Influence Of Non-Commercial Recommendations On Store-Choice Criteria: An Empirical Study


The present study explores the impact of non-commercial recommendation sources on store attributes salience and store choice for electronics bricks-and-mortar retailing. For that purpose, an empirical exploratory quantitative study was conducted, using face-to-face interviews with a sample of 555 store actual customers. In the quantitative hypothesis testing, we correlated the store-attributes saliences with a list of non-commercial recommendation sources eventually used by the customer to choose that particular store. The results revealed several positive correlations, and each recommendation source analyzed obtained between five to thirteen positive significant correlations, out of fifteen possible. The most expressive correlations found were between the following pairs of attributes/recommendations sources: the store attribute "cozy/elegant" and the recommendation sources "other customers", "third parties" and "friends or family"; the attribute "possibility to choose between different models of one product" with the sources "friends or family", "third parties" and "other customers"; the attribute "the store carries the latest products" with the sources "other customers", "friends or family" and "third parties”.

Keywords: RetailingWord-of-mouthStore choice criteria


Individuals apply various decision heuristics in their purchase information processing tasks, because they cannot process all of the information available. An important heuristic for customers’ decisions is “word-of-mouth” (WOM), in which the decision-maker obtains external recommendations to help making a purchase decision (Duhan, Johnson, Wilcox, & Harrell, 1997).

In this context, WOM is an important topic in marketing, since it is an effective influence on consumer judgments and behaviors (Brown, Barry, Dacin, & Gunst, 2005). In fact, WOM has been found to be able to play a significant role in the dissemination of market information, assuming the form of positive, neutral or negative evaluations and recommendations (Goldenberg, Libai, & Muller, 2001). WOM has been gaining even more power and expression with the expansion of internet and social media (Dellarocas, 2003; Godes & Mayzlin, 2004; Hennig-Thurau, Gwinner, Walsh, & Gremler, 2004; Trusov, Bucklin, & Pauwels, 2009).

The present study is focused on the topic of WOM in electronics bricks and mortar retail stores, which is a specific type of retailing. Electronics bricks and mortar retail stores typically sell products with a higher customer involvement than fast-moving-consumer-goods sold by other bricks and mortar stores (e.g. supermarkets). Besides, many electronics bricks and mortar retail stores are large stores, which usually belong to stores chains with an integrated marketing and category management processes. The analyses of other types of stores different from large chain supermarkets is becoming more relevant, because the retailing formats have expanded dramatically, giving space to several types of other stores with professional marketing management (Sorescu, Frambach, Singh, Rangaswamy, & Bridges, 2011). It is also important to analyse different types of stores because the importance of each aspect of store choice varies with the kind of store the shopper intends to visit (Sinha, Banerjee, & Uniyal, 2002).

Therefore, our purpose is to investigate the impact of WOM recommendation sources on the attributes of store choice, in the particular retail setting of electronics bricks and mortar. To this end, on the next section we state the problem, elaborating upon the relevance and sources of WOM, and adding the store choice problem to this context. By doing so, we draw on the research question and purpose of the study. Next, we present the research method and the empirical findings. Last, we provide an overview of the main conclusions, managerial implications, research limitations and implications.

Problem Statement

In a broad sense, WOM “includes any information about a target object (e.g. company, brand) transferred from one individual to another either in person or via some communication medium” (Brown et al., 2005, p. 125).

The WOM valence might be positive, neutral or negative (Anderson, 1998), being able to represent a major influence on what people know, feel and do (Buttle, 1998). Many times, WOM is more influential on behaviour than other marketer-controlled sources, having the capacity to influence awareness, expectations, perceptions, attitudes, intentions and behaviors (Buttle, 1998). The reason why WOM is so powerful is because WOM is usually perceived by individuals to be more credible than brands’ initiated marketing communications. Therefore, WOM is understood as an unbiased process (Allsop, Bassett, & Hoskins, 2007). Another reason why WOM is appealing to marketers is due to the fact that WOM strategies and actions combine the prospect of overcoming consumer resistance with low costs and fast message delivery (Trusov et al., 2009).

