Organizations and Markets in Emerging Economies ISSN 2029-4581 eISSN 2345-0037
2024, vol. 15, no. 1(30), pp. 74–89 DOI: https://doi.org/10.15388/omee.2024.15.4
Vytautas Dikcius
Vilnius University, Lithuania
vytautas.dikcius@evaf.vu.lt
https://orcid.org/0000-0001-6403-2704
Karina Adomaviciute-Sakalauske (corresponding author)
Vilnius University, Lithuania
karina.adomaviciute@gmail.com
https://orcid.org/0000-0002-5963-1355
Neringa Vilkaite-Vaitone
Vilnius University, Lithuania
neringa.vilkaite-vaitone@evaf.vu.lt
Sigita Kirse
Vilnius University, Lithuania
sigita.kirse@evaf.vu.lt
https://orcid.org/0000-0003-3967-9885
Abstract. The concept of loyalty has remained a central theme in business for over 50 years. This concept has gained additional attention with the growth of online retailing due to rapidly changing retail environments. Numerous studies have focused on the a ntecedents of online store loyalty; however, unlike previous works, the present study explores a conceptual perception of loyalty. This study provides insights into behavioral and attitudinal perspectives and concentrates on the importance of the affective dimension of attitudinal loyalty. Additionally, this study analyzes differences in attitudinal loyalty toward online stores between loyal and disloyal respondents. The study was based on a survey performed in four countries—the USA, China, Spain, and Lithuania. The results highlight the importance of the affective aspect of loyalty and differences in its evaluation among the surveyed countries. The findings reveal significant and stable differences in perception of the three dimensions of attitudinal loyalty between loyal and disloyal respondents; however, in contrast to expectations, the disloyal respondents exhibited very high levels of attitudinal loyalty. These findings contribute to better measurement and interpretation of loyalty for both scientific studies and business practitioners.
Keywords: disloyalty, dimensions of attitudinal loyalty, affective loyalty, cross-culture, online retailing
Received: 29/2/2024. Accepted: 4/4/2024
Copyright © 2024 Vytautas Dikcius, Karina Adomaviciute-Sakalauske, Neringa Vilkaite-Vaitone, Sigita Kirse. Published by Vilnius University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
A steady and continuous growth of global e-commerce sales volumes was recently amplified by the COVID-19 pandemic (Kitukutha et al., 2021). This event led to an unprecedented increase in the number of internet users (and online shoppers), as many were forced to purchase online due to limited access to traditional stores caused by strict anti-pandemic regulations and a general increase in health concerns (Dahiya et al., 2021; Rexford, 2021). However, this amplification decreased following the removal of pandemic restrictions, which resulted in a restoration of global e-commerce sales volumes to close to pre-pandemic trends (Alcedo et al., 2022). This context of changing online consumer behavior highlights a crucial research gap, namely a lack of understanding of the e-store loyalty concept.
Numerous studies to date have focused on the antecedents of e-loyalty of stores. The authors of these studies most commonly evaluated the impact of satisfaction on e-loyalty (Kaya et al., 2019; Rodríguez et al., 2020; Vijay et al., 2019). Some authors also explored factors such as trust (Al-Adwan et al., 2020; Hung et al., 2019; de Matos et al., 2020), commitment (Faraoni et al., 2019; Chang et al., 2023), and other antecedents including enjoyment, perceived value, risk, and reputation (Peña-García et al., 2018; Riquelme et al., 2019).
Scientific studies have increasingly demonstrated that loyalty to online stores raises the significant attention of both marketing academics and practitioners (Dikcius et al., 2022). Many previous studies have concentrated on behavioral aspects of loyalty such as repurchase intentions (Handayani et al., 2020; Javed et al., 2020), intention to recommend (Peña-García et al., 2018), or simply behavioral loyalty (Blasco Lopez et al., 2018). In addition, many such studies have evaluated the attitudinal dimension of loyalty (Rodríguez et al., 2020) or integrated loyalty, which includes both behavioral and attitudinal aspects (Al-Adwan et al., 2020; Kaya et al., 2019; Riquelme et al., 2019). However, the affective aspect of loyalty to online stores has been almost neglected in studies to date, despite emotions playing a significant role in consumer behavior during online shopping. Thus, in the present study, we aim to address this issue by investigating whether the affective dimensions of loyalty form an important part of an overall understanding of loyalty.
