Pacific B usiness R eview I nternational

A Refereed Monthly International Journal of Management Indexed With THOMSON REUTERS(ESCI)
ISSN: 0974-438X
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Prof. Mahima Birla
(Editor in Chief)

Dr. Khushbu Agarwal

Ms. Asha Galundia
(Circulation Manager)

Editorial Team

Mr. Ramesh Modi

A Refereed Monthly International Journal of Management

Empirical Validation of Dimensionality of Loyalty in the Indian Context

Harsandaldeep Kaur

Assistant Professor

Department of Commerce, Guru Nanak Dev University

Amritsar, Punjab, India


Contact No.- +91-946-443-0291

Nidhi Sabharwal

Assistant Professor

PG Department of Commerce and Business Administration, Khalsa College

Amritsar, Punjab, India




Customer loyalty is important strategic objective for all managers. Loyal customers build businesses by buying more, paying premium prices and act as advocates of their products/services resulting in increased number of customers for the firm. Consensus is absent in the marketing literature on how loyalty should be conceptualized and measured. The objective of this study is to empirically test and validate four-dimension scale of loyalty that reflects Oliver’s (1997) conceptualization of a sequential loyalty chain. Data collected from 120 mobile phone users in India was analyzed using factor analysis and confirmatory factor analysis to test the proposed conceptualization of loyalty. The findings suggest existence of loyalty as a four-dimensional construct consisting of cognitive, affective, conative and action loyalty.


Since the beginning of the 1990’s, the topic of customer loyalty has gained importance both in marketing theory and practice (Odin et al. 2001; Bennett and Rundle-Thiele 2005; Bandyopadhyay and Martell 2007; Russell-Bennett et al.2007; Cahill 2007; Han, Kwortnik Jr. and Wang 2008). The increased interest in customer loyalty can be attributed to the emergence of relationship marketing paradigm (Gronross 1994; Berry 1995). The relationship marketing approach emphasizes the maintenance of mutually profitable and long-term relationships between customers and company (Ravald and Gronross

The development of long-term customer relationships has long been regarded as a valuable tool for building effective marketing strategies by marketing academics and practitioners (Reichheld and Sasser 1990; Heskett et al. 1994; Zeithmal et al. 1996; Bennett and Bove 2002; Agustin and Singh 2005). Research has shown that it is financially desirable to retain existing customers because obtaining new customers is costly (Rust and Zahorik 1993). Researchers have found a positive relationship between customer loyalty and long-term financial performance (Anderson et al. 1994; Heskett et al. 1994; Reichheld and Sasser 1990, Jones and Sasser 1995; Ryals 2002). Customer loyalty translates into benefits such as increased profits from repeat purchase and cross buying, lower marketing costs, more efficient operations and free word-of-mouth advertising (Reichheld 1996; Anderson and Mittal 2000; Colgate and Norris 2000; Castaneda 2010). Loyal customers are at the heart of a company’s most valuable group because of their current and potential future value (Ganesh et al. 2000).

Considering the positive benefits of customer loyalty, developing and increasing customer loyalty is crucial factor in company’s growth. One of the crucial issues of today is to understand how or why a sense of loyalty of develops in customers (Pritchard et al. 1999). A review of previous literature highlights a lack of agreement over the definition and operationlization of the construct of loyalty. There is no consensus in the literature on what loyalty is, and what constitutes the major driving forces of brand loyalty (Li and Petrick 2008).

The purpose of this study is to describe the development and refinement of a scale for measuring customer loyalty. The article is structured in the following way. First we provide brief overview of the various conceptualizations of loyalty. Second, we introduce Oliver’s (1997) four-stage loyalty model.Thereafter, we describe the procedure adopted to develop the loyalty scale. After presenting our results, we conclude with a discussion.

Literature Review

Customer loyalty as a concept has its origins in the 1920’s. The views on loyalty have oscillated between uni-dimensional and two-dimensional views (Russell-Bennett 2002). There are two schools of thought to define and operationalize customer loyalty: stochastic and deterministic approach. The stochastic approach defines loyalty in terms of observable behavior i.e. i.e. the pattern of past purchases (Tucker 1964; Cunningham 1956; McConnell 1968). The deterministic approach considers loyalty as an attitude and seeks to explain it in terms of attitudes, values and beliefs (Bennett and Bove 2002). Recent research in area of customer loyalty has acknowledged multidimensional view of customer loyalty, which is important, both to understand dimensions and measures of loyalty (East et al. 2005; Rundle-Thiele 2005).