Regarding the possible WOM recommendation sources, Duhan et al. (1997) mention that they can be categorized according to the closeness of the relationship between the decision maker and the recommendation source, ranging from strong-tie to weak-tie. Those authors state that strong-ties exist if the source is someone who knows the decision maker personally. Weak-ties are acquaintances or one entity who does not know the decision maker at all. The primary advantage of strong-tie recommendation sources is that they can they can provide information tailored to the decision maker's. On the other hand, weak-tie recommendation sources are more numerous and varied, perhaps with a greater likelihood of expertise on the product or service on evaluation.

Previous WOM studies have been essentially focused on tangible products (e.g. Bone, 1995; Godes, 2016; Yang & Mattila, 2017), hospitality and tourism (e.g. Litvin, Goldsmith, & Pan, 2008; Yang & Mattila, 2017) and digital channels (e.g. Jansen, Zhang, Sobel, & Chowdury, 2009; Babić Rosario, Sotgiu, De Valck, & Bijmolt, 2016). But WOM is an important behavioral dimension in a service setting as well (Bloemer & Odekerken-Schröder, 2002), in which retailing fits. Store choice is an important topic in retailing, and is primarily a cognitive process and a dynamic decision (Sinha et al., 2002). Understanding this decision problem is a primary concern for marketers, as well as for researchers, because it affects not only where individuals buy, but also what and how much they do (Briesch, Chintagunta, & Fox, 2009). Most of the previous research on the store choice problem has been directed to supermarket groceries stores Nilsson, Gärling, Marell, & Nordvall, 2015), representing a research opportunity and avenue to other types of stores.

In this context, store choice and store-attribute saliences form the basis for the specific reasons that consumers have for buying a product or service (Van Kenhove, De Wulf, & Van Waterschoot, 1999). The store choice problem has been studied for some decades and a considerable number of approaches have been used to determine consumer store choice (Yılmaz, Aktaş, & Celik, 2007). One approach mentioned in several studies is the attributes used by Van Kenhove and Wulf (1999): “the store is nearby”; “I can get what I want quickly”; “I know for sure that the store sells those products”; “the store has low prices”; “the store has a large enough stock of the product I want”; “the store offers service also after sales”; “I can choose between different models of one product”; “the store is cozy and elegantly designed”; “the store carries the latest products”; “the store carries products of good quality”. Yılmaz et al., (2007) developed a scale for measuring the costumer behaviour on store choice with a more comprehensive list of items: selling improvement efforts (easy payment, promotion services, discount card, bonus), sales personnel attitude (knowledge level and experience, helpfulness , neat and clean attire, cheerfulness), service (quality of cash services, meets replacement demand of the sold products, after sales services, take consumer complains into account), convenient location (closeness of store, accessibility to the market, having service vehicle), physical environment (parking facility, security services inside and outside, cleanness, proper indoor atmosphere), store reputation (friend recommendation, advertisements, brochures, image of the store in the market), greengrocer butcher services (assortment, quality, fresh products in the greengrocer and butcher departments), attractive atmosphere (ordering online or by phone, eat and drink facility at the store, atmosphere and chance for a plentiful time), characteristics of price-quality (brand variety, quality of products sold, prices), neat and order (neat and order of the departments, easy accessibility to the product. Given the considerable number of items on Yılmaz et al.’s list (2007), and due to the emergence of multi-channel retail mix, it is possible to concluded that the store choice problem is a complex decision, and has been gaining even more complexities with the internet expansion and consequent e-commerce implications (Melis, Campo, Breugelmans, & Lamey, 2015).

Given the already discussed influencing power of WOM, we expect that WOM might impact the store attributes considered by individuals when choosing which specific store to visit.

