Measuring customer experience in banks: scale ...

Journal of Modelling in Management Measuring customer experience in banks: scale development and validation Ruchi Garg, Zillur Rahman, M.N. Qureshi,

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Measuring customer experience in banks: scale development and validation Ruchi Garg and Zillur Rahman Department of Management, Indian Institute of Technology, Roorkee, India, and Downloaded by KING KHALID UNIVERSITY At 04:08 06 June 2017 (PT)

M.N. Qureshi

Customer experience in banks 87 Received 6 July 2012 Revised 5 December 2012 Accepted 13 February 2013

Faculty of Technology and Engineering, The M.S. University of Baroda, Vadodara, India Abstract Purpose – The paper aims to measure customer experience in Indian banks. This study examines the 14 factors of customer experience and identifies their impact on customer satisfaction. Design/methodology/approach – In this study, psychometric scale development procedure is followed comprising with the steps of item generation and selection, scale refinement and scale validation. A one-way ANOVA test is applied to identify the relationship between 14 experience factors and demographics of respondents. Findings – The findings of the study present a 41-item 14 factor reliable and valid customer experience scale among which “convenience” appears as the most significant among all the factors. Research limitations/implications – This study concentrates on a sector-specific scale, whereas a generalized scale that can be applied in other service sectors should be developed. In comparison with previous studies, the results of the current study provide a more absolute coverage and understanding of various touch points used in measuring customer experience in banks. Practical implications – By this reliable and valid scale, bank managers can identify the current and expected experiences of the customers and can build up effective strategies for the utmost satisfaction of the customers. Originality/value – To the best of the authors’ knowledge, this study represents the foremost studies for developing a validated tool to measure the experiences of banks’ customers. Keywords Customer experience, Banks, Scale development, Validation Paper type Research paper

Introduction A deliberate effort to study customer experience issues can be traced back to mid-1980s. However, the importance of this subject has acquired substantial momentum in the last two decades (Gentile et al., 2007). The reasons are that on the one hand, positive customer experience offers an opportunity for long-term competitive advantage to the firms and on the other hand, it also results in the form of satisfied and loyal customers with positive word-of-mouth, improved retention and reduced complaints. Therefore, firms in the twenty-first century have started paying attention from service-based to experience-based economy (Kim et al., 2011). Consequently, along with the compelling attention of academicians and practitioners, customer experience has now become a critical measure in support of organizational performance.

Journal of Modelling in Management Vol. 9 No. 1, 2014 pp. 87-117 q Emerald Group Publishing Limited 1746-5664 DOI 10.1108/JM2-07-2012-0023

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Now-a-days, customer experience has become the next battleground for the organizations. After commodities, goods and services; experiences acts as fourth economic offering for the organizations (Pine and Gilmore, 1998). The satisfaction-level of the customers primarily depends on their positive or negative experiences (Meyer and Schwager, 2007). These experiences play a vital role in the decision of purchasing process of the customer (Zeithmal et al., 2011). The concept of customer experience is; the resultant that depends upon the set of interactions occurs between a customer and an organization, which creates a reaction. This experience is stringently personal to an individual and affects his/her emotional, rational, physical, spiritual and sensorial levels (Gentile et al., 2007). The evaluation of this experience depends upon the comparison made by the customer between his/her expectations and the stimuli arising from the interface with the organization and its offerings in association with different instants of touch or contact points (LaSalle and Britton, 2003; Gentile et al., 2007). In the time span of last 30 years, several researchers and scholars attempted to define the term “customer experience” which have provided the better understanding of what customer experience is all about. The definitions of customer experience are summarized in Table I. The crux of all these definitions is that, first customer experience is an emotional connection between the customer and the organization. Second, it is completely internal to a customer. Last, it largely depends on the moments of contact. But, this non-figurative and elusive notion (Knutson et al., 2007) of customer experience arises some questions as, what are the factors of customer experience? How customer experience can be measured? Does customer experience affect customer satisfaction or not? These questions led to the development of the managerially functional and psychometrically sound instrument which aims to measure customer experience. To develop, test and validate a measurement scale, Indian retail banking sector has been considered as a case in point. It is because a lack of such type of instrument has been found in the existing body of literature, which can be used for measuring the banking experiences of the customers. So, this study attempts to fill this major research gap, by developing a scale which will assess the experiences of the retail banks’ customers. For such purpose, this paper is organized into five major sections. First, the prior literature that has been carried out in this arena has been reviewed. Second, the factors of customer experience used in this scale development process have been discussed. Third, a standard scale development procedure is applied to develop the scale. In the fourth section, the effect of customer experience on customer satisfaction has been assessed. In the last section, the study has been concluded with the discussion of the result’s implications along with the limitations and directions for future research. Background and review of literature In the growing body of existing literature, researchers have developed various sector-specific and generalized scales for the measurement of customer experience. These scales have been used to measure customer experience in different application areas. As Otto and Ritchie (1996) developed a six-dimensional (hedonic, interactive, novelty, comfort, safety and stimulation) scale for measuring the tourism experience of customers. Novak et al. (2000) proposed an instrument in order to measure customer experience in online environment with constructs as web usage, arousal, challenge, control, exploratory behaviour, flow, focused attention, interactivity, involvement,

