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Article

How Market Orientation Impacts Customer’s Brand Loyalty and Buying Decisions

by
Elizabeth Serra
1,2,
Mariana de Magalhães
3,4,
Rui Silva
5,* and
Galvão Meirinhos
6
1
Atlântico Business School, 4405-604 Vila Nova de Gaia, Portugal
2
Pulsar Development International, 20-22 Wenlock Road, London N1 7GU, UK
3
Faculty of Business and Law, Anglia Ruskin University, Cambridge CB1 1PT, UK
4
European Economic and Social Committee, B-1000 Brussels, Belgium
5
CETRAD (Centre for Transdisciplinary Development Studies) and NECE (Center for Studies in Business Sciences), University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
6
LABCOM-IFP, University of Trás-os-Montes e Alto Douro—UTAD, 5000-801 Vila Real, Portugal
*
Author to whom correspondence should be addressed.
J. Risk Financial Manag. 2022, 15(8), 357; https://doi.org/10.3390/jrfm15080357
Submission received: 13 July 2022 / Revised: 3 August 2022 / Accepted: 5 August 2022 / Published: 11 August 2022
(This article belongs to the Special Issue Accounting and Information Management)
Browse Figure
Review Reports Versions Notes

Abstract

As retail management has become increasingly demanding, it is imperative that retailers use market orientation to promote and increase loyalty to their private labels. This can be important in efforts to differentiate themselves from their competition. The focus of this study is to understand how these factors impact the loyalty of customer purchase decisions, through the link between the potential for brand risk and brand commitment, in order to facilitate customer orientation and brand loyalty. An online survey was conducted with a sample of 2900 consumers in Portugal and Spain. This study analyzed two distinct and high involvement product categories: Denomination of Origin (DOC) wine and anti-wrinkle cream. Structural equation modeling methodology was used to analyze the relationship between different constructs. It was found that there is no direct correlation between customer orientation and brand loyalty. However, this connection is critical when the two mediating variables of brand risk and brand commitment are accounted for. Another important finding relates to the values and differences identified between the two product categories. The results obtained show the importance of risk and commitment for high involvement products. In practice, this justifies brands explicitly managing these factors, because they can translate into loyalty behaviors. The results also contribute to demystifying the market for more complex products, particularly when the choice and risk process is more complex.

1. Introduction

PLMA (2017) defined Private label as all products for which the designation is the same as the point of sale. Furthermore, it also states that these brands are created by the producer’s brands; however, three years later, PLMA (2020) redefined it as, “Private labeled products encompass all merchandise sold under a retailer’s brand”. That brand can be the retailer’s own name, or a proprietary brand name created exclusively by that retailer. In some cases, a retailer may belong to a wholesale group that owns the brands that are only available to members of the group. There is a change in focus, as it is no longer vital to mention who the brand belongs to.
Private labels can use your brand designation, or another name created exclusively for its products. In some cases, the point of sale may belong to a distribution chain or a specific business group. Over the years these brands have had several designations that define them; however, they are always hypermarket and supermarket brands, sold only in those same stores (Sutton-Brady et al. 2017). Private brands have been gaining a market share in recent years. It should also be noted that this trend has been noticeable in recent years, particularly since 2015. Nevertheless, as PLMA noticed (PLMA 2020) “the value share of Private Labels in Europe (28%) declined (−0.5%) in 2021. The progress in 2021 is in significant contrast with the start of 2022. Unforeseen circumstances from an economic and geopolitical point of view, result in the highest inflation seen in decades. Rising food prices across European markets drives Private Label share growth in 14 out of the 18 countries, analyzed by NielsenIQ for YTD 2022”.
In Europe, Portugal (49.6%) and Spain (49.9%) are among the countries with the largest market share deeply driven by perishable foods across both markets.
Private labels provide clients with the opportunity to buy, comparing offers most often with lower prices, without quality being overlooked. For several years, private labels were smaller retail bets that only focused on increasing retailer margins. Today, these brands are a source of great value as they have become increasingly sophisticated and produce high-quality products. In fact, private labels represent the most prominent trend in modern consumption, (Dawes and Nenycz-Thiel 2013; Martinez-Ruiz et al. 2014).
According to Rubio et al. (2017), private labels bet on a diversified offer where they include products from specific categories, such as the product lines tested in this study, (DOC wine and anti-wrinkle cream). On the other hand, Mishra et al. (2020) state that marketing strategies applied to points of sale improve the private label quality perception, decreasing the associated risk. Consumer purchase goals, conscious or not, can be activated according to the brand’s focus on, and ability to be, customer-oriented. For Homburg et al. (2011), customer orientation has become a fundamental concept within the evolution of market perception.
The model proposed in this work is based on Ajzen and Fishbein’s (1972) behavioral theory, which considers customer attitudes and their relational influence on loyalty. We measured the direct and indirect impact of customer orientation on brand loyalty (Slater and Narver 1998; Ha and John 2010). The indirect relationship between customer orientation and brand loyalty is moderated by the brand risk and brand commitment.
The current research was conducted in order to understand the impact that customer orientation and the interrelationship between innovativeness and new service development has on loyalty in the context of private labels.

