The hidden impact of word-of-mouth: A system dynamics approach
The hidden impact of word-of-mouth: A system dynamics approach
The method of System Dynamics is applied to study the impact of word-of mouth on sales over time. Five simulations (or experiments) are run under different assumptions. The outcomes show a significant leverage effect of WOM: Depending on Customer Involvement, Product Life Cycle and Customer Satisfaction, the market development of a New-Product-Introduction varies between 11.0 and 59.3 percent, the contribution of Classical Advertising ranges from 18.0 to 75.0 percent, and the contribution of WOM from 25.0 to 82.0 percent at the end of the time series. Possible insights for practitioners as well as scholars are discussed. WOM can be the decisive factor for both business success or failure when introducing a product into a market which calls for more attention of WOM in the daily business agenda. Furthermore, the model highlights the importance of various influences of WOM under different conditions which may support decision makers in planning their communication investments and in allocating their communication budgets wisely under these varying conditions.
One of the main functions of communication is to give both, the individual and the group, a low-risk opportunity to better understand their environment and to anticipate future alternatives for action, aka affective forecasting (e.g., Schwarz and Clore 1983, 2003) or mental simulation (e.g., Gilbert and Wilson 2007, Kraigher-Krainer 2014). In the buying context it is about better understanding decisions for or against products and services offered by companies. According to Hanna and Wozniak (2009) there are basically four sources of communication which customers may utilize in the information collection process: (1) personal experiences with companies and brands in the past by retrieval from memory – aka internal information; (2) company dominated information like advertising; (3) neutral sources like consumer reports and; (4) interpersonal communication and recommendations among customers, aka word-of-mouth (WOM). If consumer reports are not available and personal experiences do not exist or are outdated, customers depend on company information and experiences of others, the former being usually seen as more competent and more easily accessible yet less impartial than the latter (Childers and Rao 1992, Cialdini 2002, Gershoff, Broniarczyk and West 2001, Kraigher-Krainer 2014, Walker 1995, Wangenheim and Bayon 2004).
For the purpose of the paper at hand we borrow the definition of WOM and eWOM from Rosario et al. (2016, p. 297):
“In marketing, word of mouth (WOM) is the act of consumers providing information about goods, services, brands, or companies to other consumers. Such information communicated through the Internet (through, e.g., reviews, tweets, blog posts, »likes,« »pins,« images, video testimonials) is called »electronic word of mouth« (eWOM) …”
2 Word-of-mouth (wom, ewom)
The impact of Word-of-mouth (WOM) has long been underestimated if not neglected in the field of Business Communications by both, scholars and practitioners (Arndt 1967, Buttle 1997). Though silent in its nature and unspectacular compared to a national campaign, it always was and still is the foundation of many SMEs in their struggle to survive against big ad-spenders (Stokes and Lomax 2002). Bughin, Doogan, and Vetvik (2010, p. 113) estimate that word of mouth is the primary factor behind 20 to 50 percent of all purchase decisions. And Walker (1995, p. 39) reports that 46 percent of Americans rely on others’ referrals in choosing a doctor, 44 percent in selecting a mechanic, and 42 percent for obtaining legal advice. Reichheld (2004) identifies the willingness to recommend a company or a product as a key indicator for customer satisfaction and the resulting “Net Promoter Score, NPS” has become a well-established indicator of customer satisfaction and company performance (Best 2009).
With the diffusion of the internet, notably social media, spreading the word about companies and brands literally around the globe has become a matter of a mouse click – thus introducing Word-of-Mouse (Hennig-Thurau 2004) or eWOM as the new complementary phenomenon reshaping the image of companies and their brands, sometimes overnight.
As early as 1999, when the web was in a quite nascent stage, CDNow, a web-based CD-shop and the world market leader at that time, found out, that they gained 45 percent of their customers by paid ads above-the-line (radio, TV, print …) eating up 96 percent of their media budget whereas 55 percent of their clients were attracted by below-the-line-measures such as online networks, public relations, private links to their site and, most importantly, word of mouth, costing them altogether 4 percent of the media spending (Hoffmann and Novak 2000).
The huge potential of eWOM for both sales success and cost saving spread rapidly and today hardly any company does not attempt to participate in this gold rush of accessing new and young customers with comparably low advertising budgets. Correspondingly, the field of eWOM-related marketing research has lately literally exploded (Rosario et al. 2016, Ya You, Vadakkepatt and Joshi 2015) and cannot be squeezed into a fair state-of-the-art review for the purpose at hand.