Dikcius et al. (2023) found that online store loyalty-related studies had been performed in 25 countries, including China, the USA, India, South Korea, the UK, Brazil, Malaysia, Spain, and Australia. Previous studies of loyalty to online stores included respondents with various demographic characteristics, including male and female respondents, those with varying ages, income, education, and marital status, and, probably, respondents with different levels of loyalty. A systematic analysis of previous studies on online store loyalty concluded that participants’ intention to be loyal was 6.75 on a 10-point scale (Dikcius et al., 2022). These results raise further important questions—do loyal and disloyal clients express the same levels of loyalty intention, and how stable are these differences in different countries? Thus, the second aim of this study is to investigate the differences between loyal and disloyal customers in terms of their loyalty intentions toward online stores and evaluate the stability of the results within different countries.
This study aims to deepen understanding of measuring and interpreting customer loyalty in online commerce environments. Previous studies of online store loyalty primarily investigated the cognitive and conative dimensions of attitudinal loyalty, whereas the affective dimension was included quite rarely. A survey performed in four different countries—the USA, China, Spain, and Lithuania—highlighted the importance of affective loyalty for evaluating attitudinal loyalty and also emphasized its differences across the studied countries. Another important contribution of this paper relates to behavioral loyalty. Most previous studies have used loyal and disloyal respondents to measure loyalty intentions. The current study reveals substantial differences between loyal and disloyal respondents in terms of their loyalty intentions based on every dimension of attitudinal loyalty. Since these differences were quite stable across different countries, we suggest considering a customer disloyal if their attitudinal loyalty score is below five points on a seven-point scale.
The remainder of this paper is organized as follows. Section 1 reviews relevant literature on behavioral and attitudinal loyalty types and the dimensions of attitudinal loyalty, in addition to presenting the research questions for this study. Section 2 describes the research methodology in detail. Section 3 presents the results of the work and answers to the research questions, while the remaining part includes the discussion, conclusions, managerial applications, and insights for future studies.
The concept of loyalty has always been a central topic in both the academic and professional fields. This concept is often linked to ongoing customer–business relationships where the consumer prioritizes one option among all the alternatives (Toufaily et al., 2013). Although the literature investigates loyalty from a range of perspectives, the two most important are behavioral and attitudinal perspectives (Oliver, 1999; Picon et al., 2014). Some issues arise due to differing interpretations of the definition of behavioral loyalty. The primary meaning of loyalty is defined as a repeating behavior toward a certain object, e.g., buying the same brand (Romaniuk & Nenycz-Thiel, 2013) or patronizing the same store (Osman, 1993; East et al., 2000). This perception represents the behavioral perspective of loyalty. However, actual consumer behavior is hard to measure due to the inability to observe it and/or privacy concerns, especially in online environments (Toufaily et al., 2013). Therefore, the authors prefer to use the attitudinal perspective for the measurement of loyalty in scientific studies. According to this perspective, loyalty comprises three phases (dimensions)—cognitive, affective, and conative (Oliver, 1999), which are consistent with the three dimensions for measurement of attitude, i.e., cognition, affect, and behavior (Breckler, 1984). Two dimensions are the same in both models (cognitive and affective), while the last dimension (conative) causes some issues when comparing results. Some studies have measured behavioral loyalty (Cachero-Martínez & Vázquez-Casielles, 2021; East et al., 2000; Trinh et al., 2017), but most studies emphasize the measurement of conative loyalty (Alagarsamy et al., 2021; Maity & Gupta, 2016; Tankovic & Benazic, 2018) and refer to it as behavioral. Thus, we conclude that conative loyalty reflects behavioral intentions only, whereas behavioral loyalty relates to actual consumer behavior.