Behavioral Loyalty

Behavioral approach to customer loyalty has been at the core of early marketing research. The behavioral approach suggests that the repeat purchasing of a brand over time by a consumer expresses their loyalty (Tucker 1964; McConnell 1968; Chaudhuri and Holbrook 2001). The major assumption of defining loyalty from behavioral perspective is that repeat purchasing captures the loyalty of a consumer towards the brand of interest (Bandyopadhay and Martell 2007). The most frequently used measures of behavioral loyalty are: sequence-of-purchase (Tucker 1964; McConnell 1968; Dekimpe et al. 1997); proportion-of-purchase (Cunnigham 1956; Jones and Sasser 1995; Dekimpe et al. 1997) and purchase probability measures (Javalgi and Moberg 1997). The advantage of behavioral measures are that they measure observable behaviors (Odin et al. 2001) and thus help marketers to understand how people buy primarily in markets where data is readily available (Rundle-Thiele 2005). Another advantage of behavioral measures is that they are not incidental as they are based on purchasing behavior over a period of time (Mellens et al. 1996). The behavioral measures are useful and easy to measure through panel and scanning data (Amine 1998).

A major shortcoming of loyalty measures based on repeat purchase behavior is that they make no attempt to understand the underlying repeat purchase (Dick and Basu 1994). The behavioral measurements fail to distinguish customers who buy products and service strictly for habit or convenience from those whose repeat purchase behavior is based on genuine attachment (Amine 1998; Pritchard et al. 1999; Palmer et al. 2000; Odin et al. 2001). High repeat purchase may reflect situational constraints, such as brands stocked by retailers; whereas low repeat purchase may simply indicate lack of choice, variety seeking or different usage situations etc. (Dick and Basu 1994; Mellens et al. 1996; Hart et al. 1999).

Attitudinal Loyalty

To overcome the limitations of behavioral approach, researchers have proposed measuring loyalty by means of an attitudinal dimension in addition to behavioral dimension (Day 1969; Jacoby and Kyner 1973; Srinivasan et al. 2002). The attitudinal perspective assumes that consistent buying of a brand is a necessary but not sufficient condition to ‘true’ brand loyalty and it must be complemented with a positive attitude towards this brand to ensure that this behavior will be pursued further (Amine 1998). Attitudinal measures of brand loyalty incorporate consumer preferences and dispositions toward brands to determine levels of loyalty (Javalgi and Moberg 1997). The attitudinal measurements are concerned with the sense of loyalty, engagement and allegiance (Bowen and Chen 2001). Attitudinal loyalty refers to the level of consumer’s psychological attachments and attitudinal advocacy towards the supplier (Chaudhuri and Holbrook 2001).

The attitudinal measures based on stated preferences and commitment, distinguish loyalty from repeat buying and are also less sensitive to short-run fluctuations (Mellens et al. 1996). Attitudinal loyalty measures help brand managers to understand reasons for customer’s purchase of their brands as well as those of competitors and also help to identify strengths and weaknesses of their brands (Bandyopadhay and Martell 2007). Attitudinal measures are not an accurate representation of reality as they rely on consumer declaration and not on observed behavior and it is possible that consumers may not provide true information (Mellens et al. 1996; Odin et al. 2001). Another disadvantage of attitudinal measurements is that while operationalzing attitudinal loyalty, researchers use either antecedents or consequences of loyalty (Odin et al. 2001).

Composite Loyalty

A number of researchers have stressed the need to combine behavioral and attitudinal aspects of loyalty (Day 196p; Jacoby and Kyner 1973; Bowen and Chen 2001; Back and Parks 2003). The composite approach to loyalty considers customer’s favorable attitudes, intentions and repeat purchasing as measure of true loyalty (Shoemaker and Lewis 1999; Rundle-Thiele 2005). The composite approach to loyalty claims that to be truly loyal the consumer must hold a favorable attitude toward the brand in addition to repeat purchasing it (Jensen and Hansen 2006). Dick and Basu (1994) conceptualized loyalty as the strength of the relationship between an individual’s relative attitude and their repeat patronage. Recent studies have operationalized loyalty using the composite approach (Pritchard et al. 1999; Ganesh et al. 2000; Chaudhuri and Holbrook 2001; Yi and Jeon, 2003; Rauyren and Miller 2007; Li and Petrick 2008).