We choose System Dynamics as the method for better understanding the influence of interpersonal communication on business success, as WOM is a phenomenon that happens over time (Bruce, Foutz and Kolsarici 2012) and System Dynamics (SD) is designed to look at effects over time (Sterman 2009). As such, it can simulate reinforcement effects as well as balancing effects and compute the bottom-line of these mutual influences and interactions over a given period. Our research question is: Which are the most important predictors and criterions of WOM and how do the predictors influence the criterions over time in a global SD-model?
4 Conceptualizing wom
In order to answer the research question, we first review the literature in an attempt to identify the most important drivers and consequences of WOM. WOM and eWOM have been related to many aspects which can, of course, not be modelled sufficiently. Among the discussed antecedents are product- and industry characteristics (Ya You, Vadakkepatt and Joshi 2015), exclusivity of product innovations, locked-in customers, and their price sensitivity (Peres and van den Bulte 2014), the need to belong and the individual’s level of self-disclosure (Sicilia, Delgado-Ballester, and Palazon 2016); or self-enhancement (Chawdhary and Dall’Olmo Riley 2015).
But the most frequently discussed criterions of WOM are Customer Satisfaction (e.g., Buttle 1997, Derbaix and Vanhamme 2003, Matos and Rossi 2008, Nyer and Gopinath 2005, Stephens and Gwinner 1998) and Involvement (e.g., (Berger and Schwartz 2011, Sicilia, Delgado-Ballester, and Palazon 2016, Turnbull and Meenaghan 1980, Walker 1995).
That customer satisfaction drives WOM is quite straightforward and not very surprising: Practically each textbook on customer satisfaction states somewhere that satisfied customers speak positively about the company whereas dissatisfied customers spread negative word. Customer satisfaction is, in turn, usually conceptualized as expectation disconfirmation based on schema-theory (Kraigher-Krainer 2007, 2014, Oliver 1980, Parasuraman, Zeithaml, and Berry 1985).
Furthermore, scholars hypothesize an influence of the mix of volume vs. variability of valence on the impact of WOM (Rosario et al. 2016). Usually it is also postulated that negative WOM is more likely than positive WOM and that the negative impact of negative WOM is stronger than the positive impact of positive WOM (Arndt 1967, Chawdhary and Dall’Olmo Riley 2015, Walker 1995).
Significantly less attention is put on the phenomenon that customers frequently remain silent about their positive or negative experiences and why this is. And that is where involvement comes into play with its two dimensions, perceived risk and motivation (Kraigher-Krainer 2007, 2012). Again, it is quite straightforward that WOM, sometimes also grasped as a form of herd behavior, serves the reduction of perceived risk in the purchase situation (Arndt 1967, Bauer 1960, Katona 1953), and may refer to many things such as the product itself, the company, possible shipping problems, or even loss of privacy and credit card misuse (Garbarino and Strahilevitz 2004). However, as influential as the perceived-risk dimension in understanding WOM seems to be the motivational component of involvement, as the ECID-model (Kraigher-Krainer 2007, 2012) illustrates. According to this model vivid and impactful sharing of experience with others needs a balanced mix of opinion leaders and opinion seekers. In other words: There are products which are simply too unimportant to share experiences about them in a community regardless of whether they were positive or negative. And there are products where the motivation to gain personal experience is low, the perceived risk however is high. This constitutes an opinion seeker, which is someone seeking a shortcut to a proper decision by finding competent others, usually called opinion leaders.
Correspondingly, the literature identifies moderators of WOM such as the degree of participation of opinion leaders (Peres and van den Bulte 2014), pioneers and early adopters (Arndt 1967) or, market mavens (Feick and Price 1987) in the interpersonal communication process. Furthermore, source credibility, source competence, similarity between source and receiver (Childers and Rao 1992, Cialdini 2002, Kraigher-Krainer 2014, Wangenheim and Bayon 2004, Wangenheim, Bayon, and Weber 2002) as well as social ties between sender and receiver (Baker, Donthu, and Kumar 2016) are discussed as possible moderators.