References

Definitions of customer experience

Holbrook and Hirschman (1982) Carbone and Haeckel (1994)

Consumption experience has been seen “a steady flow of fantasies, feelings, and fun” The experiences are a “take-away impression form by people’s encounters with products, services, and businesses – a perception produced when humans consolidate sensory information” During a service encounter, experience can be defined as “the subjective mental state felt by participants” “Experiences are a distinct economic offering, as different from services as services are from goods. An experience occurs when a company intentionally uses services as the stage, and goods as props, to engage individual customers in a way that creates a memorable event” Experiences are the “result of encountering, undergoing, or living through situations. They are triggered stimulations to the senses, the heart, and the mind. Experiences also connect the company and the brand to the customer’s lifestyle and place individual customer actions and the purchase occasion in a broader social context. In sum, experiences provide sensory, emotional, cognitive, behavioral, and relational values that replace functional values” “Experience is specific knowledge that has been acquired by and agent during past problem solving. Experience is therefore always situated in a certain, very specific problem solving context. Therefore, experiences are stored knowledge” “Experience is an emergent phenomenon. It is the outcome of participation in a set of activities within a social context” Consumer experience is “the total outcome to the customer from the combination of environment, goods and services purchased” “A total customer experience is a consistent representation and flawless execution, across distribution channels and interaction points, of the emotional connection and relationship you want your customers to have with your brand” “By ‘total experience’ we mean the feelings customers take away from their interaction with a firm’s goods, services, and ‘atmospheric’ stimuli” “Total customer experience emphasises the importance of all contacts that a consumer has with an organisation and the consumer’s holistic experience” Customer experience acts as “an engaging act of co-creation between a provider and a consumer wherein the consumer perceives value in the encounter and in the subsequent memory of that encounter” “A brand is the sum of the customer’s experiences with the product of a company [. . .] An effective customer experience programme analyses rich customer feedback to determine not just what customers say, but also what they do” “A customer experience is an interaction between an organization and a customer. It is a blend of an organization’s physical performance, the senses stimulated and emotions evoked, each intuitively measured against customer expectations across all moments of contact” (continued)

Otto and Ritchie (1996)


Pine and Gilmore (1998)

Schmitt (1999)

Bergmann (1999)

Gupta and Vajic (2000) Lewis and Chambers (2000) Seybold et al. (2001)

Haeckel et al. (2003) Harris et al. (2003) Poulsson and Kale (2004)

Hogan et al. (2005)

Shaw (2005)

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Table I. Definitions of customer experience

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References

Definitions of customer experience

Mascarenhas et al. (2006)

Total customer experience “is a totally positive, engaging, enduring, and socially fulfilling physical and emotional customer experience across all major levels of one’s consumption chain and one that is brought about by a distinct market offering that calls for active interaction between consumers and providers” Customer experience acts “as the user’s interpretation of his or her total interaction with the brand” “Customer experience is the internal and subjective response customers have to any direct or indirect contact with a company. Direct contact generally occurs in the course of purchase, use, and service and is usually initiated by the customer. Indirect contact most often involves unplanned encounters with representations of a company’s products, services, or brands and takes the form of word-of-mouth recommendations or criticisms, advertising, news reports, reviews, and so forth” “The customer experience originates from a set of interactions between a customer and a product, a company, or part of its organization, which provoke a reaction. This experience is strictly personal and implies the customer’s involvement at different levels (rational, emotional, sensorial, physical, and spiritual). Its evaluation depends on the comparison between a customer’s expectations and the stimuli coming from the interaction with the company and its offering in correspondence of the different moments of contact or touch-points” The perfect customer experience addresses that “advocacy typically implies achieving a very high score on customer satisfaction” The customer experience is “a mental journey that leaves the customer with memories of having performed something special, having learned something or just having fun” Customer experience is “holistic in nature and involve(ing) the customer’s cognitive, affective, emotional, social and physical responses to the retailer. This experience is created not only by those factors that the retailer can control (e.g. service interface, retail atmosphere, assortment, price), but also by factors outside of the retailer’s control (e.g. influence of others, purpose of shopping)” “A customer experience is defined as the customer’s direct and indirect experience of the service process, the organization, the facilities and how the customer interacts with the service firm’s representatives and other customers. These in turn create the customer’s cognitive, emotional and behavioral responses and leave the customer with memories about the experience”

Ghose (2009)


Meyer and Schwager (2007)

Gentile et al. (2007)

Frow and Payne (2007) Sundbo and Hagedorn-Rasmussen’s (2008) Verhoef et al. (2009)

Walter et al. (2010)

Table I.