The attitudinal type of online store loyalty is measured using either its conative dimension or both conative and cognitive dimensions (Peña-García et al., 2018; Dikcius et al., 2022). Many previous studies have used scales, as exemplified by Zeithaml et al. (1996), Parasuraman et al., (2005), and Anderson and Srinivasan (2003). The affective dimension is rarely emphasized, and only a few recent studies have incorporated this aspect when evaluating consumer loyalty (e.g., Diallo et al., 2021; Goutam et al., 2021). In addition, the importance of the affective dimension of loyalty is mostly recognized in service-related contexts (e.g., Han et al., 2019; Li et al., 2020), whereas only some recent studies have evaluated it in a retail setting (e.g., Diallo et al., 2021). A meta-analysis performed by Liu-Thompkins et al. (2022) indicated that affective experience has become extremely important over time in retail. Thus, a key research gap still exists in distinguishing the cognitive dimension of loyalty, which is associated with the development of a liking or preference toward a company (or brand) based on satisfying usage (Ahn & Back, 2018; Yuksel et al., 2010), from the affective dimension of loyalty, which relates to strong established feelings for a specific company (or brand) (Han & Hyun, 2012). Therefore, we propose the following research question:
RQ1: What value could the affective loyalty add for the measurement of loyalty to online stores?
Another important issue that remains a research gap in loyalty studies is the difference between loyal and disloyal consumer behavior (Golf-Papez & Moolenaar, 2022). Some authors have argued that ‘disloyalty’ was not a dual continuum measurement of variable and should instead represent another dimension. Rowey and Dawes (2000) and Valvi and West (2013) argued that the opposite of satisfaction is not strictly dissatisfaction and that this should instead be another vector used for measurement; the same idea was also proposed about trust and distrust by Kramer (1999). Similarly, Slack et al. (2020) measured loyalty and disloyalty as two distinct dimensions. Nordman (2004) stated that loyalty and disloyalty were a continuum but only in the context of the behavioral type of loyalty.
Loyalty and disloyalty in retail have not been analyzed in detail in previous studies (Balabanis et al., 2006, Slack et al., 2020). However, Bhatnagar et al. (2019) named customer switching intentions as a behavioral part of disloyalty and argued that switching intentions as a form of disloyalty had been analyzed in previous retail studies. These authors attempted to prove the existence of attitudinal disloyalty; however, both behavioral and attitudinal disloyalty were measured using reversely coded statements from the loyalty scale (Bhatnagar et al., 2019). Another study of disloyalty was based on clustering of respondents into two groups based on their attitudinal loyalty (Pandey & Chawla, 2016). This measurement of disloyalty contradicts the theoretical assumption of two loyalty types (perspectives) and/or just the behavioral nature of the disloyalty.
Most previous online store loyalty studies were based on a single sample of respondents without differentiation between the two respondent sub-groups (loyal vs disloyal), and the factors determining loyalty were assessed based on the responses of all respondents (e.g., Al-Adwan et al., 2020; Hung et al., 2019; Kaya et al., 2019; Peña-García et al., 2018; Rodríguez et al., 2020). The systematic literature analysis performed by Dikcius et al. (2022) found that the respondents’ loyalty intentions were between 6.23 and 6.88 on a 10-point scale. Relatively low loyalty intentions may arise from combining loyal and disloyal respondents in one study. In addition, this may lead to misleading results since loyalty and disloyalty could potentially be impacted by different variables such as satisfaction and trust for loyalty and dissatisfaction and distrust for disloyalty (Bhatnagar et al., 2019; Shabankareh et al., 2022; Veloutsou & McAlonan, 2012). However, the mean loyalty intention calculated based on all respondents indicates that both loyal and disloyal respondents could express the intention to be loyal. Therefore, due to inconsistent previous study results, we propose the following research question:
RQ2: Do loyal consumers differ in their attitudinal loyalty toward an online store compared to disloyal consumers?
Lastly, previous studies confirmed that consumers’ loyalty to online stores may vary depending on the country or its culture (e.g., Djelassi et al., 2018; Dikcius et al., 2023; Peña-García et al., 2018). Furthermore, based on the results of a meta-analysis, Dikcius et al. (2023) confirmed that loyalty within a country may differ based on the cultural dimension. Their study found that the lower the level of uncertainty avoidance in a culture (country), the higher the customer loyalty level to an online store. The same study also revealed that people living in countries with high levels of indulgence tended to be less loyal to online stores than those who lived in countries with low levels of indulgence. Hofstede’s cultural dimensions of long-term orientation, power distance, and masculinity were also predicted in the literature to have a strong direct or indirect impact on customers’ loyalty (e.g., Luria et al., 2014; Malik & Ramay, 2017; Dikcius et al., 2023). Although previous studies have analyzed the impact of the country or, more specifically, culture, on loyalty toward online stores, these studies did not separate their respondents into loyal and disloyal cohorts. However, significant differences may exist between these two groups of respondents in terms of their loyalty to online stores. Therefore, our final proposed research question is as follows:
RQ3: Are the differences in attitudinal loyalty toward an online store between loyal and disloyal consumers stable across different countries?