Research Questions

Based on the problem statement, the main research question to address with the present is study is;

Does non-commercial recommendation sources influence the retail store chosen by the shopper, via store-attributes salience?”

The study is directed to investigate this research question in electronics retailing stores.

Purpose of the Study

The study aims to analyze if store-attributes salience in a store choice decision context is positively influenced by non-commercial recommendations sources. Therefore, this paper is an attempt to understand to what extent store choice behaviour of shoppers is impacted by WOM sources.

In this sense, understanding the role of non-commercial recommendation sources on store choice context is important, as it may affect the retailer marketing decisions, namely the communication mix activities and goals.

Research Methods

An empirical quantitative study was conducted to address the research question stated. To that end, a semi-random sampling technique was used to interview electronics retail customers, obtaining a sample of 555 individuals. To gather the respective primary data, a structured questionnaire was used to conduct face-to-face interviews. The final sample was composed of 45% males and 66% females, and the average age of respondents was X ̅=29,53 with S=12,676.

Respondents were asked to respond on a five-point Likert scale about the importance of store choice attributes concerning the electronic store they visited most recently. Individuals were also asked to answer on a five-point Likert scale about the influence of each recommendation source for having chosen that store. The store choice attributes items were based on Van Kenhove and Wulf (1999). This list was completed with five more items identified on a two qualitative pilot exploratory focus groups. Some o these items have a correspondence with attributes mentioned by Yılmaz et al. (2007): “the store has good offers and discounts”; “the staff is nice and competent”; “the checkout is fast”; “I sympathize with the store; “I have confidence on the store”.

According to the possible groups of recommendation-source referred by Duhan et al. (1997), we have included in the questionnaire strong-tie, medium and weak-tie sources, namely: friends and family; other customers, third parties independent source.


A preliminary step taken was the analysis of store attributes-salience general importance, calculating the mean and standard deviation for each attribute (Table 1 ). Results show that all items have a mean importance higher than neutral (i.e. higher than threshold X - =3,00), revealing that all of them might play a role on the reason why the shoppers choose a specific electronics bricks and mortar retail store to visit. The most important attributes were related to:

  • Product mix: products’ quality ( X - =3,92; S=0,941), being able to sell the latest products ( X - =3,82; S=0,967) and portfolio diversity ( X - =3,64; S=1,035):

Convenience: distance to store ( X - =3,62; S=1,26), confidence that the store sell the products stock ( X - =3,82; S=0,993) and being able to quickly accomplish the shopping mission ( X - =3,75; S=0,982);

  • Confidence on the store: which is related to brand equity and retailer image, was perceived as an important item, as well ( X - =3,76; S=0,965).

Another first step taken was the analysis of the perceived general importance of each recommendation source studied. In this case, results reveal that friends or family” was perceived as the most important one ( X - = 2,84; S=1,272) followed by other customers ( X - =2,42; S=1,207) and third parties ( X - = 2,11; S=1,094).

Table 1 -
See Full Size >

To the end of answering the primary research question, a correlation matrix and respective statistical testing was computed, presented in Table 2 . From this table is noticeable that the most expressive positive significant correlations found were between the following store attributes and recommendation sources:

  • "Cozy/elegant" with the recommendation sources "other customers", "third parties" and "friends or family" (sources ordered from the higher correlation significant coefficient to the lowest);

  • "Possibility to choose between different models of one product" with the recommendation sources "friends or family", "third parties" and "other customers" (sources ordered from the higher correlation significant coefficient to the lowest);

  • "Carries the latest products" with the sources "other customers", "friends or family" and "third parties" (sources ordered from the higher correlation significant coefficient to the lowest).

The results also show that the recommendation source “friends or family” was positively correlated with the following thirteen attributes (out of all the fifteen attributes studied), from the stronger to weakest correlation: “I can choose between different models of one product”, “I have confidence on the store”, “Store carries the latest products”, “Store also offers also after sales service”, “Store carries products of good quality”, “ Store is cozy and elegantly designed”, “Staff is nice and competent”, “Store has a large enough stock of the product I want “, “Store has low prices”, “Store is nearby”, “I know for sure that the store sells those products”.