The most prominent consequences comprise attitudes to and perceptions of the brand (Buttle 1997), loyalty to the brand (Reichheld 1997, 2001, 2004), purchase intentions (Baker, Donthu, and Kumar 2016, Matos and Rossi 2008) and behaviors such as demand, purchases and purchase rates (Arndt 1967, Bruce, Foutz, and Kolsarici 2012, Bughin, Doogan, and Vetvik 2010, Buttle 1997, Rosario et al. 2016).
This pattern of behaviors, in turn, promotes the willingness to share experience and that closes the loop to the predictors of WOM. Strictly speaking, the superiority of System Dynamics over deterministic approaches like the S-O-R-model is that, in fact, many phenomena cannot be separated and assigned to predictors, mediators, moderators, or criterions, because the consequences may serve as predictors of the predictors. In other words: In loops, there are no starting points and end points. And that is why we prefer this method of investigation over a deterministic method for the research question at hand.
5 Setting up the model
The conclusions of our literature review are: Firstly, there is a significant body of literature conceptualizing (dis)satisafaction based on schema-theory and expectation disconfirmation. Secondly, there is sufficient support for the assumption that (dis)satisfaction along with involvement create (or not) WOM (for an overview see Matos and Rossi (2008), for a respective model see Derbaix and Vanhamme (2003). Hence, the components of our model for integrating WOM into the “classical” communications concept are:
Figure 1: Conceptualizing the influence of satisfaction-triggered WOM on sales and market development
1. (Dis)satisfaction as the predictor of future sales which is in turn the result of
a. expected performance of a product/service (expressed in numbers of periods the product is expected to work) minus;
b. actual performance of the product/service (expressed in numbers of periods the product actually works);
c. conceptualized this way we can also integrate possible effects of product lifetime on WOM discussed among scholars (e.g., (Berger and Schwartz 2011, Ya You, Vadakkepatt, and Joshi 2015);
2. Involvement as a moderating factor reinforcing or diminishing WOM;
3. Future sales and respective market development as the criterion.
6 Translating the model into mathematical relations
Although SD is just a set of mathematical formulas about (non)linear relations of components, SD software provides a simple way of letting the computer do these computations in discretionary granulation. We use Anylogic for this purpose. The applied assumptions and hypothesized mathematical relations between the variables are:
1. Market research has revealed 10,000 potential customers, the market potential, for a new product launch sensu “New to the World” (Trott 2013, Fig. 12.6.), that is to be introduced into the market.
2. Depending on the ratio Product Lifetime (PLT) to expected Product Lifetime (ELT) subjects are in one of four states:
a. Noncustomers (NC);
b. Satisfied Customers, (as long as the product works); it is assumed that customers recommend the product throughout their state as customers adjusted by their involvement;
c. Dissatisfied Customers (DC), if the product was expected to work longer than it did. It is assumed that disappointed customers engage in negative WOM, meaning that their negative WOM is subtracted from the positive WOM of the other customers, again adjusted by their involvement;
d. Delighted customers (if the lifetime exceeds the expected lifetime); correspondingly it is assumed that delighted customers positively contribute to WOM (thus their positive WOM add to the positive WOM of all customers), adjusted by their involvement;
e. For the sake of simplicity positive and negative WOM have the same weight and the total WOM is computed as positive WOM minus negative WOM even though literature indicates that negative WOM has a higher weight and is more likely as positive WOM, as discussed before, which would significantly amplify the results below. However, we wanted to keep the model conservative and not exaggerate the hypothesized relations;
3. Adoption from Ads:
a. The market entry starts with zero customers as we are introducing a product “new to the world”;
b. the contact rate with ads (i.e., the probability to have contact with the ads) is set to 0.5 whereby the rate is set to smooth (otherwise we would have sudden customers);
c. the conversion rate from ads is set to 0.1 meaning that one out of ten contacted Non-customers will adopt the product;
d. Example: If there are 7,000 Non-Customers, the contact rate is 0.5 and the conversion rate from Ads is 0.1 then the adoption from Ads per time unit will be 7,000 * 0.5 * 0.1 = 350 per period;
4. Adoption from WOM:
a. Depending on the product involvement, each customer shares his product experience with 1 vs. 3 market members per period: No. of talks = No. of customers * Involvement;
b. the probability that the recipient will be a Non-customer is Non-Customers/Market size;
c. adoption from WOM is therefore: NC/Market Potential*Involvement*((CU-DC)*0.1);
d. Example: If the conversion rate (the recipient can be convinced of the opinion) is again 0.1, the involvement is high (3 contacts) and we have 3,000 customers and 1,000 dissatisfied customers, then the adoption from WOM per time unit will be 6,000/10,000 * 3 * ((3,000-1,000)*0.1) = 360;
5. Product Life Time: As mentioned earlier, this variable serves two functions:
a. After the product life time (PLT) customers are going back into the Non-Customer base and have to be acquired again; this is operationalized by a delay (=PLT) in the outflow valve;
b. Together with the expected life time (ELT) this variable simulates customer satisfaction, e.g.:
i. PLT = 3 and ELT = 3 → satisfaction → Customer speaks positively for 3 time units and then becomes Non-customer;
ii. PLT = 2 and ELT = 2 → satisfaction → Customer speaks positively for 2 time units and then becomes Non-customer;
iii. PLT = 2 and ELT = 3 → dissatisfaction → Customer becomes a DC and speaks negatively about the product for 1 time unit then moves to NC.