In this study, a survey was performed in four countries—the USA, China, Spain, and Lithuania. The selected countries include both economically developed and developing countries with differing population sizes and levels of online retail usage. Additionally, the countries had different scores on the Hofstede Country Comparison. To be eligible, all respondents had to have made at least one online purchase during the previous six months. The respondents were asked about products that they buy in online stores most often and were requested to name their favorite online store. A total of 300 respondents from each country were selected, making the total sample size 1200 responses. Some questionnaires were excluded from the data analysis because they were either incomplete, had no variance in responses, or the respondents had taken too little time to fill them in. Therefore, the analysis was performed based on 1025 responses.
As shown in Table 1, the distribution of eligible respondents was relatively equal by country, with 234 respondents from the USA, 281 from Spain, 266 from China, and 244 from Lithuania. A slightly higher proportion of females (57%) participated in the survey compared to males. In terms of age, most respondents (59%) were from Generation Y (27–42 years old) and X (43–58 years old; 22.4%), while Generation Z (under 27 years of age) accounted for 10.5%, and Generation BB (59–77 years old) accounted for 7.6%. Since the study was performed across countries with different levels of economic development, incomes were measured subjectively—respondents were asked to evaluate their incomes relative to their country’s average. Almost a quarter of respondents answered that their income was lower than the average in their country, 31% said their incomes corresponded to the country’s average, and 45.5% stated that they had higher-than-average incomes. Most respondents indicated that in online stores, they usually buy apparel-related products (56.5%), cosmetics, perfumery, and personal hygiene products (14.4%), and household appliances and electronic goods (12.2%).
Table 1
Demographic Data of Respondents
|
Country |
% |
Generation |
% |
Gender |
% |
Monthly income after taxes |
% |
|
|
USA |
22.8 |
Z less 27 |
10.5 |
Female |
57.3 |
Lower than average in my country |
23.6 |
|
|
Spain |
27.4 |
Y 27-42 |
59.4 |
Male |
42.7 |
Corresponds to the average in my country |
30.9 |
|
|
China |
26.0 |
X 43-58 |
22.4 |
Higher than average in my country |
45.5 |
|||
|
Lithuania |
23.8 |
BB 59-77 |
7.6 |
|||||
|
Usually purchased type of products online |
% |
|||||||
|
Clothing, footwear, accessories |
56.5 |
|||||||
|
Cosmetics, perfumery, personal hygiene products |
14.4 |
|||||||
|
Household appliances, electronic goods |
12.2 |
|||||||
|
Food products |
6.8 |
|||||||
|
Games, books, leisure products |
5.3 |
|||||||
|
Products for animals |
1.7 |
|||||||
|
Furniture, interior items |
1.5 |
|||||||
|
Stationery |
0.4 |
|||||||
|
Other |
1.3 |
|||||||
|
Total |
100.0 |
|||||||
The study’s research questions highlight the need to measure both behavioral and attitudinal types of loyalty. To measure behavioral loyalty, the respondents were asked to describe their typical online purchasing behavior in the selected product category. Four possible answers were presented: 1) “I always buy products in this category only from one and the same online store”. 2) “I usually buy goods in this category only from one online store, but there are times when I have to buy from others as well”. 3) “I buy products in this category from 2–3 online stores”. 4) “I buy products in this category from 4 or more online stores”. Based on their answers to this question, the respondents were divided into two groups: “loyal”, i.e., those who selected the first or second answer categories, and “disloyal”, i.e., those who chose the third or fourth answer categories.