playfulness, positive effect, skill, telepresence and time distortion. Similarly, Greenwell et al. (2002) constructed a scale to assess the experiences of the sports fans. Grace and O’Cass (2004) dealt with the post-consumption experiences of the bank customers and identified the effect of constructs (core service, employee service and services cape) on feelings, satisfaction and brand attitude of the customer. Similarly, Knutson et al. (2007) created a seven-factor (environment, benefits, accessibility, convenience, utility, incentive and trust) scale to measure the experiences of the


hospitality sector customers. Further, Kim et al. (2011) constructed and validated a generalised scale on the same factors and developed a customer experience index (CEI). To evaluate the experiences of bed-and-breakfast industry customers, Oh et al. (2007) generated a scale which was based on the four realms of Pine and Gilmore (1998). On the similar steps, Hosany and Gilbert (2009) have developed a measuring instrument for the sake of measuring the cruise experiences of the customers. Likewise, the scale developed by Slatten et al. (2009) was used to assess the atmospheric experiences which emotionally touch visitors at a winter park. Similarly, Hosany and Gilbert (2009) examined the constructs of tourist’s emotional experience in relation to hedonic holiday destinations and developed a destination emotion scale (DES) with three significant dimensions as joy, love and positive surprise. Brakus et al. (2009) have distinguished many experience dimensions and constructed a four dimensional brand experience scale with the dimensions as sensory, affective, intellectual and behavioural. They also highlighted the relationship of brand experience with brand personality, satisfaction and loyalty. In the meanwhile, Knutson et al. (2009) proposed a four-factor 18-item measuring instrument for the assessment of hotel industry’s customer experience resulting as hotel experience index (HEI). Wu and Liang (2010) developed a scale to measure the antecedents of experience and to identify their impact on rafting satisfaction and rafting loyalty. Moreover, in case of service research, “customer experience” has been merged with service quality which evaluates the resultant of the service process recognized by the customers. To measure service quality, a widely acceptable measuring instrument, i.e. SERVQUAL has been proposed by Parsuraman et al. (1988). But this scale is not sufficient to measure the experiences of the customers at every touch point with the organization. Its main reason is that, in service quality studies customers are treated as passive observers, who just process the information and later assess the service interactions as a resultant outcome. But their interactions with the organization (in a social context) and the entire customer process has not been explicitly considered and empirically investigated (Walter et al., 2010; Lindquist and Persson, 1997; Williams, 2000; Stauss and Weinlich, 1996; Verhoef et al., 2009). Therefore, a need has been found to develop a comprehensive scale in order to measure the banking experiences of customers. The reasons being, first, after examining the existing scales with the help of experts and bank managers, the crux of the observation is that most of the developed instruments were industry-specific which were designed while concentrating on the sector-specific requirements of the customers. Therefore, these existing scales cannot be fully applied in case of banking experiences. Second, in the existing studies, the researchers have developed a scale either in online settings or in offline settings. None of the existing scales have considered both these elements in a combined way. However, day-by-day as there is a vast and fast paced expansion of e-banking services, developing a measuring instrument by considering either the online elements or the offline elements is not worthwhile. The appropriate evaluation of banking experiences can only be done when both these elements are considered in the collective manner. Third, a lack of such scales was found in the Indian context, as none of the above mentioned studies has been carried out in India. Therefore, to overcome these limitations, it is imperative to develop a systematic and psychometric scale to measure the banking experiences of the Indian customers. Different researches have attempted to make out factors which can be used for the


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evaluation of customer experience. But, today in this internet savvy environment, no business can survive without going online. A customer avails his/her experiences in both ways, i.e. through direct interaction with the organization and through the web site of the organization. So, to deliver total customer experience it has become imperative to take both the online and offline factors collectively. Garg et al. (2012) have identified and described 14 factors of customer experience comprising both online and offline elements in detail. They identified the weightages of the factors through analytic hierarchy process (AHP) and laid the foundation for formal empirical evaluation. For further validation, without loss of generality we have adopted all the 14 factors from the study done by Garg et al. (2012). These factors are: servicescape, core service, customization, value addition, convenience, marketing-mix, employees, speed, service process, customer interaction, presence of other customers, online aesthetics, online hedonic elements and online functional elements. The description and literature evidence of these factors is depicted in Table II. Scale development In this study, the well-accepted scale development paradigm given by Churchill (1979) has been applied further augmented by Anderson and Gerbing (1982), Bentler and Bonnet (1980), Bagozzi et al. (1991), Nunnally and Bernstein (1994) and Hinkin (1995). The scale development procedure (Arnold and Reynolds, 2003) is shown in Figure 1. Phase I: item generation and selection Aiming to generate specific items that comprise the proposed factors of customer experience (as discussed in Table II) in retail banking sector, an extensive review of literature dealing with these factors was conducted. The articles reviewed to gather the items for each factor are shown in Table III. From this extensive literature search, a total number of 234 items were selected, These 234 items refer to servicescape (18 items); core service (20 items); customization (14 items); value addition (17 items); convenience (21 items); marketing-mix (14 items); employees (22 items); speed (16 items); service process (19 items); customer interaction (12 items), presence of other customers (16 items); online aesthetics (15 items); online hedonic elements (13 items) and online functional elements (17 items). After assembling these items, in an initial screening 218 items were retained. In the next stage, a panel of six experts (professors/bank managers) reviewed this initial item pool and the resultant of a close scrutiny appears as the deletion of 39 overlapping items. In the third stage, panel was asked to retain the clearly worded items. After a long discussion, due to lack of clarity and possibility of misinterpretation (Babin et al., 1994), panel agreed to deduct 33 of 179 items. On steps similar to Lin and Hsieh (2011), experts were requested to rate items in one out of three categories, i.e. “not representative”, “somewhat representative” or “clearly representative”. “Only items rated clearly and somewhat representative by at least 80 per cent of the judges were retained” (Lin and Hsieh, 2011). A substantial amount of items were deleted at this stage, resulting in 84 items. On the basis of expert’s feedback, the remaining items were further evaluated on several occasions in an iterative process and 13 more items were eliminated. Finally, a pool of 71 items were retained which was again reviewed by four external experts. None of the items was deleted at this stage, but the experts suggested incorporating more familiar item wordings.