6. The Market Development Index (MDI) is the ratio of current market demand to market potential (Best 2009, p. 75). In the case of a quasi-monopolistic situation of a company introducing a product new to the world it equates the ratio of sales to market potential. It is important to recall the above mentioned study of Peres and van den Bulte (2014), which reveals that exclusivity of product innovations may have a negative effect on WOM.
The modelling of these assumptions in Anylogic is depicted in Figure 2.
Figure 2: Modelling of the previously mentioned variables and relations in Anylogic
Five simulations (or experiments) are run under varying assumptions. Table 1 shows both, the assumptions (Involvement, PLT, ELT) and the outcomes (MDI and from where it comes):
Table 1: Assumptions and outcomes of the five simulations
The outcomes show a high leverage effect of WOM: Depending on Customer Involvement, Product Life Cycle and Customer Perceived Quality, the degree of market development of a New-Product-Introduction varies between 11.0 and 59.3 percent, the contribution of Classical Advertising varies between 18.0 and 75.0 percent, and the contribution of WOM varies between 25.0 and 82.0 percent at the end of the time series.
The presented business simulation provides some possible insights for practitioners as well as for scholars: (1) WOM can be the decisive effect for both, business success and flop when introducing a product into a market which calls for more attention to WOM in the daily business agenda. (2) The model highlights the importance of different influences of WOM under different conditions which may support decision makers in planning their communication investments and in allocating their communication budgets wisely under these varying conditions. (3) WOM is of particular importance when it comes to introducing a new product into the market (Reingen and Kernan 1986), thus emphasizing the necessity to manage WOM ( (Wirtz and Chew 2002) instead of just letting it happen.
9 Limitations and future research directions
SD is theoretical in nature. It does not depict real WOM; instead does it help simulate WOM in the laboratory over time. As such, all outcomes are bound to the goodness of the underlying model. Future studies may stress or extend the model that we used. Or they may break down the model to certain industries or even individual companies where, for example, life cycles are longer, customer perceived quality is more diffuse, customer involvement is very low or, WOM is inhibited or biased by considerations such as the protection of competitive advantages, which may be in place in some B2B purchase contexts.
Furthermore, an extended model could emphasize the relationship and interdependence between ads and WOM. Buttle (1997), for instance, identifies varying support through advertisement in terms of content and frequency at different stages of product introduction and Bruce, Foutz, and Kolsarici (2012, p. 469) hypothesize that “… ad spending is more effective at an earlier stage due to repetition wear-in and synergy with WOM, increased WOM activities at a later stage could become more powerful in driving demand.“
Furthermore, it might be insightful to validate and triangulate the model at hand through an agent-based model.
Literatúra/List of References
 Arndt, J., 1967. Role of Product-Related Conversations in the Diffusion of a New Product. In: Journal of Marketing Research. 1967, 4(3), p. 291-95. ISSN 1547-7193.
 Baker, A., Donthu, N. and Kumar, V., 2016. Investigating How Word-of-Mouth Conversations About Brands Influence Purchase and Retransmission Intentions. In: Journal of Marketing Research. 2016, 53(2), p. 225-39. ISSN 1547-7193.
 Bauer, R. A., 1960. Consumer Behavior as Risk Taking. presented in Dynamic Marketing for a Changing World, Proceedings of the American Marketing Association, Hancock, S. R. ed. Chicago: American Marketing Association, p. 389-98.