The recommendation source “other customers” was positively correlated with the following eleven attributes (from the stronger to weakest correlation): “Store carries the latest products”, “Store carries the latest products”, “I sympathize with the store”, “Staff is nice and competent”, “I have confidence on the store”, “Store carries products of good quality”, “Store also offers after sales service”, “Store has low prices”, “I can get what I want quickly”, and “Store has a large enough stock of the product I want”.

The “third parties” was positively correlated with five attributes, ranging from the stronger to weakest significant positive correlation : “Store is cozy and elegantly designed”, “I can choose between different models of one product”, “Store carries the latest products”, “I sympathize with the store” and “Store has a large enough stock of the product I want ”.

Table 2 -
See Full Size >

So, the correlations were not strong, but all the recommendation sources were positively correlated with several store choice attributes. The attributes with higher correlations were not the same among the recommendation source: in “friends and family” source, the highest correlation was with the attribute “I can choose between different models of one product”. In the source “other customers”, was “Store is cozy and elegantly designed”, just like in “third parties” source.


This study investigated the relationship between WOM recommendation sources and store choice/store-attribute saliences in the electronics bricks-and-mortar retail business.

Each one of the non-commercial recommendations sources analyzed showed, at least, five positive significant correlations with the store-attributes considered. "Friends or family" source had thirteen, "other customers" source had twelve and "third parties" had five. The correlations were not strong, but is possible to conclude that non-commercial sources might impact positively the store-attributes salience in electronics retail. So, the results show that WOM recommendation sources may have a positive impact in some store-attribute saliences. Considering the correlations’ strength, perhaps is even more important to stress that there might be a way to improve such strength. So, this might be a relevant stream of research to conduct, with direct and practical implications for managers.

The most expressive positive correlations found were between: (i) the store attribute "cozy/elegant" and the recommendation sources "other customers", "third parties" and "friends or family"; (ii) the attribute "possibility to choose between different models of one product" with the sources "friends or family", "third parties" and "other customers"; (iii) the attribute "the store carries the latest products" with the sources "other customers", "friends or family" and "third parties. Further research is suggested to assess the existence of such positive correlations between store choice and WOM recommendation sources in other retailing sectors.

As an overall conclusion, we found positives, but not strong, correlations between store choice attributes and non-commercial recommendation sources. But, just like most other empirical studies using a customer sample, this study has limitations related to that research option. Therefore, we suggest the replication of this study in other samples, to test the conclusions reached.


Authors acknowledge the publishing funding support of Polytechnic Institute of Setúbal and the help of students on conducting interviews and tabulation process.