2. Theoretical Background and Hypotheses

Following the structure presented above and all the relevant literature, we define customer orientation as the first antecedent to customer value creation with a strong impact on the purchasing decision process (Narver and Slater 1990; Kirca et al. 2005; Ismail 2017).
Because retailers are in direct contact with their customers, it is easier for them to gain insight into market influences and preferences, plus trends, and deliver a superior value within their brand’s offers. Previous studies suggested brand loyalty was perceived as a consumer commitment to buy a product/service of the same brand despite marketing efforts, situational influences, and degree of recommendation from others (Kumar and Kumar 2020).
Market orientation is a key construct in the development of brand loyalty (Osuagwu 2019). Cognitive evaluations by customers precede emotions and behavior. When customers realize that the brand is customer oriented, they are more likely to be loyal to that brand. This relationship may be direct or indirect (Ha and John 2010; Homburg et al. 2011; Dursun and Kilic 2017; Smirnova et al. 2018).
Therefore, we propose:
H1. 
The greater the retailer’s customer orientation, the greater will be the customer’s private brand loyalty.
In another perspective, customer orientation is defined by Deshpande et al. (1993, p. 27) as a “set of believes that puts the customer’s interest first, but it does not exclude stakeholders such as owners, managers, and employees in order to develop a long-term profitable enterprise”.
Customer orientation should focus on a based knowledge marketing strategy as a source of competitive advantage. In this way, a private label knows better its clients and is able to provide a better-quality service and experience with less risk (Zhang et al. 2017; Babakus et al. 2017). In this sense, market orientation also enhances front line employees’ creativity and positively impacts in the product development (Balaji 2015; Zhang et al. 2017; Jeng 2018).
Therefore, we propose:
H2. 
The greater the retailer’s customer orientation, the greater will be the customer’s brand risk.
In addition, the commitment is mainly driven by consumers’ experience with the brand and has a strong impact on brand loyalty. Several studies (Mick and Buhl 1992; Kanten et al. 2016) showed customers are committed to the situation of use, experiences lived, and the way the brand fits consumers’ lifestyles instead of how effective it is in meeting a specific need or solving a specific problem. There is a positive relationship between customer orientation and brand commitment mostly if noticed through affective dimension (Srivastava and Owens 2010; Balaji et al. 2016). Eisingerich and Rubera (2010) mention a direct link between customer orientation and brand commitment.
Therefore, we propose:
H3. 
The greater the retailer’s customer orientation, the greater will be the customer’s commitment to the private label.
Perceived risk is commonly defined as an assessment of uncertainty regarding financial, performance, physical, psychological, and social issues, consequences of a consumption experience (Statt 1997; Solomon et al. 1999). The risk assessment when a consumer is buying a brand is considered before it is acquired (Dowling and Staelin 1994). All of these risk perceptions have substantial behavioral implications and can significantly affect purchasing decisions (Kim et al. 2008). Perceived risk is thus a challenge to retailer’s marketing strategies because consumers focus on the potential loss of resources (Salam et al. 2003).
However, information is essential in this process, because inconsistent or discordant information increases uncertainty (Stern et al. 1977). Empirical evidence (Tuu et al. 2011; Hu 2012; Lai-Ming Tam 2012; Marakanon and Panjakajornsak 2014). shows consumers choose brands that they know and feel comfortable with to minimize unnecessary risks incurred (Currás-Pérez et al. 2013). Moreover, if the risk is perceived as low enough to stimulate an initial purchase, loyalty begins to develop, encouraging the consumer to make additional purchases, further reducing risk (Bennett et al. 2005; Flavián and Guinalíu 2006; Petrovici et al. 2022). It becomes evident in the literature that there is a definitive relationship between perceived risk and brand loyalty.
Therefore, we propose:
H4. 
The greater the private label brand risk, the greater will be the customer’s loyalty.
Previous research suggests that the commitment concept is a strong factor in the customer process decision (Sargeant and Lee 2004; Raju et al. 2009). Customer commitment is a “customer’s desire to maintain a valued relationship with a brand due to previous satisfactory interactions with it” (Hsiao et al. 2015).
Research suggests commitment is a motivation linked to fostering relationships based on brand to achieve long-term benefits. It encourages pro-relational knowledge, motivations, and positive behaviors (Rusbult and Buunk 1993; Truong et al. 2017).
Thus, the customer’s repurchase intention and loyalty to a brand are affected by their level of brand commitment and a pleasing attitude (Fishbein and Ajzen 1974; Hollebeek and Chen 2014). Commitment also links with other constructs such brand attachment, wherein the customer demonstrates loyalty by purchasing a specific brand repeatedly (Park et al. 2010; Aurier and N’Goala 2010). Dariyoush and Alireza (2021) state that there is a direct and indirect relationship between brand commitment and brand loyalty.
Therefore, we propose:
H5. 
The greater a customer’s private label commitment, the greater will be the customer’s brand loyalty.
Risk perception is a very personal decision-making process affected by the consumption situations of certain product categories (Sebri and Zaccour 2017).
Research shows (O’Brien and Jones 1995; Vieira et al. 2018) a relationship between a product category’s value and brand loyalty behavior. However, the impact of customer orientation on brand loyalty may not be as direct, as suggested by Ha and John (2010). Therefore, the brand risk and brand commitment could be important mediating variables in private label management.
To understand the impact of the product category we used a high involvement condition: the DOC wines tested are associated with customer’s social contexts, and with the anti-wrinkle face cream we considered that its performance must match the brand promise and delivery of that promise. Evaluations will be positively or negatively affected when correlated with demographic, socio-economic, and ethno-cultural stereotypes of the sample used.
Therefore, we propose:
H6. 
The product category will moderate the strength of mediated relationships between market orientation and brand loyalty, namely that of brand risk (H6a) and brand commitment (H6b), in such a way that the mediated relationship will be deeper in a high-involvement product category, as opposed to a low involvement product category.
Based on the literature review, we propose the following model (Figure 1):

3. Methodology

Structural Equation Modeling (SEM) is a quantitative research analysis technique in which a multivariate model is used to measure structural relationships between both measured and latent variables. Given the characteristics and objectives of the study, we consider SEM to be the best-suited methodology.
According to Steenkamp and Baumgartner (2000), we believe that SEM is a research approach guided by principles that are of theoretical and practical value for any model development. We believe that SEM is an appropriate research approach guided by considering factors that have high theoretical and managerial value, leading them to become models. It represents a philosophy that differs significantly from that typically followed in marketing modelling (Bagozzi and Yi 1988). Marketing is composed of a set of processes; in this process, testing the theory is necessary to develop valid marketing models. Many, if not most, scientific research related, for example, to perceived benefits, attitudes, brand value, customer satisfaction, and market orientation, are composed of variables that cannot be observed directly. They can only be measured using metrics or observable indicators that vary in degree of significance and observational validity. In this sense, SEM’s uncompromising focus on the operationalization of constructs is probably the most distinctive feature and the greatest contribution to science in the marketing area. On the other hand, models are always simplified representations of reality. Therefore, based on a literature review and seeking to find new connections, we propose this model. In other words, the estimation techniques used in SEM try to minimize a function that depends on the differences between the variations and covariance implicit in the model and the observed variations and covariance. Compared to other modelling techniques, SEM is more focused on explaining marketing phenomena than predicting specific outcome variables.
The convergent validity and reliability of the scale were assessed with confirmatory factor analysis, following the guidelines of Anderson and Gerbing (1988). Convergent validity analyzes the common variation shared between the items and the latent construct (Boley et al. 2018). To establish convergent validity, the factor coefficients of the variables must be significant and greater than the minimum limit value of 0.5 (Hair et al. 2014).