The findings of the survey show that a significant majority of those who shop online are more likely to return to the same online shops rather than visit other online stores for the same product category: USA (65%), Spain (66%), China (69%), and Lithuania (55%) (see Table 2). The Lithuanian respondents were the least loyal to online stores (45%), whereas the disloyalty results for the other countries were relatively similar: USA (35%), Spain (34%), and China (31%).
Table 2
Distribution of Loyal and Disloyal Respondents by Country
|
Disloyal |
Country |
Total |
|||
|
USA |
Spain |
China |
Lithuania |
||
|
35.0% |
33.8% |
31.2% |
45.1% |
36.1% |
|
|
65.0% |
66.2% |
68.8% |
54.9% |
63.9% |
|
The measurement of attitudinal loyalty was based on the scales employed by other scholars in the field to ensure consistency with previous studies. Cognitive loyalty was measured based on the statements adapted from Jin et al. (2008), while the affective loyalty scores were evaluated using the statements from Kim and Lee (2010). Finally, conative loyalty was measured based on the statements presented by Giovanis and Melanthiou (2017) and Gracia et al. (2015). All statements were measured using a seven-point Likert scale, on which 1 represented “strongly disagree” and 7 represented “strongly agree”. This scale consisted of 11 statements (see Table 3) and had high reliability (affective loyalty Cronbach α=0.848; cognitive loyalty Cronbach α= 0.868; conative loyalty Cronbach α= 0.875).
Table 3
Measurement Scales Used in the Research and their Reliability
|
Affective |
Cognitive |
Conative |
h2 |
|
|
When I need to make a purchase, this online store is my first choice. |
0.871 |
0.700 |
||
|
I believe this is my favorite online store to buy the same kind of product. |
0.917 |
0.796 |
||
|
To me, this website is the best online store to do business with. |
0.798 |
0.589 |
||
|
I love this online store. |
0.870 |
0.690 |
||
|
I am passionate about this online store. |
0.926 |
0.743 |
||
|
I’m very attached to this online store. |
0.805 |
0.661 |
||
|
I like shopping at this online store. |
0.704 |
0.587 |
||
|
I try to purchase at this online store whenever I need to make a purchase. |
0.581 |
0.601 |
||
|
As long as the present service continues, I doubt that I would switch to another online store. |
0.918 |
0.666 |
||
|
I intend to continue using this online store rather than any other one. |
0.883 |
0.695 |
||
|
I will actively search for this online store to buy from it again. |
0.580 |
0.655 |
||
|
Cronbach’s α |
0.884 |
0.868 |
0.875 |
Note. The extraction method used was principal component analysis with a Promax (with Kaiser normalization) rotation. Loadings lower than 0.4 were not presented; h2 = communality coefficient.
This study applied principal component analysis with a Promax rotation to extract factors from the 11 statements used in the questionnaire. The results of Bartlett’s test of sphericity indicated that the correlation matrix was not random, with values of χ2(55) = 7868.814, p < 0.001, and a KMO statistic of 0.944, well above the minimum threshold for conducting factor analysis. Therefore, the correlation matrix was determined suitable for factor analysis.
Table 3 presents the factor loadings, communalities, and variances explained. Four statements (0.704–0.926) defined the first factor and were related to affective loyalty to online stores. Three statements (0.798–0.917) described the second factor, i.e., cognitive loyalty. The other four statements (0.580–0.918) determined the third factor, i.e., conative loyalty to the online store. These three factors explained more than 75.7% of the total variance.
A repeated-measures analysis of variation was performed to evaluate the differences in the loyalty intentions within three types of attitudinal loyalty across all respondents (see Table 4). Mauchly’s test indicated that the assumption of sphericity had been violated (χ2 (2) = 47.39, p<.001); therefore, the degrees of freedom were corrected using the Huynh–Feldt estimates of sphericity (ε = .958). The evaluation of differences among the three attitudinal dimensions was significant: F(1.9, 1962.9) = 26.44, p<.001, and partial η2 =.025. Post-hoc pairwise comparisons with the Bonferroni adjustment indicated that there was significantly higher intention for cognitive loyalty (5.58) than for affective loyalty (5.41) (p<.001). Similarly, the intention for cognitive loyalty (5.43) was significantly higher than that for conative loyalty (5.43) (p<.001). However, the intention for affective loyalty was almost equal (5.41) to that for conative loyalty (5.43) (p = 1.00).
