has been little research on methods that can discover and track the topics of interest to lurkers. The lack of such methods has led the WOM literature to focus on analyzing participants while largely ignoring the vast majority of people who read WOM without participating. TDA fills this gap by providing managers with market-sensing information on issues of importance to lurkers that might otherwise be overlooked when just reading WOM generated by participants. In addition, TDA provides insights into marketing messages that may appeal more to lurkers as well as indicating on which websites managers should place advertisements containing those messages. By applying TDA to large scale online WOM data, we demonstrate the ability of TDA to identify themes and content areas generating interest among lurkers, as well as those content areas generating differing levels of interest between lurkers and active participants. Given that there are no other publicly available methods for generating these types of insights about non-participants, this represents a significant contribution to both academic research and managerial practice.