3.1. Sample

This research focuses on three brands—two brands in Portugal, and one brand in Spain—to examine the same product categories (Table 1) with different degrees of social value. The study used a database of e-mail addresses from residents in Portugal and Spain to contact survey respondents. Table 1 Products and Markets tested.
In our sample, there were more responses from Portugal (60.55%) than from Spain (39.45%)—Portuguese and Spanish email databases were similar in size so there was a higher response rate from Portuguese customers.
The questionnaires were distributed to 91,394 individuals in total. The e-mails were sent via the online service provider, Mandrill; 2900 valid answers were received (Table 2).

3.2. Scales Used in Model

In our research design, we used previously validated scales to measure their strength/intensity on a seven-point Likert scale (1 = totally disagree; 7 = totally agree). Customer Orientation scale was measured using nine items adapted from Homburg et al. (2011). The brand risk scale was measured using three items adapted from Eckert et al. (2012) to analyze the perception of risk evaluation and the degree of comfort when the consumer chooses a brand. Brand Commitment was measured using three items adapted from Raju et al. (2009) to evaluate consumer’s choice behavior when the brand is available/unavailable and check if there is a real preference in the buying process. Finally, Brand Loyalty was measured using the 14 items adapted from Zehir et al. (2011).

Psychometric Characteristics of the Scales

Table 3—Descriptive Statistics: The table shows that Customer Orientation is 4.25 on a Likert Scale of 1 to 7. This can be considered a relatively low value, because Brand Risk is 5.33 on the same scale. Standard deviation is greater than 1 for all variables. The lowest value of Coefficient Variable is 2.16 and the highest is 3.06.
Table 4 shows the values related to the correlation between the different variables. There are moderate levels of correlation in the variables tested. Regarding the coefficient of variation, the lowest value of the Coefficient Variable is Brand Risk (CV = 2.16), and the highest is Brand Commitment (CV = 3.06).
One of the simplest procedures to test whether or not CMB exists in the analyzed dataset is using two different tests (Aguirre-Urreta and Hu 2019).
The results of the Harman one-factor test (Podsakoff and Organ 1986) showed that a single general factor did not account for most variance in an exploratory factor analysis (only 30.33%). This indicates that the presence of common method variance was unlikely to be significant. In the second test, based on the approach of Podsakoff et al. (2003), a model with all the observed variables loading on one factor was re-estimated. The results were unacceptable (Chi-square = 4.409; df = 365; RMSEA = 0.109; CFI: 0.850; IFI = 0.850; TLI = 0.833; LO = 0.104; HI = 0.115)); Hair et al. (2014) suggests that RMSEA values below 0.08 are considered acceptable. This suggested removal of some items from the measurement model (Table 5).

4. Analysis and Results

Taking Byrne (2010) as a principle, we tested for Mardia’s coefficient and found that it exceeded 294,502, which implies that the data may not be normally distributed. Following the recommendations by Nevitt and Hancock (2001), we made a Bollen-Stine bootstrap (B-S) on 2900 samples, to adjust the model.
Convergent validity and scale reliability were assessed using Confirmatory Factor Analysis (CFA), following the guidelines of Anderson and Gerbing (1988). Convergent validity examines how much common variance is shared between the items and the latent construct (Boley et al. 2018). The results are within conventional cut-off values (Vandenberg and Lance 2000), so the model was deemed acceptable. All constructs showed acceptable levels of composite reliability. The extracted variance considerably exceeded the levels of 0.60 and 0.50, as recommended by Bagozzi and Yi (1988). To assess convergent validity, we examined the standardized factor loadings. All items load on their specified latent variables, and each loading is large and significant, thus indicating convergent validity (Anderson and Gerbing 1988; Hair et al. 2014). Therefore, to analyze the convergent validity we used the criterion of Fornell and Larcker (1981), so we could analyze the degree of confidence we had that a trait was well measured by its indicators (see Table 6).
So, regarding the confirmatory factor analysis, more precisely the convergent validity, our estimations related to Customer Orientation (0.528 and 0.903), Brand Risk (0.878 and 0.962), Brand Commitment (0.804 and 0.964), and Brand Loyalty (0.764 and 0.872) are acceptable.
To analyze the degree of flexibility of the variations used in this study, we measured Cronbach’s Alpha, and the results were quite satisfactory for Customer Orientation (α = 0.869), Brand Risk (α= 0.900), Brand Commitment (α = 0.574), and Brand Loyalty (α = 0.910), considering the reference value of 1.
These results suggest that common method bias was not a problem in this study.
To assess discriminant validity, we observed construct inter-correlations, and the results show that they were significantly different from 1. Furthermore, the shared variance between any two constructs (square of their inter-correlations) was less than the average variance extracted, as shown in Table 7. To further confirm discriminant validity, the confidence intervals were analyzed (Table 8).