Factors

Relevance in customer experience

Literature evidence

Servicescape (SS)

It is the physical environment which is shared by the employees and customers of any organization. The responses of the customers are affected by the three dimensions of physical environment. These are artifact and symbols, ambient conditions and space and function and signs It is the fundamental service due to which an organization positions itself in the market

Bitner (1992), Pine and Gilmore (1998), Grace and O’Cass (2004), Knutson et al. (2007), Jain and Bagdare (2009), Walter et al. (2010)


Core service (CS)

Customization (CUS)

Value addition (VA)

Convenience (CON)

Marketing mix (MM)

Employees (EM)

Speed (SPE)

Grace and O’Cass (2004), O’Cass and Grace (2004), Walter et al. (2010), Jain and Bagdare (2009) It is an extent up to which the services Addis and Holbrook (2001), Haeckel et al. (2003), are customized for a particular Olorunniwo and Hsu (2007), customer. These are the particular requisites of the customers according Zhang ( Jane) et al. (2008) to which an organization tailors its products or services These are the supplementary services Lovelock (1996), Schmitt (1999), which are delivered in addition to the Berry et al. (2002), Lexhagen core service; creates an exclusive and (2005), Knutson et al. (2007), unforgettable feeling in the minds of Zhang ( Jane) et al. (2008), Slatten et al. (2009), Jain and the customers Bagdare (2009), Walter et al. (2010) It acts as one of the main constituents Rowley (1994, 1999), Constantinides (2004), in building the experiences of Arnold et al. (2005), customers. The reason is that the Knutson et al. (2007), customers require an ease at their Mahfouz et al. (2008), Jain and every single contact point with the Bagdare (2009) organization Tsai (2005), Constantinides In an organization, marketing-mix strategies of all P’s are planned in such (2004, 2010) a manner that they can fulfill the requirements of their customers. It is a salient tool, which to a certain extent affects customer’s buying behaviors and decisions In any organization, employees are the Sarel and Marmorstein (1999), basic source of delivering services to Sun (2002), O’Cass and Grace the customers. In such case, they ought (2004), Arnold et al. (2005), to be friendly, helpful, time committed, Rahman (2006), Zhang competent and capable of sustaining ( Jane) et al. (2008), Verhoef et al. (2009), Sheu et al. (2009) interpersonal distance It is the rapidity of any organization, Sarel and Marmorstein (1999), Grove and Fisk (1997), which it shows in delivering the responses against the requirements of Berry et al. (2002), Flanagan et al. (2005), Jain and the customers Bagdare (2009) (continued)


Table II. Critical factors of customer experience

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Table II.

Factors Service process (SP)

Relevance in customer experience

It is the combination of the series and steps of activities, the flow and interface among these activities, and the requirement of resources which are used to produce and deliver the outcome of the service Customer interaction (CI) It is an interface which exists between a customer and an organization. In any organization, a customer interacts with its different parts as with its servicescape, with its products/ services, with its other customers, etc. Presence of other customers The perception related to presence of (POOC) other customers differs from industry to industry. In some types of service settings such as at athletic events, at cinema halls, in amusement parks, presence of others gives a social surrounding to any individual, while in service settings where reservation lines exist such as in banks, at reservation counters, the presence of other customers is perceived to be a crowd Online aesthetics (OA) The aesthetic attributes of any organization’s web site aim; to attract the web-user in a very short duration of time and to leave a positive impression about its products/services in the mind of the customer Online hedonic elements These elements help the user to escape (OHE) from its real-life surroundings by immersing him/her into the online environment, where he/she feels more delighted in contrast to reality Online functional elements These elements basically deal with the (OFE) functionality aspect of the web site, its usability and interactivity components highly affect the web-experience of the user

Literature evidence Tseng et al. (1999), Grace and O’Cass (2004), Bigne et al. (2008), Walter et al. (2010)