Table 4
Evaluation of Attitudinal Loyalty Dimensions in Different Countries
|
Loyalty types |
Countries |
|||||||||
|
All |
USA |
China |
Spain |
Lithuania |
||||||
|
M |
SD |
M |
SD |
M |
SD |
M |
SD |
M |
SD |
|
|
Cognitive loyalty |
5.58 |
1.07 |
5.91 |
0.98 |
5.86 |
0.91 |
5.37 |
1.01 |
5.19 |
1.18 |
|
Affective loyalty |
5.41 |
1.13 |
5.84 |
0.90 |
5.81 |
0.84 |
5.38 |
1.00 |
4.61 |
1.30 |
|
Conative loyalty |
5.43 |
1.07 |
5.80 |
0.90 |
5.74 |
0.89 |
5.33 |
1.05 |
4.87 |
1.16 |
|
Mauchly’s χ2 |
47.39 |
4.80 |
11.47 |
5.07 |
24.85 |
|||||
|
p |
< .001 |
.091 |
.003 |
.079 |
< .001 |
|||||
|
Huynh-Feldt ε |
.958 |
Not applied |
.966 |
Not applied |
.918 |
|||||
|
F (df) |
1.9, 1962.8 |
2, 466 |
1.9, 512.0 |
2, 560 |
1.8, 446.0 |
|||||
|
F |
26.44 |
4.40 |
5.21 |
0.65 |
35.57 |
|||||
|
p |
< .001 |
.013 |
.006 |
.524 |
< .001 |
|||||
|
η2 |
.025 |
.019 |
.019 |
.002 |
.128 |
|||||
The analysis of the differences in loyalty intentions revealed a marked difference by country (see Table 4). The results were fairly similar for respondents from the USA and China, with differences observed in the evaluation of three dimensions of attitudinal loyalty (F(2, 466) = 4.40, p = .013, partial η2 = .019 for the USA, and F(1.9, 512) = 5.21, p = .006, partial η2 = .019 for China). Post-hoc pairwise comparisons with the Bonferroni adjustment indicated that there was a significantly higher intention for cognitive loyalty (5.91) than for conative loyalty (5.80) in the case of the USA (p = .006). A similar trend was noticed for China, where a significantly higher intention for cognitive loyalty (5.86) than for conative loyalty (5.74) was recorded (p = .012). However, the difference between the affective loyalty (MUSA = 5.84; MChina = 5.81) and cognitive loyalty (MUSA = 5.91, p = .203; MChina = 5.86, p = .634) or conative loyalty (MUSA = 5.80, p = 1.00; MChina = 5.74, p = .103) was not significant for either country. In addition, no difference was identified among the three dimensions of attitudinal loyalty in the respondents from Spain (F(2, 560) = 0.65, p = .524, partial η2 < .01). In contrast to the findings from the other countries, the evaluation of all three dimensions of attitudinal loyalty was significantly different among Lithuanian respondents (F(1.8, 446) = 35.57, p < .001, partial η2 = .128). Post-hoc pairwise comparisons with the Bonferroni adjustment indicated that there was significantly higher intention for cognitive loyalty (5.19) than for conative loyalty (4.87; p < .001). Additionally, the evaluation of the intention of affective loyalty (4.61) was significantly lower than for cognitive loyalty (5.19; p < .001) or conative loyalty (4.87; p < .001). These results indicate that affective loyalty is important for understanding attitudinal loyalty and its variations between countries, confirming a positive answer to RQ1.
The second question in this study relates to differences in attitudinal loyalty between loyal and disloyal respondents. Across the whole research sample, 655 online consumers (63.9%) were classified as loyal, whereas 370 (36.1%) were classified as disloyal (see Table 2). To compare the attitudinal loyalty intentions between these two groups, a series of independent sample t-tests were performed, which revealed significant differences in cognitive, affective, and conative loyalty, as shown in Table 5. Specifically, compared to the disloyal respondents, the loyal respondents displayed higher levels of attitudinal loyalty in terms of their cognitive (Mloyal = 5.78, Mdisloyal = 5.21, t(691.3) = 8.18, p <.001, Cohen’s d = 1.03), affective (Mloyal = 5.60, Mdisloyal = 5.07, t(682.5) = 7.18, p <.001, Cohen’s d = 1.10), and conative Mloyal = 5.66, Mdisloyal = 5.03, t(630) = 8.84, p <.001, Cohen’s d = 1.03) dimensions. Cohen’s d indicated large effects in all dimensions of attitudinal loyalty. These findings prove the initial expectation that loyal respondents would have a higher intention to be loyal in the future (RQ2).