Hypothesis Testing: SEM Analyses

We conducted a SEM analysis to test our hypotheses. The variable abbreviations used are as follows: customer orientation, brand risk, brand commitment, brand loyalty. The comparison between the goodness-of-fit and the parsimony of the three alternative models shows that the most parsimonious model that best fits our data is the total mediation model (Table 9).
We do not find support for H1 (−0.074; p < 0.001), which means that the direct relationship between customer orientation and brand loyalty is negative, but the indirect relationship was supported (0.723; p < 0.001), as shown in Table 10. We found support for all other hypotheses.
The effect of the product social value was tested by comparing the goodness-of-fit for each regression. The model with the free parameter for each product category was compared to the model with the parameter restricted to an identity between social value. The fit of the model is supported in each of the categories, anti-wrinkle cream and DOC wine independently, as well as for both categories together. This analysis supported the moderation effect of (H6a) Brand Risk on Brand Loyalty and that of (H6b) Brand Commitment on Brand Loyalty for the anti-wrinkle cream category. A less significant effect was found for the category DOC wine, (H6a) Brand Risk on Brand Loyalty, and (H6b) Brand Commitment on Brand Loyalty. Thereby, it should be noted that the moderating effect of the anti-wrinkle cream category and the DOC wine category is significant, but when the anti-wrinkle cream category is considered on its own, the moderating effect is stronger (Table 11).

5. Discussion

The main contribution of this study lies in its consideration of the customer orientation of the retail chain as a precursor to brand loyalty in a causal sequence, mediated by the risk and commitment that private label brands bring about in their customers. With this research, we prove that there is no direct correlation between customer orientation and brand loyalty, and that it is verified as an indirect relationship. This effect could only be explained once the mediating effects of brand risk and brand commitment were explored. In the context of private labels, the results obtained confirm this relationship.
In fact, customer orientation allows for a direct relationship between the brand and its consumers, if this relationship is worked out strategically, it can then become increasingly closer (Hasan and Habib 2017). In this sense, the more the consumer perceives that the brand is geared towards them, the greater the impact it will have on their behaviors, namely the level of brand loyalty generation. However, this relationship may not be direct, but indirect, as suggested by Ha and John (2010). According to some authors, customer orientation allows reduction of the perception of risk and generates loyalty, and this relationship between the perceived risk and the brand loyalty is also supported by several authors (Hu 2012; Lai-Ming Tam 2012; Marakanon and Panjakajornsak 2014).
Another fundamental variable in this study is brand commitment; there is a direct relationship between customer orientation and brand commitment (Eisingerich and Rubera 2010). The direct relationship between brand commitment and brand loyalty is also supported by Dariyoush and Alireza (2021). Another important factor in this study is related to the value and its relevance in buying behaviors, in which the value (in the product categories) is relevant to the generation of brand loyalty (O’Brien and Jones 1995). Value perception and customer loyalty have a direct link and impact (Vieira et al. 2018).
In order to carry out this study, products were selected based on their characteristics. The selected product categories were DOC quality wines and anti-wrinkle cream. Another important factor in these studies is related to the moderating effect of the selected categories, leading to a conclusion based on mediating effect, brand risk, and brand commitment, which is based on the results demonstrated with the DOC wine and anti-wrinkle cream products.

6. Conclusions

The results show the adequacy of several relationships among marketing assumptions in the context of private/proprietary labels. Here, we share some inputs and implications. This work contributes to the literature on brand management by shedding new light on the customer behavior of private label brands. In practice, we observed that customer orientation is increasingly used in brand management decision making, particularly by the use of private labels.
Customer orientation is the main variable in this study, allowing us to better understand the extent to which customer orientation has an impact and exerts influence through consumer behavior in order to generate behaviors of brand loyalty. Hence and considering the first hypothesis, to measure the impact of customer orientation on brand loyalty, it was found that this relationship exists, but it is indirect.
Brand loyalty is not only perceived as a repeat purchase, but as an attitude towards the brand itself, which at its limit generates behavior, not automatic behavior, but conscious behavior in which the choice made by the consumer is effectively the best choice, i.e., the brand cares about its image and its well-being.
Results also suggest the perception of brand policy and how much it is oriented and concerned with itself; however, this relationship is only perceived if the risk of purchase is low, and the brand inspires an attitude of commitment and customer loyalty. Thus, the indirect relationship is confirmed.
Finally, and through the selected product categories, we intend to validate that product categories tested with high involvement are also those associated with a perception of high value.
In conclusion, the results of this study show how customer orientation impacts on the consumer’s long relationship with the private label, being a strategic core factor, from a brand management point of view. Thereby, the mediating role of brand risk and brand commitment is both refined and clarified. It also shows the critical nature of considering the product categories in the context of private label brands, which should be considered by researchers and practitioners when they are working on topics related with these factors, to gain higher levels of customer loyalty.
Where managerial implications are concerned, this research delivers several relevant insights for brand managers, in particular to those who work with private labels. We exhibit how important the consumer perception of retailers’ customer orientation really is, and the impact this has on the buying behaviors of the consumer.
Nevertheless, results suggest that brands should remain focused on minimizing the perception of risk that consumers feel when making purchasing decisions, as well as implementing strategies that allow consumers to have a closer connection to the brand. This connection is only possible through a commitment, a feeling of belonging to the brand, and only if it can generate loyalty.
Nevertheless, we need to notice some limitations in our study; foremost, the cultural similarity of buying behavior’s between Portugal and Spain. Future research needs to use data from other countries as well using a comparative study with a different sample size and socio-demographic characteristics.
Another suggestion would be to conduct a longitudinal study to capture the change in behavior and perceive how relationships could be modified over time. Furthermore, an applicable theory linking the open innovation and market orientation was developed, supported by several models and proposals. In this line, with implications for theory and practice, our study suggests several promising issues for future research on the positive impacts between open innovation and market orientation.
Future research must also focus on the relationship between open innovation and the degree of market orientation that encourages constant processes of innovation that lead to the insight of higher customer value.
Finally, we suggest testing the impact of incremental innovation to understand how the perception of retailer innovativeness can generate a greater market orientation and stronger loyalty to the reputation of the private label.
In fact, market-driven companies develop distinguishing outside-in/out crossing skills that encourage the matching and incorporation of internal resources with external associates. In this framework, open innovations arise as a strategic driver to preserve and expand their competitiveness.