  1. Allsop, D. T., Bassett, B. R., & Hoskins, J. A. (2007). Word-of-mouth research: principles and applications. Journal of advertising research, 47(4), 398-411.
  2. Anderson, E. W. (1998). Customer satisfaction and word of mouth. Journal of service research, 1(1), 5-17.
  3. Babić Rosario, A., Sotgiu, F., De Valck, K., & Bijmolt, T. H. (2016). The effect of electronic word of mouth on sales: A meta-analytic review of platform, product, and metric factors. Journal of Marketing Research, 53(3), 297-318.
  4. Bloemer, J. M. M., & Odekerken-Schröder, G. J. (2002). Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior, 15, 68-80.
  5. Bone, P. F. (1995). Word-of-mouth effects on short-term and long-term product judgments. Journal of business research, 32(3), 213-223.
  6. Briesch, R. A., Chintagunta, P. K., & Fox, E. J. (2009). How does assortment affect grocery store choice?. Journal of Marketing research, 46(2), 176-189.
  7. Brown, T. J., Barry, T. E., Dacin, P. A., & Gunst, R. F. (2005). Spreading the word: Investigating antecedents of consumers’ positive word-of-mouth intentions and behaviors in a retailing context. Journal of the Academy of Marketing Science, 33(2), 123-138.
  8. Buttle, F. A. (1998). Word of mouth: understanding and managing referral marketing. Journal of strategic marketing, 6(3), 241-254
  9. Dellarocas, C. (2003). The digitization of word of mouth: Promise and challenges of online feedback mechanisms. Management science, 49(10), 1407-1424.
  10. Duhan, D. F., Johnson, S. D., Wilcox, J. B., & Harrell, G. D. (1997). Influences on consumer use of word-of-mouth recommendation sources. Journal of the academy of marketing science, 25(4), 283.
  11. Godes, D., & Mayzlin, D. (2004). Using online conversations to study word-of-mouth communication. Marketing science, 23(4), 545-560.
  12. Godes, D. (2016). Product policy in markets with word-of-mouth communication. Management Science, 63(1), 267-278.
  13. Goldenberg, J., Libai, B., & Muller, E. (2001). Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing letters, 12(3), 211-223.
  14. Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion platforms: what motivates consumers to articulate themselves on the internet?. Journal of interactive marketing, 18(1), 38-52.
  15. Jansen, B. J., Zhang, M., Sobel, K., & Chowdury, A. (2009). Twitter power: Tweets as electronic word of mouth. Journal of the American society for information science and technology, 60(11), 2169-2188.
  16. Litvin, S. W., Goldsmith, R. E., & Pan, B. (2008). Electronic word-of-mouth in hospitality and tourism management. Tourism management, 29(3), 458-468.
  17. Melis, K., Campo, K., Breugelmans, E., & Lamey, L. (2015). The impact of the multi-channel retail mix on online store choice: does online experience matter? Journal of Retailing, 91(2), 272-288.
  18. Nilsson, E., Gärling, T., Marell, A., & Nordvall, A. C. (2015). Who shops groceries where and how?–the relationship between choice of store format and type of grocery shopping. The International Review of Retail, Distribution and Consumer Research, 25(1), 1-19.
  19. Sinha, P. K., Banerjee, A., & Uniyal, D. P. (2002). Deciding where to buy: Store choice behaviour of Indian shoppers. Vikalpa, 27(2), 13-28.
  20. Sorescu, A., Frambach, R. T., Singh, J., Rangaswamy, A., & Bridges, C. (2011). Innovations in retail business models. Journal of retailing, 87, S3-S16.
  21. Trusov, M., Bucklin, R. E., & Pauwels, K. (2009). Effects of word-of-mouth versus traditional marketing: findings from an internet social networking site. Journal of marketing, 73(5), 90-102.
  22. Van Kenhove, P., De Wulf, K., & Van Waterschoot, W. (1999). The impact of task definition on store-attribute saliences and store choice. Journal of Retailing, 75(1), 125-137.
  23. Yang, W., & Mattila, A. S. (2017). The impact of status seeking on consumers’ word of mouth and product preference—A comparison between luxury hospitality services and luxury goods. Journal of Hospitality & Tourism Research, 41(1), 3-22.
  24. Yılmaz, V., Aktaş, C., & Celik, H. E. (2007). Development of a scale for measuring consumer behavior in store choice. Sosyal Bilimler Dergisi,1, 171-184.

Copyright information

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

About this article

Publication Date

25 December 2020

eBook ISBN



European Publisher



Print ISBN (optional)


Edition Number

1st Edition




Cite this article as:

Silveira, P. D., & Galvão, S. (2020). Influence Of Non-Commercial Recommendations On Store-Choice Criteria: An Empirical Study. In I. Elkina, & S. Ivanova (Eds.), Cognitive - Social, and Behavioural Sciences - icCSBs 2020, vol 1. European Proceedings of Educational Sciences (pp. 244-252). European Publisher.