Tsai (2005), Nagasawa (2008), Zhang ( Jane) et al. (2008), Verhoef et al. (2009), Sheu et al. (2009), Slatten et al. (2009) Grove and Fisk (1997), Arnold et al. (2005), Nagasawa (2008), Verhoef et al. (2009), Walter et al. (2010)

Sun (2002), Sheu et al. (2009), Constantinides (2004, 2010)

Mathwick and Rigdon (2004), Takatalo et al. (2008), Bridges and Florsheim (2008), Sheu et al. (2009) Constantinides (2004), Mathwick and Rigdon (2004), Takatalo et al. (2008), Sheu et al. (2009)

Further, on the similar steps of Brakus et al. (2009), 44 management graduates were requested to take part in a study on customer experience. After clearing up the concept of customer experience, they were asked to assess the degree to which the 71 items described their banking experiences, using a five-point Likert scale where 1 – highly non-descriptive and 5 – highly descriptive. We retained the items whose mean value was greater than 3 and standard deviation was less than 2. According to this criterion, ten out of 71 items were deleted, hence the final set contains 61 items. The refinement and validation of these items were described in the subsequent phases.

ITEM GENERATION & SELECTION PHASE

Item Generation and Selection

Qualitative Inquiry Stage

Customer experience in banks

• Content and face validity assessment


95

Pilot Test Sample (n = 203)

Pilot-testing Stage • Item analysis • Exploratory factor analysis

• Consistency and reliability assessment

SCALE REFINEMENT PHASE Calibration Sample (n = 624)

Purification Stage • Confirmatory factor analysis

• Unidimensionality and reliability assessment

• Convergent and

discriminant validity assessment

SCALE VALIDATION PHASE

Validation Sample (n = 348)

Validation Stage • Replication of confirmatory factor analysis

• Unidimensionality and reliability assessment

• Convergent and discriminant validity assessment • Nomological validity assessment

Phase II: scale refinement This phase covers the pilot-testing and purification stage as shown in Figure 1. For pilot-testing, a questionnaire of 61 customer experience items to be evaluated on a five-point Likert scale (where 1 – highly disagree and 5 – highly agree) was constructed. The questionnaire was divided into two sections, where first section dealt with the demographic profile of the respondents as gender, marital status, age, education-level and income. Second section, comprises 61 questions on customer experience. For the preliminary refinement of 61-item instrument, similar to Brakus et al. (2009), Lin and Hsieh (2011), Froehle and Roth (2004) and Arnold and Reynolds (2003) the data was gathered on the new sample of doctoral and postgraduate students. The sample size of 203 customers was used for pilot-testing of the items which was inline with the

Figure 1. Scale development procedure

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Factors

References

Customer interaction Presence of other customers Employees

Edris and Almahmeed (1997), Nagasawa (2008) Naser et al. (1999), Zineldin (1996), Alfansi and Sargeant (2000) Athanassopoulos et al. (2001), Grace and O’Cass (2004), Sureshchander et al. (2003), Foscht et al. (2009), Karatepe et al. (2005) Al-Eisa and Alhemoud (2009), Foscht et al. (2009), Karatepe et al. (2005), Grace and O’Cass (2004), Athanassopoulos et al. (2001) Jabnoun and Al-Tamimi (2003), Edris and Almahmeed (1997), Karatepe et al. (2005), Rahman (2006) Bick et al. (2004), Edris and Almahmeed (1997), Beerli et al. (2004), Foscht et al. (2009) Alfansi and Sargeant (2000), Foscht et al. (2009), Bick et al. (2004), Devlin and Gerrard (2005), Naser et al. (1999) Chakravarty et al. (2004), Boyed et al. (1994), Jabnoun and Al-Tamimi (2003), Angur et al. (1999) Grace and O’Cass (2004), O’Cass and Grace (2004), Zineldin (1996) Sureshchander et al. (2003), Athanassopoulos et al. (2001), Angur et al. (1999), Beerli et al. (2004) Athanassopoulos et al. (2001), Zineldin (1996), Sureshchander et al. (2003), Bloemer et al. (1998) Jun and Cai (2001), Khan and Mahapatra (2009), Devlin and Gerrard (2005), Aldas-Manzano et al. (2009) Jun and Cai (2001), Khan and Mahapatra (2009), Devlin and Gerrard (2005), Aldas-Manzano et al. (2009) Jun and Cai (2001), Khan and Mahapatra (2009), Devlin and Gerrard (2005), Aldas-Manzano et al. (2009)

Servicescape Convenience


Customization Value addition Speed Core service Service process Marketing-mix Online functional elements Online hedonic elements Table III. Articles reviewed for the generation of items