Author Contributions

Conceptualization, E.S. and M.d.M.; methodology, R.S.; software, R.S.; validation, E.S., M.d.M. and G.M.; formal analysis, E.S.; investigation, E.S.; resources, M.d.M.; data curation, R.S.; writing—original draft preparation, E.S.; writing—review and editing, M.d.M.; visualization, M.d.M.; supervision, G.M.; project administration, G.M.; funding acquisition, G.M. All authors have read and agreed to the published version of the manuscript.

Funding

The work of author Rui Silva is supported by national funds, through the FCT—Portuguese Foundation for Science and Technology under the project UIDB/04011/2022 and by NECE-UBI, Research Centre for Business Sciences, Research Centre under the project UIDB/04630/2022.

Institutional Review Board Statement

Informed consent was obtained from all subjects involved in the study.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors gratefully acknowledge the University of Trás-os-Montes and Alto Douro and CETRAD (Centre for Transdisciplinary Development Studies); University of Beira Interior (NECE–UBI) and Anglia Ruskin University–Faculty of Business and Law–Cambridge, UK.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Aguirre-Urreta, Miguel I., and Jiang Hu. 2019. Detecting Common Method Bias: Performance of the Harman’s Single-Factor Test. Data Base Journal 50: 45–70. [Google Scholar] [CrossRef]
  2. Ajzen, Icek, and Martin Fishbein. 1972. Attitudes and normative beliefs as factors influencing behavioral intentions. Journal of Personality and Social Psychology 21: 1–9. [Google Scholar] [CrossRef]
  3. Anderson and Gerbing. 1988. Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin 103: 411–23. [Google Scholar] [CrossRef]
  4. Aurier, Philippe, and Gilles N’Goala. 2010. The differing and mediating roles of trust and relationship commitment in service relationship maintenance and development. Journal of Academic Marketing Science 38: 303–25. [Google Scholar] [CrossRef]
  5. Babakus, Emin, Ugur Yavas, and Osman Karatepe. 2017. Work engagement and turnover intentions: Correlates and customer orientation as a moderator. International Journal of Contemporary Hospitality Management 29: 1580–98. [Google Scholar] [CrossRef]
  6. Bagozzi, Richard, and Youjae Yi. 1988. On the evaluation of structural equation models. Journal of the Academy of Marketing Science 16: 74–94. [Google Scholar] [CrossRef]
  7. Balaji, Makam S. 2015. Investing in customer loyalty: The moderating role of relational characteristics. Service Business 9: 17–40. [Google Scholar] [CrossRef]
  8. Balaji, Sanjit Roy, and Khong Wei. 2016. Does relationship communication matter in B2C service relationships? Journal of Services Marketing 30: 186–200. [Google Scholar] [CrossRef]
  9. Bennett, Rebekah, Charmine Härtel, and Janet McColl-Kennedy. 2005. Experience as a moderator of involvement and satisfaction on brand loyalty in a business-to-business setting. Industrial Marketing Management 34: 97–107. [Google Scholar] [CrossRef]
  10. Boley, Bynum, EvanJordan, Carol Kline, and Whitney Knollenberg. 2018. Social return and intent to travel. Tourism Management 64: 119–28. [Google Scholar] [CrossRef]
  11. Byrne, Barbara. 2010. Structural Equation Modelling with AMOS: Basic Concepts, Applications, and Programming. Mahwah: Lawrence Erlbaum Associates. [Google Scholar] [CrossRef]
  12. Currás-Pérez, Rafael, Carla Ruiz-Mafé, and Silvia Sanz-Blas. 2013. Social network loyalty: Evaluating the role of attitude, perceived risk and satisfaction. Online Information Review 37: 61–82. [Google Scholar] [CrossRef]
  13. Dariyoush, Jamshidi, and Rousta Alireza. 2021. Brand commitment role in the relationship between brand loyalty and brand satisfaction: Phone industry Malaysia. Journal of Promotion Management 27: 151–76. [Google Scholar] [CrossRef]
  14. Dawes, John, and Magda Nenycz-Thiel. 2013. Analysing the intensity of private label competition across retailers. Journal of Business Research 66: 60–66. [Google Scholar] [CrossRef]
  15. Deshpande, Farley, and Fredericj E. Webster, Jr. 1993. Corporate culture, customer orientation, and innovativeness in Japanese firms: A quadrad analysis. Journal of Marketing 57: 23–37. [Google Scholar] [CrossRef]
  16. Dowling, Grahame, and Richard Staelin. 1994. A model of perceived risk and intended risk-handling activity. Journal of Consumer Research 21: 119–34. [Google Scholar] [CrossRef]
  17. Dursun, Turkan, and Ceyhan Kilic. 2017. Conceptualization and Measurement of Market Orientation: A Review with a Roadmap for Future Research. International Journal of Business and Management 12: 1–18. [Google Scholar] [CrossRef][Green Version]
  18. Eckert, Christine, Jordan Louviere, and Towhidul Islam. 2012. Seeing the forest despite the trees: Brand effects on choice uncertainty. International Journal of Research in Marketing 29: 256–64. [Google Scholar] [CrossRef]
  19. Eisingerich, Andreas, and Gaia Rubera. 2010. Drivers of brand commitment: A cross-national investigation. Journal of International Marketing 18: 64–79. [Google Scholar] [CrossRef]
  20. Fishbein, Martin, and Icek Ajzen. 1974. Attitudes toward objects as predictors of single and multiple behavioral criteria. Psychological Review 81: 59–74. [Google Scholar] [CrossRef]
  21. Flavián, Carlos, and Miguel Guinalíu. 2006. Consumer trust, perceived security and privacy policy: Three basic elements of loyalty to a web site. Industrial Management & Data Systems 106: 601–20. [Google Scholar] [CrossRef]
  22. Fornell, Claes, and David Larcker. 1981. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 18: 39–50. [Google Scholar] [CrossRef]