 Berger, J. and Schwartz, M. E., 2011. What Drives Immediate and Ongoing Word of Mouth? In: Journal of Marketing Research. 2011, 48(5), p. 869-80. ISSN 1547-7193.
 Best, R. J., 2009. Market-Based Management. Strategies for Growing Customer Value and Profitability. Upper Saddle River: Pearson, 2009. ISBN 9780136006060.
 Bruce, N. I., Foutz, Z. N. and Kolsarici, C., 2012. Dynamic Effectiveness of Advertising and Word of Mouth in Sequential Distribution of New Products. In: Journal of Marketing Research. 2012, 49(4), p. 469-86. ISSN 1547-7193.
 Bughin, J., Doogan, J. and Vetvik, O., 2010. A new way to measure word-of-mouth marketing. Assessing the impact of word-of-mouth marketing as well as its volume will help companies take better advantage of buzz. In: McKinsey Quarterly, 2, p. 113-16. 2010. [online]. [cit. 2017-01-17]. Available at: <https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/a-new-way-to-measure-word-of-mouth-marketing>
 Buttle, F. A., 1997. I heard it through the grape wine: Issues in referral marketing. Presented at the 5th International Colloquium in Relationship Marketing, Cranfield School of Management, Ed. Cranfield University.
 Chawdhary, R. and Dall’Olmo Riley, F., 2015. Investigating the consequences of word of mouth from a WOM sender’s perspective in the services context. In: Journal of Marketing Management. 2015, 31(9-10), p. 1018-39. ISSN 2333-6080.
 Childers, T. L. and Rao, R. A., 1992. The Influence of Familial and Peer – based Reference Groups on Consumer Decisions. In: Journal of Consumer Research. 1992, 19(2), p. 198-211. ISSN 0093-5301.
 Cialdini, R. B., 2002. Die Psychologie des Überzeugens. Ein Lehrbuch für alle, die ihren Mitmenschen und sich selbst auf die Schliche kommen wollen. Bern: Huber, 2002, ISBN 9783456838007.
 Derbaix, Ch. and Vanhamme, J., 2003. Inducing word-of-mouth by eliciting surprise – a pilot investigation. In: Journal of Economic Psychology. 2003, 24(1), p. 99-116. ISSN 0167-4870.
 Feick, L. and Price, L. L., 1987. The Market Maven: A Diffuser of Marketplace Information. In: Journal of Marketing. 1987, 51(1), p. 83-97. ISSN 0022-2429.
 Garbarino, E. C. and Strahilevitz, M., 2004. Gender differences in the perceived risk of buying online and the effects of receiving a site recommendation. In: Journal of Business Research. 2004, 57(7), p. 768-75. ISSN 0148-2963.
 Gershoff, A. D., Broniarczyk, M. S. and West, M. P., 2001. Recommendation or Evaluation? Task Sensitivity in Information Source Selection. In: Journal of Consumer Research. 2001, 28(3), p. 418-38. ISSN 0093-5301.
 Gilbert, D. T. and Wilson, D. T., 2007. Prospection: Experiencing the future. In: Science. 2007, 317(5843), p. 1351-54. ISBN 1095-9203.
 Hanna, N. and Wozniak, R., 2009. Consumer Behavior. An applied approach. Upper Saddle River: Kendall Hunt, 2009. ISBN 978-0757560347.
 Hennig-Thurau, T., 2004. Word-of-Mouse – Warum Kunden anderen Kunden im Internet zuhören. Presented in Jahrbuch der Absatz- und Verbraucherforschung, Vol. 50, GfK Nürnberg, Ed. Berlin: GfK Nürnberg, p. 52-75.
 Hoffmann, D. L. and Novak, P. T., 2000. How to acquire customers on the web. In: Harvard Business Review. 2000, 78(3), p. 179-88. ISSN 0017-8012.
 Katona, G., 1953. Rational Behavior and Economic Behavior In: Psychological Review. 1953, 60(5), 307-18. ISSN 0033-295X.
 Kraigher-Krainer, J., 2007. Das ECID-Modell. Fünf Kaufentscheidungstypen als Grundlage der strategischen Unternehmensplanung. Wiesbaden: Gabler, 2007. ISBN 978-3-8350-5499-8.