Online aesthetics

sample sizes of other scale development studies such as Parsuraman et al. (1988), Karatepe et al. (2005) and Webster (1990). Among 203 respondents, 63 per cent respondents were male and 37 per cent respondents were female. In this phase, the steps suggested by Churchill (1979) were adopted for the complete refinement of the instrument. These are item analysis, exploratory factor analysis (EFA) (pilot-testing) and confirmatory factor analysis (CFA) (purification). The procedure for the scale refinement is as follows. (i) Item analysis. As recommended by Churchill (1979), the first and the foremost step to refine the scale is the computation of coefficient a, i.e. Cronbach a. For all factors of customer experience, coefficient a was computed, that ranged from 0.67 to 0.85. But according to Nunnally’s criterion, the minimum satisfactory value of Cronbach a is 0.7. Therefore, to improve the value of a, corrected item-to-total correlation for each cluster of items were computed. Items possessing very low correlations and/or items whose correlations produce sharp drop among the corrected item-to-total correlations and/or items whose removal improves the value of a, were deleted. This iterative sequence was repeated numerous times which resulted in the form of 56 items and five items being deleted. The improved values of a for all 14 factors ranged from 0.75 to 0.88. These values were servicescape (0.81); core service (0.82); customization (0.82); value addition (0.76); convenience (0.88); marketing-mix (0.81); employees (0.85); speed (0.83); service process (0.76); customer interaction (0.84), presence of other


customers (0.77); online aesthetics (0.79); online hedonic elements (0.75) and online functional elements (0.85). (ii) Exploratory factor analysis. After item analysis, next EFA was applied on remaining 56 items. The aim of EFA was to determine the condition where links between the latent and observed variables are uncertain or unknown and principal component analysis (PCA) along with varimax rotation was executed for extracting factors (Costello and Osborne, 2005) through SPSS 19.0 software. A minimum cut off criteria for the deletion of the items was: factor loadings (, 0.50) (Karatepe et al., 2005), cross loadings (. 0.40) or communalities (, 0.30) (Hair et al., 1998). The appropriateness of the analysis was determined by the examination of Kaiser-Meyer-Olkin (KMO) statistic of sampling adequacy. For good factor analysis, the value of KMO must be at least 0.60 and above (Tabachnick and Fidell, 1996). The results of the analysis revealed that eigen value of all 14 factors was greater than 1 (Kaiser, 1960), therefore, none of the factors can be eliminated from the study. These 14 factors accounted for 67.64 per cent variance in the analysed items and KMO measure was 0.84, indicated good factor analysis. All communalities ranged from 0.38 to 0.83. Six items were dropped after a close inspection as they could not fulfil the minimum cut off criteria mentioned above. The reliability coefficients of all the factors ranged from 0.75 to 0.88 specifying good internal consistencies among all the items. Further, the combined reliability was computed for all the 50-items (Nunnally, 1978) and it was found to be quite high, i.e. 0.93. Finally, total 50 items for all the 14 factors were retained in this phase as shown in Table IV. (iii) Confirmatory factor analysis. After pilot-testing stage, next step was to purify the items as shown in Figure 1. In purification stage, CFA (Marsh and Hocevar, 1985) was performed on the remaining set of items. CFA is a special case of structural equation modeling (SEM), which is also known as linear structural relationship model (Joreskog and Sorbom, 2004) or covariance structure (McDonald, 1978). It is a multivariate technique applied when the researcher possesses some information about the underlying latent variable structure. To test the stability of the 50-item 14 factor scale, a sample of bank customers was employed. For this sample, the top three banks from the list of “The Best Banks, 2010” rated by a reputed business magazine Business Today, 2010 was selected. These three banks were Axis Bank Ltd, Punjab National Bank (PNB) and HDFC Bank. The ranking parameters adopted by Business Today were growth, size and strength of the banks. The data was collected from the capital city of India and the region nearby to it, i.e. Delhi and National Capital Region (NCR). There were a total of 921 branches, i.e. AXIS bank – 100, PNB – 592 and HDFC bank – 229 branches, in Delhi and NCR of the selected banks. The number of target branches considered for this study was (0.15 of 921) following Lenka et al. (2009), i.e. 138 branches. Yet in some district regions, it was identified that after applying the formula of 15 per cent of the total number of branches approximately nil results appeared, in that case we considered the minimum one branch lying in that district region. In this manner, the number of sample branches has been exceeded from 138 to 156. Finally, through simple random sampling total 156 branches were considered for data collection. Using mall-intercept (branch intercept) survey method, data was collected from 624 customers. The demographic segmentation of these customers is shown in Table V.


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Table IV. EFA results for n ¼ 203

Items CON 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50

SS

EMP OFE POOC

OA

CUS

CS

VA

SPE MM

SP

OHE

CI

0.79 0.77 0.77 0.76 0.72 0.79 0.78 0.65 0.57 0.55 0.77 0.77 0.64 0.62 0.59 0.82 0.78 0.72 0.64 0.74 0.71 0.61 0.58 0.82 0.81 0.73 0.72

Note: List of items is depicted in the Appendix

0.81 0.79 0.70 0.79 0.78 0.76 0.74 0.72 0.60 0.59 0.86 0.82 0.82 0.79 0.72 0.72 0.68 0.63 0.62 0.81 0.75 0.81 0.72