  23. Ha, Hong-Youl, and Joby John. 2010. Role of customer orientation in an integrative model of brand loyalty in services. Service Industries Journal 30: 1025–46. [Google Scholar] [CrossRef]
  24. Hair, Joseph, Tomas Hult, Christian Ringle, and Marko Sarstedt. 2014. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Los Angeles: Sage Publications. [Google Scholar]
  25. Hasan, Mostafa, and Ahsan Habib. 2017. Corporate life cycle, organizational financial resources and corporate social responsibility. Journal of Contemporary Accounting & Economics 13: 20–36. [Google Scholar]
  26. Hollebeek, Linda, and Tom Chen. 2014. Exploring positively- versus negatively-valanced brand engagement: A conceptual model. Journal of Product and Brand Management 23: 62–74. [Google Scholar] [CrossRef]
  27. Homburg, Christian, Michael Müller, and Martin Klarmann. 2011. When should the customer really be king? On the optimum level of salesperson customer orientation in sales encounters. Journal of Marketing 75: 55–74. [Google Scholar] [CrossRef]
  28. Hsiao, Chih-Hui, George Shen, and Pei-Ju Chao. 2015. How does brand misconduct affect the brand–customer relationship? Journal of Business Research 68: 862–66. [Google Scholar] [CrossRef]
  29. Hu, Yu-Jia. 2012. Exploring the relationship between perceived risk and customer involvement, brand equity and customer loyalty as mediators. The International Journal of Organizational Innovation 5: 224–30. [Google Scholar]
  30. Ismail, Ahmed Rageh. 2017. The influence of perceived social media marketing activities on brand loyalty: The mediation effect of brand and value consciousness. Asia Pacific Journal of Marketing and Logistics 29: 129–44. [Google Scholar] [CrossRef]
  31. Jeng, Shih. 2018. Enhancing the creativity of frontline employees: The effects of job complexity and customer orientation. The International Journal of Logistics Management 29: 387–408. [Google Scholar] [CrossRef]
  32. Kanten, P., S. Kanten, and M. Baran. 2016. The effect of organizational virtuousness on front line employees’ customer orientation: Role of perceptions. Paper presented at 10th International Congress on Social Sciences Conference Proceedings, Madrid, Spain, September 23–24, vol. 2, pp. 857–74. [Google Scholar]
  33. Kim, Dan J., Donald L. Ferrin, and H. Raghav Rao. 2008. A trust-based consumer decision making model in electronic commerce: The role of trust, perceived risk, and their antecedents. Decision Support Systems 44: 544–64. [Google Scholar] [CrossRef]
  34. Kirca, Ahmet, Satish Jayachandran, and William Bearden. 2005. Market orientation: A meta-analytic review and assessment of its antecedents and impact on performance. Journal of Marketing 69: 24–41. [Google Scholar] [CrossRef]
  35. Kumar, Jitender, and Vikas Kumar. 2020. Drivers of brand community engagement. Journal of Retailing and Consumer Services 54: 10–19. [Google Scholar] [CrossRef]
  36. Lai-Ming Tam, Jackie. 2012. The moderating role of perceived risk in loyalty intentions: An investigation in a service context. Marketing Intelligence and Planning 30: 33–52. [Google Scholar] [CrossRef]
  37. Marakanon, Lalinthorn, and V. Panjakajornsak. 2014. The influence of perceived risks on customer loyalty of environment-friendly electronics products in Thailand. Conference of the International Journal of Arts and Sciences 7: 209–18. [Google Scholar]
  38. Martinez-Ruiz, María Pilar, Pablo Ruiz-Palomino, Ricardo Martinez-Canas, and Juan Blazquez-Resino. 2014. Consumer satisfaction and loyalty in private-label food stores. British Food Journal 116: 849–71. [Google Scholar] [CrossRef]
  39. Mick, David Glen, and Claus Buhl. 1992. A meaning-based model of advertising experiences. Journal of Consumer Research 19: 317–38. [Google Scholar] [CrossRef]
  40. Mishra, Sita, Gunjan Malhotra, and Garima Saxena. 2020. In-store marketing of private labels: Applying cue utilization theory. International Journal of Retail & Distribution Management 49: 145–63. [Google Scholar] [CrossRef]
  41. Narver, John, and Stanley Slater. 1990. The effect of a market orientation on business profitability. Journal of Marketing 54: 20. [Google Scholar] [CrossRef]
  42. Nevitt, Jonathan, and Gregory Hancock. 2001. Performance of bootstrapping approaches to model test statistics and parameter standard error estimation in structural equation modeling. Structural Equation Modeling 8: 353–77. [Google Scholar] [CrossRef]
  43. O’Brien, Louise, and Charles Jones. 1995. Do rewards really create loyalty? Harvard Business Review 2: 75–82. [Google Scholar]
  44. Osuagwu, Caroline. 2019. Market Orientation Conceptualizations, Components and Performance-Impacts: A Literature Review and Conceptual Framework. International Journal of Marketing Studies 11: 102. [Google Scholar] [CrossRef]
  45. Park, Whan, Deborah MacInnis, Joseph Priester, Andreas Eisingerich, and Dawn Iacobucci. 2010. Brand attachment and brand attitude strength: Conceptual and empirical differentiation of two critical brand equity drivers. Journal of Marketing 74: 1–17. [Google Scholar] [CrossRef]
  46. Petrovici, Dan, Liuchen Guo, and Andrew Fearne. 2022. Perceived Risk and Private Label Purchasing Behavior: An Abstract. In Celebrating the Past and Future of Marketing and Discovery with Social Impact. AMSAC-WC 2021. Developments in Marketing Science: Proceedings of the Academy of Marketing Science. Edited by Juliann Allen, Bruna Jochims and Shuang Wu. Cham: Springer. [Google Scholar] [CrossRef]
  47. PLMA (Private Label Manufacturers Association). 2017. The Store Brands Story. Scribbr. Available online: https://plma.com/storeBrands/facts2017.html (accessed on 10 July 2021).