Oliver’s Four Stage Loyalty Model

A number of researchers have adopted Oliver’s four-dimensional loyalty conceptualization (Oliver 1999). Oliver’s (1997) definition includes both attitudinal and behavioral aspects of loyalty. Oliver (1997) defines “a deeply held commitment to rebuy or repatronizc a preferred product/service consistently in the future, thereby causing repetitive same-brand or same brand-set purchasing, despite situational influences and marketing efforts having the potential to cause switching behavior”. According to Oliver (1999), consumers are theorized to become loyal in a cognitive sense first, then later in an affective sense, still later in a conative manner, and finally in a behavioral manner.

Cognitive Loyalty

Cognitive loyalty refers to the existence of beliefs that (typically) a brand is preferable to others (Harris and Goode 2004). At this stage, consumer loyalty is determined by information relating to the offering, such as price, quality, and so forth (Blut et al. 2007). It is the weakest type of loyalty.

Affective Loyalty

Affective loyalty reflects a favorable attitude or liking based on satisfied usage (Harris and Goode 2004). This form of loyalty relates to a favorable attitude towards a specific brand (Blut et al. 2007). The feelings, moods, or emotional responses towards the brand can be measured by collecting verbal reports or by physiological responses (Back and Parks 2003).The affective form of loyalty remains subject to switching behavior (Oliver 1999).

Conative Loyalty

Conation includes behavioral intentions or willingness to act (Back and Parks 2003). Conative loyalty implies that attitudinal loyalty must be accompanied by a desire to intend an action, for example repurchase a particular brand (Blut e al., 2007). This form of loyalty constitutes the development of behavioral intentions characterized by a deeper level of commitment (Harris and Goode 2004).

Action Loyalty

This relates to the conversion of intentions to action, accompanied by a willingness to overcome impediments to such action commitment (Harris and Goode 2004).

Research Methodology

Instrument Development

Prior to instrument development, an extensive literature survey was carried out for conceptualizing constructs and specifying their domain. An exploratory study among cell phone mobile users was undertaken to better understand the key antecedents of customer loyalty. For this purpose we conducted open-ended interviews with fifteen customers. The literature review and in-depth interviews with customers suggested a pool of 26 items to measure the various constructs.

Data was obtained through self-administered questionnaires from 250 postgraduate business students of a major university in India. In order to develop, refine and validate multi-item scales for measuring loyalty, scale development procedures were used. The methodology used to develop measures followed recommendations of Churchill (1979), Gerbing and Anderson (1988) and Saxe & Weitz (1982).

Item Generation

Prior to item generation, an extensive literature survey was carried out for conceptualizing various dimensions of loyalty. An exploratory study among cell phone mobile users was undertaken to better understand the various dimensions of customer loyalty. For this purpose we conducted open-ended interviews with fifteen customers. The literature review and in-depth interviews with customers suggested a pool of 20 items to measure the various dimensions of loyalty.

Assessment of Content Validity

The assessment of content validity serves as a pretest, permitting the deletion of items that are deemed to be conceptually inconsistent (Hinkin 1998). Content validity is ensured to the extent that expert judges agree that items are reflective of the overall construct and that these judges agree that the items are representative of the domain and facets of the construct (Netemeyer et al. 2003). A panel of three marketing judges evaluated the items for content validity and suggested the removal of some items they considered to be redundant, double-barreled and ambiguous. This process resulted in elimination of 6 items, leaving a pool of 14 items for further analysis.

Data Collection

Data was obtained through a questionnaire consisting of two parts. The first part included the items to measure various dimensions of loyalty. The second part concerns the demographic details of the respondents’ and general information regarding mobile phone connection. Table 1 reports the items used to measure various dimensions of loyalty. The sampling frame was the set of customers using mobile phone connection at the time of survey. In all 175 customers were contacted. Of the 150 completed questionnaires, 120 were usable, resulting in a response rate of 60%.