The final aspect considered related to the stability of the results across different countries. Based on the gathered data, loyal consumers had higher intentions to be loyal in terms of the three dimensions of attitudinal loyalty in all the studied countries. Cohen’s d value was higher than 0.8 in all cases, indicating large effect sizes. The effect size had the broadest value range for affective loyalty (0.81 for China and 1.28 for Lithuania), whereas Cohen’s d value ranges were smaller for the cognitive dimension (0.89 in China and 1.15 in Lithuania) and conative dimension (0.87 in China and 1.07 in Lithuania). This demonstrates the stability of the results across the four studied countries and positively answers RQ3.
Table 5
Differences in Attitudinal Loyalty Intentions Between Loyal and Disloyal Respondents
|
Countries |
Loyalty types |
Loyal |
Disloyal |
t |
p |
Cohen’s |
||
|
M |
SD |
M |
SD |
|||||
|
All |
Cognitive loyalty |
5.78 |
0.98 |
5.21 |
1.11 |
-8.18 |
<0.001 |
1.03 |
|
Affective loyalty |
5.60 |
1.05 |
5.07 |
1.20 |
-7.18 |
<0.001 |
1.10 |
|
|
Conative loyalty |
5.66 |
0.93 |
5.03 |
1.18 |
-8.84 |
<0.001 |
1.03 |
|
|
USA |
Cognitive loyalty |
6.11 |
0.79 |
5.54 |
1.18 |
-3.91 |
<0.001 |
0.94 |
|
Affective loyalty |
5.98 |
0.76 |
5.58 |
1.08 |
-2.93 |
0.002 |
0.89 |
|
|
Conative loyalty |
5.94 |
0.74 |
5.55 |
1.10 |
-2.89 |
0.002 |
0.88 |
|
|
China |
Cognitive loyalty |
6.00 |
0.85 |
5.56 |
0.97 |
-3.72 |
<0.001 |
0.89 |
|
Affective loyalty |
5.96 |
0.72 |
5.48 |
0.98 |
-4.00 |
<0.001 |
0.81 |
|
|
Conative loyalty |
5.88 |
0.82 |
5.41 |
0.97 |
-4.09 |
<0.001 |
0.87 |
|
|
Spain |
Cognitive loyalty |
5.54 |
0.93 |
5.02 |
1.09 |
-4.15 |
<0.001 |
0.98 |
|
Affective loyalty |
5.49 |
0.97 |
5.16 |
1.04 |
-2.69 |
0.004 |
0.99 |
|
|
Conative loyalty |
5.48 |
0.99 |
5.04 |
1.10 |
-3.41 |
<0.001 |
1.03 |
|
|
Lithuania |
Cognitive loyalty |
5.46 |
1.22 |
4.88 |
1.05 |
-3.93 |
<0.001 |
1.15 |
|
Affective loyalty |
4.86 |
1.34 |
4.31 |
1.19 |
-3.38 |
<0.001 |
1.28 |
|
|
Conative loyalty |
5.30 |
1.01 |
4.35 |
1.13 |
-6.87 |
<0.001 |
1.07 |
|
This analysis revealed important insights for interpreting attitudinal loyalty intentions. Mean values for the cognitive, affective, and conative dimensions were in the interval 5.0–6.0 on the seven-point scale. These results would seem natural for the loyal respondents, but the intentions to be loyal were also slightly above 5.0 for the disloyal respondents (with the exception of the Lithuanian respondents). Such a high evaluation of attitudinal loyalty among disloyal respondents could be explained by their intention to be “better” in the future. However, this does not mean that disloyal respondents will necessarily behave in this way. Especially, considering the opposite approach that has been recently discussed in marketing, indicating that behavior drives attitude (Sharp, 2010). A more realistic approach would be to move the average of a seven-point scale from a value of 4 to 5, meaning that it should be assumed that respondents with scores below five do not intend to be loyal to an online store.