Demographic variables Gender Male Female Marital status Married Unmarried Age (in years) Under 20 21-35 36-50 51-65 66 and over Education level High school and below Diploma Bachelor degree Masters degree Professional degree Income (monthly) ,10,000 10,000-30,000 30,000-50,000 .50,000 None

Frequency

Percentage

329 295

52.7 47.3

439 185

70.4 29.6

76 251 141 87 69

12.2 40.2 22.6 13.9 11.1

134 120 231 86 53

21.5 9.2 37.0 13.8 8.5

78 217 147 113 69

12.5 34.8 23.6 18.1 11.1

Note: n ¼ 624

According to Bagozzi (1980), Anderson and Gerbing (1988) and Arnold and Reynolds (2003), in scale purification stage, improvement of the scale’s psychometric properties depends upon the reiteration of CFA. A 50-item 14 factor confirmatory factor model was studied using AMOS 18.0. The indices of the model appear as (x2ð1084Þ ¼ 1,998.86, p ¼ 0.000; x 2 /df ¼ 1.84; GFI ¼ 0.88; AGFI ¼ 0.86; NFI ¼ 0.88; CFI ¼ 0.94; RMSEA ¼ 0.04; RMR ¼ 0.04). This result reveals that some indices are below the acceptable threshold values. The squared multiple correlations (SMCs) ranged from 0.39 to 0.79 and modification indices ranged from 4.0 to 95.9. An inspection of both these parameters required the deletion of nine items. Further, the domain representativeness of each item was also inspected (Nunnally and Bernstein, 1994). For example, in online functional elements factor, the removed candidate item was “The functioning of the web pages is correct” tapped into same facet with the retained item “The links are accurate, problem-free and page downloads quickly”. Therefore, due to the facet representation in the similar manner other eight candidate items were also removed. The complete process was repeated once more with no further deletion of the items. The final confirmatory model appears with 41 items and the results of this 41-item 14 factor model demonstrated good model-fit indices as (x2ð687Þ ¼ 1,020.7 p ¼ 0.000; x 2/df ¼ 1.48; GFI ¼ 0.93; AGFI ¼ 0.91; NFI ¼ 0.92; CFI ¼ 0.97; RMSEA ¼ 0.03; RMR ¼ 0.035). All the modification indices appear significantly low and SMCs now


Table V. Demographic profile of the respondents

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ranged from 0.46 to 0.81. After identifying the model-fit indices through CFA, the next step in the purification stage is to verify the unidimensionality, reliability, convergent validity and discriminant validity of the remaining set of items as shown in Figure 1. (a) Unidimensionality and reliability. These results evidently prove the unidimensionality of the measures, as each item is related with one and only one fundamental construct (Gerbing and Anderson, 1988; Bollen, 1989). As depicted in Table VI, the values of coefficient a, range from 0.80 to 0.87, item-to-total correlation estimates range from 0.57 to 0.76, and values of composite reliability range from 0.80 to 0.88. It shows good construct reliability which ultimately indicates high internal consistency (Nunnally and Bernstein, 1994; Fornell and Larcker, 1981). (b) Convergent and discriminant validity. From the measurement model, convergent validity can be examined by identifying whether the maximum likelihood loading of each indicator is significant to its underlying construct (Peter, 1981; Anderson and Gerbing, 1988; Arnold and Reynolds, 2003). As illustrated in Table VI, all CFA loadings exceed 0.68 and are significant with t estimates ranging from 12.93 to 22.30. Therefore, the convergent validity of all measures was evident. In addition, as suggested by Bagozzi et al. (1991) the average variance extracted (AVEs) of all the 14 factors ranging from 0.57 to 0.75 were above the minimum threshold value of 0.5. As recommended by Hair et al. (1998), composite reliability of all measures was above 0.80 (Table VI). All these estimates indicate the high degree of convergence between the items with their respective constructs. The discriminant validity can be examined by comparing the shared variance between measures with the AVEs of the individual measures (Fornell and Larcker, 1981). The comparison between the AVEs and shared variance is depicted in Table VII. The results revealed that the shared variance between the measures was less than the AVEs of the individual measures, which confirms discriminant validity. Phase III: scale validation The next step after the scale refinement phase was the validation of the developed scale as shown in Figure 1. The main reasons to validate the developed scale were: first, to fulfil the requirement of replicating the confirmatory factor model on an independent sample, thus reducing error which may occur by capitalization on chance (MacCallum et al., 1992; Chin and Todd, 1995), and next, to check the nomological validity by examining the relationship between the factors of customer experience and a theoretically related variable, i.e. customer satisfaction (SAT) (Arnold and Reynolds, 2003). The steps of the scale validation phase are: (i) Replication of CFA. In this phase, three items of customer satisfaction were added in the 41-item questionnaire. These satisfaction items were adopted from the studies done by Levesque and McDougall (1996), Grace and O’Cass (2004), Foscht et al. (2009), Beerli et al. (2004) and Karatepe et al. (2005). For the validation of 14 factor 41-item scale, data was collected on the new sample of customers of the same banks (surveyed in purification stage). A total 384 responses were gathered, out of which 348 responses, i.e. 90 per cent response rate were found usable. This sample size was at par with the inline studies done by Brakus et al. (2009), Lin and Hsieh (2011), Froehle and Roth (2004) and Arnold and Reynolds (2003). The demographic profile summary of the validation sample was approximately similar with the demographics of the calibration sample.