Take in Table 1 here



Sample Characteristics

Respondents were mostly male (70%) and were dominantly in the age group of 20-24 years (75 %). Regarding current provider subscription, 42.5 per cent of respondents were using Airtel followed by BSNL (15.8 %), Vodafone (15 %), IDEA (8.3 %), Reliance Communications (15.8%) and TATA Indicom (2.5%). More than sixty percent of the respondents had a prepaid mobile connection. In terms of duration of mobile phone usage, forty nine per cent of respondents were using their connection for last 4 years. Table 2 provides a summary of respondents’ demographic characteristics and general information regarding mobile phone connection.


Take in Table 2 here


Exploratory Factor Analysis

Exploratory factor analysis (EFA) was employed to identify the potential pattern of the fourteen items. Since the primary purpose of using factor analysis was data summarization, we used principal component analysis method to examine whether items in each measure loaded onto one factor or not. It was found that the fourteen items in discussion all loaded on four dimensions, reflecting Oliver’s (1997) conceptualization of sequential loyalty chain. Table 3 reports results of exploratory factor analysis.


Take in Table 3 here


Interpretation of Factors

As seen from Table 3 the variance explained by Factor 1 is 22.742%. The variables identified under this factor are V1, V12, and V13 with loadings 0.945, 0.846 and 0.905. These variables represent customer’s thoughts about the mobile phone service provider. Therefore this factor can be renamed as ‘Cognitive Loyalty’. The variance explained by Factor 2 is 18.948%. The variables identified under this factor are V8, V9, V10, and V11 with loadings of 0.845, 0.871, 0.905 and 0.574. These variables represent customer’s liking about the mobile phone service provider. Therefore this factor can be renamed as ‘Affective Loyalty’. As seen from Table 3 the variance explained by Factor 3 is 18.378%. The variables identified under this factor are V3, V4, V5, and V6 with loadings of 0.755, 0.813, 0.854 and 0.717. These variables represent customer’s willingness to act. Therefore this factor can be renamed as ‘Conative Loyalty’. As seen from Table 3 the variance explained by Factor 4 is 11.429%. The variables identified under this factor are V2 and V14 with loadings of 0.785 and 0.705. These variables represent customer’s word-of-mouth behavior. Therefore this factor can be renamed as ‘Advocacy Intentions’.

Assessment of Reliability

Coefficient alpha is most often used to test the reliability of a multi-item scale. It is concerned with the degree of interrelatedness among a set of items designed to measure a single construct (Netemeyer et al. 2003). Table 3 reports the coefficient alpha for the four subscales and all the values exceed the recommended level of 0.60, which is sufficient in exploratory stages of research (Nunnally and Bernstein1994).

Confirmatory Factor Analysis

Confirmatory factor analysis using AMOS 18.0 was used to test the measurement model.

Model Fit

The measurement showed an adequate data fit. AMOS output yielded a Chi-square value (χ2) of 91 with 71 degrees of freedom. The CMIN/DF ration was 1.309, which is within the recommended range of less than 3, which are indicative of an acceptable fit between the hypothetical model and the sample data (Carmines and McIver 1981). The goodness-of-fit index (GFI) was .908 and adjusted goodness-of- index (AGFI) was .864. The root mean square error of approximation (RMSEA) was .051, which falls within cutoff value of 0.06 (Hu and Bentler. 1999). The Tucker-Lewis Index (TLI) was .966 while the Comparative Fit Index (CFI) was .974. The Bentler-Bonett normed fit index (NFI) was .900 and Bollen's incremental fit index (IFI) was .974. The values for fit indices are shown in Table 4 and all exceed the recommended level of 0.90, supporting acceptance of the model.


Take in Table 4 here


Convergent Validity

A measure is said to possess convergent validity if independent measures of the same construct converge, or are highly correlated (Netemeyer et al. 2003). Convergent validity can be assessed from the measurement model by determining whether each indicator’s estimated pattern coefficient is on its posited underlying factor is significant or not (Anderson and Gerbing 1988). Standardized factor loadings are shown in Table 5. As can be seen all the factor loadings are significant at 0.05 significance level, which supports the convergent validity of the measures.