This study aimed to contribute to the knowledge base regarding loyalty to online stores. This topic has received considerable interest from researchers, including studies of direct and indirect antecedents of loyalty (Liu-Thompkins et al., 2022), the importance of two dimensions of loyalty (cognitive and conative; Cachero-Martínez & Vázquez-Casielles, 2021), and differences in loyalty to online stores in different countries or cultures (Peña-García et al., 2018; Dikcius et al., 2023). However, some theoretical and practical gaps remain in the literature that must be addressed.
The findings of this study allow us to draw several main conclusions. First, the study results reveal that affective loyalty is an important dimension of loyalty. Most previous studies analyzed just one (conative) or two (cognitive and conative) dimensions of loyalty to online stores, while the affective component of consumer behavior was neglected. However, recent studies have considered the emotions related to online behavior, and it has been suggested that even classical theories such as Technology Acceptance Model or measurements such as customer engagement could be extended by adding affective elements or dimensions. This research included affective loyalty as one of the dimensions of attitudinal loyalty. The results showed the importance of this dimension and its greater similarity with conative loyalty than cognitive loyalty. These findings provide new insights into the measurement of loyalty in a digital environment and could potentially have important implications for other business areas such as services or even employee loyalty.
In addition, the analysis of three dimensions of loyalty across the four different countries highlighted that the importance of affective loyalty differed by country. Countries with high affect levels evaluated affective loyalty almost the same as behavioral loyalty; however, affective loyalty received much lower scores in the countries with lower affect levels. Lithuania, being one of the least affective countries in the world (Clifton, 2012), had very low scores on all dimensions of attitudinal loyalty and the affective dimension in particular. Therefore, the affective dimension could provide valuable information about consumer loyalty in an online commerce environment. Additionally, such results are quite important for studies related to affective objects or when responses may relate to an affective background, e.g., satisfaction, user experience, engagement, or loyalty.
Another important contribution of this study relates to the disloyalty of respondents. This work highlighted a problem related to the usage of loyal and disloyal consumers in one study. Current studies have shown the importance of attitudinal and behavioral types of loyalty for the perception of loyal and disloyal customers. Loyal customers would be expected to express a higher level of loyalty in the future, and Cohen’s d showed a large effect size. Additionally, this difference was identified in all three dimensions of loyalty, and these results were stable across the four studied countries. This average effect size raises the question of whether we should believe in the intention of disloyal clients to be loyal to online stores in the future or whether we should instead trust the strength of habitual behavior and resistance to change. Additionally, this finding opens a discussion surrounding whether attitudinal loyalty means the same for loyal and disloyal customers. In this context, attitudinal loyalty would mean using (continuing to stay with) the same online store (brand or company) for loyal customers, whereas for disloyal customers, it would mean a significant change in their behavior in which they would stop looking for other options and rely on one store (brand or company).
Finally, relatively high scores on the attitudinal intention in the case of disloyal respondents open a new discussion on the interpretation of findings. Likert-type constructs with a five- or seven-point scale yield several possibilities for researchers to determine a cut-off point. Typically, we expect that this point will be the middle value of the scale (e.g., a value of 4 on a seven-point scale from 1 to 7), and we would expect that the average scores for disloyal respondents should be below 4 (ideally between 2 and 3), whereas the value for loyal respondents should be between 5 and 6. However, the results of the current research showed that intention to be loyal is higher than 5 on a seven-point scale even among disloyal respondents. Therefore, these results suggest that we should not assume that a respondent who provides a score lower than 5 would be loyal in the future. These findings are consistent with the interpretation of Net Promoter Scores (Reichheld, 2006), where promoters are respondents with the highest (9 or 10) evaluations on a 10-point scale. This discussion highlights the need for a validation of scores that represent respondents’ attitudinal loyalty. In addition, it raises the question of whether both groups of respondents (non-loyal and loyal) should be included in a single study as some variables may impact the intention of disloyal customers to become loyal while other variables could be more important for loyal customers to continue their loyalty.
This work was supported by the Research Council of Lithuania [grant number S-MIP-21-23]
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