  48. PLMA (Private Label Manufacturers Association. 2020. Private Label Today. Scribbr. Available online: https://www.plmainternational.com/industry-news/private-label-today (accessed on 10 July 2021).
  49. Podsakoff, Philip, and Dennis Organ. 1986. Self-report in organizational research. Journal of Management 12: 531–44. [Google Scholar] [CrossRef]
  50. Podsakoff, Philip, Scott MacKenzie, Jeong Lee, and Podsakoff Nathan. 2003. Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology 88: 879–903. [Google Scholar] [CrossRef]
  51. Raju, Sekar, H. Rao Unnava, and Nicole Votolato Montgomery. 2009. The effect of brand commitment on the evaluation of nonpreferred brands: A disconfirmation process. Journal of Consumer Research 35: 851–63. [Google Scholar] [CrossRef]
  52. Rubio, Natalia, Nieves Villaseñor, and Maria Jesús Yagüe. 2017. Creation of consumer loyalty and trust in the retailer through store brands: The moderating effect of choice of store brand name. Journal of Retailing and Consumer Services 34: 358–68. [Google Scholar] [CrossRef]
  53. Rusbult, Caryl, and Buunk Bram. 1993. Commitment processes in close relationships: An interdependence analysis. Journal of Social and Personal Relationships 10: 175–204. [Google Scholar] [CrossRef]
  54. Salam, Abdus, Lindsay Ross, and Malcon Beveridge. 2003. A comparison of development opportunities for crab and shrimp aquaculture in southwestern Bangladesh, using GIS modelling. Aquaculture 220: 477–94. [Google Scholar] [CrossRef]
  55. Sargeant, Adrian, and Stephen Lee. 2004. Trust and relationship commitment in the United Kingdom voluntary sector: Determinants of donor behavior. Psychology and Marketing 21: 613–35. [Google Scholar] [CrossRef]
  56. Sebri, Mouna, and Georges Zaccour. 2017. Cross-country differences in private-label success: An exploratory approach. Journal of Business Research 80: 116–26. [Google Scholar] [CrossRef]
  57. Slater, Stanley F., and John C. Narver. 1998. Customer-led and market-oriented: Let’s not confuse the two. Strategic Management Journal 19: 1001–6. [Google Scholar] [CrossRef]
  58. Smirnova, Maria, Vera Rebiazina, and Johanna Frösén. 2018. Customer orientation as a multidimensional construct: Evidence from the Russian markets. Journal of Business Research, Elsevier 86: 457–67. [Google Scholar] [CrossRef]
  59. Solomon, Michael, Gary Bamossy, and Soren Askegaard. 1999. Consumer Bebavior: A European Perspective. Harlow: Pearson. [Google Scholar]
  60. Srivastava, Prashant, and Deborah Owens. 2010. Personality traits and their effect on brand commitment: An empirical investigation. The Marketing Management Journal 20: 15–27. [Google Scholar]
  61. Statt, David. 1997. Understanding the Consumer: A Psycbological Approacb. London: Macmillan. [Google Scholar]
  62. Steenkamp, Jan-Benedict E. M., and Hans Baumgartner. 2000. On the use of structural equation models for marketing modeling. International Journal of Research in Marketing 17: 195–202. [Google Scholar] [CrossRef]
  63. Stern, D. E., C. W. Lamb, and D. L. MacLachlan. 1977. Perceived risk: A synthesis. European Journal of Marketing 11: 312–19. [Google Scholar] [CrossRef]
  64. Sutton-Brady, Catherine, Tom Taylor, and Patty Kamvounias. 2017. Private label brands: A relationship perspective. Journal of Business & Industrial Marketing 32: 10–49. [Google Scholar] [CrossRef]
  65. Tuu, Ho Huy, Svein Ottar Olsen, and Pham Thi Thuy Linh. 2011. The moderator effects of perceived risk, objective knowledge and certainty in the satisfaction loyalty relationship. Journal of Consumer Marketing 28: 363–75. [Google Scholar] [CrossRef]
  66. Vandenberg, Robert J., and Charles E. Lance. 2000. A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods 3: 4–69. [Google Scholar] [CrossRef]
  67. Vieira, Valter, Fernando Oliveira Santini, and Clécio Falcao Araújo. 2018. A meta-analytic review of hedonic and utilitarian shopping values. Journal of Consumer Marketing 35: 426–37. [Google Scholar] [CrossRef]
  68. Truong, Yann, Richard R. Klink, Geoff Simmons, Amir Grinstein, and Mark Palmer. 2017. Branding strategies for high-technology products: The effects of consumer and product innovativeness. Journal of Business Research 70: 85–91. [Google Scholar] [CrossRef]
  69. Zehir, Cemal, Azize Şahin, Hakan Kitapçı, and Mehtap Özşahin. 2011. The effects of brand communication and service quality. In building brand loyalty through brand trust; the empirical research on global brands. Procedia Social and Behavioral Sciences 24: 1218–31. [Google Scholar] [CrossRef]
  70. Zhang, Chuang, Guijun Zhuang, Zhilin Yang, and Yang Zhang. 2017. Brand loyalty versus store loyalty: Consumers’ role in determining dependence structure of supplier–retailer dyads. Journal of Business-to-Business Marketing 24: 136–60. [Google Scholar] [CrossRef]
Jrfm 15 00357 g001
Figure 1. Research Model.
Figure 1. Research Model.
Jrfm 15 00357 g001
Table 1. Products by Markets.
Table 1. Products by Markets.
ProductPortugalSpain
Anti-wrinkle facial cream for women X
Anti-wrinkle facial cream for menX
Anti-wrinkle facial cream for women X
Anti-wrinkle facial cream for men X
DOC Douro Wine Reserve 2010X
DOC Wine 2010 X
Table 2. Sample description.
Table 2. Sample description.
Valid Sample2900
35–54 years old65.7%
Graduate degree or higher73.3%
Table 3. Descriptive Statistics.
Table 3. Descriptive Statistics.
MeanStandard DeviationCoefficient of Variation
Customer Orientation4.251.305–1.8552.72
Brand Risk5.331.365–1.5302.16
Brand Commitment4.431.712–17703.06
Brand Loyalty4.891.366–1.9652.57
Table 4. Correlation.
Table 4. Correlation.
Customer OrientationBrand RiskBrand CommitmentBrand Loyalty
Customer OrientationPearson correlation coefficient10.269 **0.192 **0.650 **
Sig. 0.0000.0000.000