Take in Table 5 here


Conclusion and Recommendations

The aim of the study was to empirically validate the Oliver’s (1997) four-dimensional structure of loyalty in the Indian context. Using Factor analysis, the number of variables was reduced from 14 to 4 underlying factors. The four factors were named as Cognitive Loyalty, Affective Loyalty, Conative Loyalty and Advocacy Intentions. On the final scale consisting of four factors with 14 items we applied confirmatory factor analysis. All the model fit indices were above the recommended criteria. Reliability was assessed using coefficient alpha. Convergent validity was established for all the four constructs.

The present study makes both academic and practical contributions, and suggests several applications for the research. Our study contributes to the existing literature on relationship marketing by exploring the conceptual and operational issues related to concept of customer loyalty and identifying antecedents of customer loyalty, namely: satisfaction, trust, affective commitment, calculative commitment, corporate image and switching cost. The study makes an important contribution by studying the various constructs and establishing their reliability and validity in the Indian mobile phone service. The newly refined and validated measures can be used by future researchers to study customer loyalty and its antecedent factors. The marketing literature is saturated with importance of cultivating valuable relationships with customers, a thorough understanding of factors that build customer loyalty is beneficial for customer relationship managers to develop and reinforce marketing strategies to increase retention. For marketing practitioners, the proposed scale could be used as a tool to identify factors crucial for development of long-term relationships with customers.

Table 1: Variables Used in the Study

Item Label Item
V1 I believe that my mobile phone company offers a wide variety of services.
V2 I say positive things about my mobile connection to others.
V3 I will always choose my current mobile phone service provider in preference to competitor mobile phone companies.
V4 I intend to use more services offered by offered by my mobile phone company.
V5 I intend to remain a customer of my present mobile phone company rather than looking for a new phone service provider.
V6 I am committed to use services of my mobile phone company.
V7 I encourage friends and relatives to use services of this company.
V8 I like the performance of my current service provider.
V9 I like my mobile company much more than other comparable mobile phone companies.
V10 I like using mobile phone service from my current service provider.
V11 Using my current mobile connection gives me pleasurable experience.
V12 I believe that the overall network quality of mobile phone service provider is very good.
V13 I believe that my mobile phone company has the best offers at the moment.
V14 I encourage friends and relatives to use services of this company.

Table 2: Demographics of Respondents and Information Regarding Mobile Phone Connection

Characteristic No. of Respondents
84 (70)*
36 (30)
Under 20 years
20-24 years
25-34 years
9 (7.5)
91 (75.8)
20 (16.7)

Current Service Provider
TATA Indicom
51 (42.5)*
19 (15.8)
18 (15.0)
10 (8.3)
19 (15.8)
3 (2.5)
Type of Connection
77 (64.2)
43 (35.8)
Duration of Mobile Phone Usage
  • Less than 1 year
  • More than 1 year but less than 2 years
  • More than 2 years but less than 4 years
  • More than 4years
9 (7.5)
59 (49.2)

* Figures in parentheses denote percentages.

Table 3: Exploratory Factor Analysis and Reliability

Variable Label Factor Name Eigen Value Factor Loading Variance Explained Cronbach Alpha
Cognitive Loyalty V1 3.907 .732 22.74% .930
V12 .715
V13 .820
Affective Loyalty V8 2.647 .845 18.94% .612
V9 .871
V10 .905
V11 .574
Conative Loyalty V3 2.267 .755 18.37% .702
V4 .813
V5 .854
V6 .717
Advocacy Intentions V2 1.189 .785 11.42% .632
V7 .507
V14 .85

Table 4: Model Fit Indices of the Measurement Model

Value 493(71) 1.309 .908 .864 .974 .900 .966 .974 .051

Table 5: Parameter estimates

Latent Variables Item Label Standardized Factor Loading Critical Ratiob
Cognitive CL1 .540 4.537
Loyalty CL2 .844 4.752
CL3 .654 a
Affective AL1 .590 12.003
Loyalty AL2 .836 10.777
AL3 .928 6.788
AL4 .827 a
Conative COL1 .747 9.993
Loyalty COL2 .897 13.882
COL3 .901 13.996
C0L4 .883 a
Advocacy AI1 .815 6.2139
Intentions AI2 .880 6.124
AI3 .589 a

a Indicates a parameter fixed at 1.0 in the measurement model.

b All Critical Ratios (t-values) are significant at 0.05.


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