 Kraigher-Krainer, J., 2012. Habit, Affect, and Cognition: A Constructivist Model on How They Shape Decision Making in Modelling Value. Paper presented at the 1st International Conference on Value Chain Management. Jodlbauer, H., Olhager, J. and Schonberger, J. R. (Ed.). Heidelberg: Physica, 2012, p. 189-206. ISBN 978-3-7908-2746-0.
 Kraigher-Krainer, J., 2014. Güterdämmerung. Wirtschaften im Zwielicht der Profitgier. Graz: Gotthard-Verlag, 2014. ISBN 978-3950377200.
 Matos, C. A. de and Rossi, V. A. C., 2008. Word-of-mouth communications in marketing: a meta-analytic review of the antecedents and moderators. In: Journal of the Academy of Marketing Science. 2008, 36(4), p. 578-96. ISSN 0092-0703.
 Nyer, P. U. and Gopinath, M., 2005. Effects of Complaining Versus Negative Word of Mouth on Subsequent Changes in Satisfaction. The Role of Public Commitment. In: Psychology and Marketing. 2005, 22(12), p. 937-53. ISSN 1520-6793.
 Oliver, R. L., 1980. A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions. In: Journal of Marketing Research. 1980, 17(4), p. 460-69. ISSN 1547-7193.
 Parasuraman, A., Zeithaml, A. V. and Berry, L. L., 1985. A Conceptual Model of Service Quality and Its Implications for Future Research. In: Journal of Marketing. 1985, 49(4), p. 41-50. ISSN 0022-2429.
 Peres, R. and van den Bulte, Ch., 2014. When to Take or Forgo New Product Exclusivity: Balancing Protection from Competition Against Word-of-Mouth Spillover. In: Journal of Marketing. 2014, 78(2), p. 83-100. ISSN 0022-2429.
 Reichheld, F. F., 1997. Der Loyalitätseffekt, die verborgene Kraft hinter Wachstum, Gewinnen und Unternehmenswert. Frankfurt/Main: Campus, 1997. ISBN 978-3593356655.
 Reichheld, F. F., 2001. The Loyalty Effect. The Hidden Forces Behind Growth, Profits, and Lasting Value. Boston. MA: Harvard Business School Press, 2001. ISBN 978-1578516872.
 Reichheld, F. F., 2004. Mundpropaganda als Maßstab für den Erfolg. In: Harvard Business Manager. 2004, 26(3), p. 22-35. ISBN 978-3935577052.
 Reingen, P. H. and Kernan, B. J., 1986. Analysis of Referral Networks in Marketing: Methods and Illustration. In: Journal of Marketing Research. 1986, 23(4), p. 370-78. ISSN 1547-7193.
 Rosario, A. B., Sotgiu, F., d. Valck, K. and Bijmolt, T., 2016. The Effect of Electronic Word of Mouth on Sales: A Meta-Analytic Review of Platform, Product, and Metric Factors. In: Journal of Marketing Research. 2016, 53(3), p. 297-29. ISSN 1547-7193.
 Schwarz, N. and Clore, L. G., 1983. Mood, Misattribution, and Judgments of Well-Being: Informative and Directive Functions of Affective States. In: Journal of Personality and Social Psychology. 1983, 45(3), p. 513-23. ISSN 0022-3514.
 Schwarz, N. and Clore, L. G., 2003. Mood as Information: 20 Years later. In: Psychological Inquiry. 2003, 14(3/4), p. 296-303. ISSN 1532-7965.
 Sicilia, M., Delgado-Ballester, E. and Palazon, M., 2016. The need to belong and self-disclosure in positive word-of-mouth behaviours: The moderating effect of self-brand connection. In: Journal of Consumer Behaviour. 2016, 15(1), p. 60-71. ISSN 1479-1838.
 Stephens, N. and Gwinner, P. K., 1998. Why Don’t Some People Complain? A Cognitive-Emotive Process Model of Consumer Complaint Behavior. In: Journal of the Academy of Marketing Science. 1998, 26(3), p. 172-9. ISSN 0092-0703.
 Sterman, J. D., 2009. Business Dynamics. Systems Thinking and Modeling for a Complex World. Boston: Irwin/McGraw-Hill, 2009.
 Stokes, D. and Lomax, W., 2002. Taking control of word of mouth marketing: the case of an entrepreneurial hotelier In: Journal of Business and Enterprise Development. 2002, 9(4), p. 349-67. ISSN 1462-6004.