Brand RiskPearson correlation coefficient0.269 **10.603 **0.861 **
Sig.0.000 0.0000.000
Brand CommitmentPearson correlation coefficient0.192 **0.603 **10.758 **
Sig.0.0000.000 0.000
Brand LoyaltyPearson correlation coefficient0.650 **0.861 **0.758 **1
Sig.0.0000.0000.000
** The correlation is significant at the 0.01% level.
Table 5. Items removed from the measure model.
Table 5. Items removed from the measure model.
Customer Orientation
The Private Label asks its customers about their specific needs
The Private Label builds its product/service offerings based on the benefits they generate for their customers
The Private Label seeks to know from its customers why they buy their products
The Private Label seeks to know why its customers do not choose products of its brand
Brand Risk
I feel that whatever I bought from the company, it would perform well
Brand Commitment
I will more likely purchase a brand that is on sale than not
Brand Loyalty
I intend to buy this brand in the near future
I want to buy other products of the Private Label
In the category of product, the Private Label brand is my first choice
I am willing to pay a higher price to buy Private Label products
I will only consider buying Private Label product if the price is low enough
I speak positively about the Private Label product
I consider the Private Label product to be my first choice in the next few years
The Private Label brand offers products that I am looking for
I always win when I buy Private Label brand products
Table 6. Confirmatory Factor Analysis: convergent validity.
Table 6. Confirmatory Factor Analysis: convergent validity.
Customer OrientationEstimate
The Private Label asks its customers about their specific needs0.528
The Private Label seeks to provide relevant information to its customers.0.722
The Private Label builds its product/service offerings0.887
The Private Label adapts its sales policies to the interests of its customers.0.903
The main offers of the Private Label seek to respond to the particularities desired by its customers.0.848
Brand Risk
I am sure about the Private Label.0.962
I know enough to feel comfortable with using the Private Label.0.878
Brand Commitment0.953
If the Private Label were not available at the store, it would make little difference to me if I had to choose another brand.0.804
I can see myself as being loyal to the Private Label.0.964
Brand Loyalty
I recommend Private Label product whenever they ask for my opinion0.846
The publicity I see on competing brands of Private Label product does not change my purchasing decision.0.872
I will continue to be a loyal customer of Private Label product.0.764
Next time I buy the product, I will buy from the Private Label brand.0.871
I intend to recommend the Private Label product to other people/friends;0.858
MODEL FIT SUMMARY
Chi-square = 11,999; df = 66; p = 0.000
CFI = 0.979; IFI = 0.979; TLI = 0.971
RMSEA = 0.062; LO = 0.058; HI = 0.065
Table 7. Composite reliability and discriminant validity.
Table 7. Composite reliability and discriminant validity.
CRAVEBrand CommitmentCustomer OrientationBrand RiskBrand Loyalty
Brand Commitment0.8800.7880.888
Customer Orientation0.8890.6240.1880.790
Brand Risk0.9180.8480.5910.2620.921
Brand Loyalty0.9250.7110.7630.2420.8550.843
Note. No squared correlations failed the Average Variance Extracted (AVE) test of discriminant validity. p < 0.05, p < 0.01.
Table 8. Interval Confidence.
Table 8. Interval Confidence.
CI
CO-BRISK0.084–0.120
CO-BCOM0.060–0.096
CO-BLOY0.077–0.113
BRISK-BLOY0.144–0.164
BCOM-BLOY0.134–0.158
Table 9. Mediation Models.
Table 9. Mediation Models.
Chi-Square
Without mediation4271.008 df = 804
Partial mediation2605.183 df = 795
Total mediation2597.020 df = 792
Table 10. Model fit summary and estimates.
Table 10. Model fit summary and estimates.
HypothesisRelationshipsStandardized Regression WeightsTestCI
H1CO-LOY−0.074Not Supported0.551–0.895
H1
Indirect effects		CO-LOY INDIRECT0.723 ***Supported
H2CO-RISK0.649 ***Supported0.573–0.961
H4RISK-LOY0.587 ***Supported0.540–0.900
H3CO-COM0.539 ***Supported0.521–0.953
H5COM-LOY0.472 ***Supported0.395–0.607
MODEL FIT SUMMARY
Chi-square = 3176, df = 789
CFI = 0.917; IFI = 0.917; TLI = 0.905; NFI = 0.884
RMSEA = 0.050
Note: * p < 0.05; ** p < 0.01; *** p < 0.001; ns = not significant.
Table 11. Moderation mediation model.
Table 11. Moderation mediation model.
HYPRelationshipsStandardized Regression Weights
Cream		Standardized Regression Weights
Wine		Test
H6aModerate category
RISK-LOY		0.7270.664Supported
p = 0.000
H6bModerate category
COM-LOY		0.5410.461Supported
p = 0.000
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MDPI and ACS Style

Serra, E.; de Magalhães, M.; Silva, R.; Meirinhos, G. How Market Orientation Impacts Customer’s Brand Loyalty and Buying Decisions. J. Risk Financial Manag. 2022, 15, 357. https://doi.org/10.3390/jrfm15080357

AMA Style

Serra E, de Magalhães M, Silva R, Meirinhos G. How Market Orientation Impacts Customer’s Brand Loyalty and Buying Decisions. Journal of Risk and Financial Management. 2022; 15(8):357. https://doi.org/10.3390/jrfm15080357

Chicago/Turabian Style

Serra, Elizabeth, Mariana de Magalhães, Rui Silva, and Galvão Meirinhos. 2022. "How Market Orientation Impacts Customer’s Brand Loyalty and Buying Decisions" Journal of Risk and Financial Management 15, no. 8: 357. https://doi.org/10.3390/jrfm15080357

APA Style

Serra, E., de Magalhães, M., Silva, R., & Meirinhos, G. (2022). How Market Orientation Impacts Customer’s Brand Loyalty and Buying Decisions. Journal of Risk and Financial Management, 15(8), 357. https://doi.org/10.3390/jrfm15080357

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