 Trott, P., 2013. Innovation Management and New Product Development. Edinburgh: Pearson Education, 2013. ISBN 9780273736578.
 Turnbull, P. W. and Meenaghan, A., 1980. Diffusion of Innovation and Opinion Leadership. In: European Journal of Marketing. 1980, 14(1), p. 3-32. ISSN 0309-0566.
 Walker, Ch., 1995. Word of mouth. In: American Demographics. 1995, 17(7), p. 38-44. ISSN 0163-4089.
 Wangenheim, F. V. and Bayon, T., 2004. The Effect of Word of Mouth on Services Switching. Measurement and moderating variables. In: European Journal of Marketing. 2004, 38(9/10), p. 1173-85. ISSN 0309-0566.
 Wangenheim, F. V., Bayon, T., and Lars Weber, 2002. Der Einfluss persönlicher Kommunikation auf Kundenzufriedenheit, Kundenbindung und Weiterempfehlungsverhalten. In: Marketing ZFP. 2002, 24(3), p. 181-94. ISSN 0344-1369.
 Wirtz, J. and Chew, P., 2002. The Effects of Incentives, Deal Proneness, Satisfaction and Tie Strength on Word-of-Mouth Behavior. In: International Journal of Service Industry Management. 2002, 13(2), p. 141-62. ISSN 0956-4233.
 Ya Y., Vadakkepatt, G. G. and Joshi, M. A., 2015. A Meta-Analysis of Electronic Word-of-Mouth Elasticity. In: Journal of Marketing. 2015, 79(2), p. 19-39. ISSN 0022-2429.
Kľúčové slová/Key Words
system dynamics approach, word-of mouth, customer involvement, product life cycle, customer satisfaction
prístup dynamiky systému, ústne podanie, zapojenie zákazníka, životný cyklus produktu, spokojnosť zákazníka
JEL klasifikácia/JEL Classification
Skrytý vplyv ústneho podania: Prístup systémovej dynamiky
Metóda systémovej dynamiky sa používa na štúdium vplyvu ústneho podania na predaj v priebehu času. Päť simulácií (alebo experimentov) sa vykonáva pod rôznymi predpokladmi. Výsledky ukazujú významný pákový efekt ústneho podania: v závislosti od angažovanosti zákazníkov, životného cyklu produktu a spokojnosti zákazníkov rozvoj trhu nového produktu predstavuje od 11,0 do 59,3%, príspevok klasickej reklamy sa pohybuje od 18,0 do 75,0% , a príspevok ústneho podania z 25,0 na 82,0% na konci časového radu. Rozoberané sú možné poznatky pre praktikov ako aj vedcov. Ústne podanie môže byť rozhodujúcim faktorom pre podnikateľský úspech alebo neúspech pri zavádzaní produktu na trh, ktorý si vyžaduje väčšiu pozornosť ústneho podania v každodennej obchodnej agende. Model ďalej vyzdvihuje dôležitosť rôznych vplyvov ústneho podania za rôznych podmienok, ktoré môžu podporiť rozhodujúce osoby pri plánovaní ich komunikačných investícií a pri rozumnom rozdeľovaní svojich komunikačných rozpočtov v týchto rozdielnych podmienkach.
Kontakt na autorov/Address
FH-Prof. Priv.Doz. Dr. Jörg Kraigher-Krainer, Degree program Global Sales and Marketing, Upper Austria University of Applied Sciences, School of Management, Wehrgrabengasse 1-3, 4400 Steyr, Austria, e-mail: [email protected]
FH-Prof. DI Dr. Margarethe Überwimmer, Degree program Global Sales and Marketing, Upper Austria University of Applied Sciences, School of Management, Wehrgrabengasse 1-3, 4400 Steyr, Austria, e-mail: [email protected]
Dr.-Ing. Yasel Costa, Degree program Global Sales and Marketing, Upper Austria University of Applied Sciences, School of Management, Wehrgrabengasse 1-3, 4400 Steyr, Austria, e-mail: [email protected]
FH-Prof. Mag. Andreas Zehetner, Degree program Global Sales and Marketing, Upper Austria University of Applied Sciences, School of Management, Wehrgrabengasse 1-3, 4400 Steyr, Austria, e-mail: [email protected]
8. marec 2017 / 8. marec 2017