Customization

Online aesthetics

Presence of other customers

Online functional elements

Employees

Servicescape

Convenience

Construct

CUS1 CUS2 CUS3

OA1 OA2 OA3 OA4

POOC2 POOC3 POOC4

OFE1 OFE3 OFE4

EMP1 EMP2 EMP3

SS2 SS3 SS4

CON1 CON2 CON4

Items

0.84

0.86

0.83

0.85

0.80

0.83

0.80

0.82

0.86

0.82

0.81

0.81

0.85

0.84

Coefficient a S1 S2

0.84

0.85

0.83

0.85

0.80

0.83

0.81

0.80

0.86

0.82

0.82

0.83

0.85

0.84

Composite reliability (CR) S1 S2

0.68

0.59

0.62

0.66

0.58

0.63

0.58

0.61

0.67

0.60

0.60

0.62

0.65

0.64

Average variance extracted S1 S2

0.81 0.79 0.70

0.72 0.81 0.72 0.82

0.71 0.61 0.73

0.72 0.79 0.82

0.77 0.77 0.63

0.79 0.66 0.78

0.79 0.72 0.77

EFA item loading S1

0.74 0.71 0.66

0.71 0.71 0.68 0.73

0.68 0.67 0.71

0.72 0.72 0.74

0.66 0.69 0.59

0.71 0.66 0.71

0.66 0.61 0.68

0.70 0.68 0.65

0.61 0.75 0.70 0.75

0.72 0.61 0.66

0.62 0.67 0.70

0.68 0.71 0.67

0.74 0.67 0.74

0.72 0.67 0.72

Corrected item-total correlation S1 S2

0.85 0.81 0.74

0.73 0.82 0.69 0.83

0.82 0.75 0.79

0.80 0.81 0.83

0.77 0.81 0.70

0.83 0.74 0.80

0.78 0.70 0.80

0.81 0.73 0.73

0.65 0.84 0.76 0.84

0.83 0.70 0.89

0.70 0.79 0.83

0.77 0.83 0.75

0.84 0.74 0.83

0.83 0.73 0.83

CFA item loading S1 S2


0.73 0.65 0.55

0.53 0.68 0.47 0.69

0.67 0.58 0.62

0.65 0.65 0.69

0.60 0.65 0.49

0.69 0.55 0.65

0.61 0.49 0.64

0.66 0.54 0.53

0.43 0.71 0.58 0.71

0.69 0.49 0.62

0.48 0.62 0.69

0.60 0.69 0.57

0.71 0.55 0.70

0.69 0.53 0.69

Squared multiple correlation S1 S2

10.2 3.4 3.3 3.5

10.5 3.5 3.5 3.5

10.3 10.4 3.5 3.5 3.4 3.4 3.5 3.5 14.6 13.5 3.6 3.3 3.6 3.4 3.5 3.4 3.9 3.4 10.7 10.8 3.6 3.6 3.5 3.5 3.6 3.7 (continued)

9.4 3.0 3.3 3.1 9.6 3.4 3.0 3.2 10.3 3.4 3.4 3.5

9.8 3.3 3.0 3.5 10.3 3.3 3.6 3.4 11.0 3.6 3.7 3.7

Scale/item mean S1 S2


Table VI. Scale/item measurement properties

Table VI.

CI 1 CI 2

OHE1 OHE2

SP1 SP2 SP3

MM1 MM2 MM3

CS1 CS2 CS3

SPE1 SPE2 SPE3

VA1 VA2 VA4

0.85

0.80

0.81

0.80

0.84

0.87

0.81

0.86

0.80

0.80

0.82

0.83

0.88

0.80

0.85

0.81

0.82

0.80

0.84

0.88

0.81

0.88

0.80

0.81

0.83

0.83

0.88

0.81

0.75

0.68

0.60

0.57

0.64

0.69

0.59

0.79

0.67

0.58

0.61

0.61

0.70

0.58

0.81 0.75

0.75 0.79

0.63 0.67 0.62

0.80 0.73 0.70

0.77 0.79 0.78

0.82 0.86 0.82

0.73 0.69 0.60

EFA item loading S1

0.74 0.74

0.67 0.67

0.57 0.71 0.67

0.68 0.65 0.59

0.71 0.73 0.66

0.74 0.75 0.76

0.68 0.68 0.60

0.76 0.76

0.68 0.68

0.59 0.69 0.67

0.69 0.73 0.63

0.65 0.70 0.69

0.76 0.79 0.73

0.72 0.71 0.61

Corrected item-total correlation S1 S2

0.90 0.82

0.73 0.82

0.68 0.78 0.73

0.80 0.79 0.68

0.81 0.84 0.75

0.82 0.84 0.83

0.82 0.80 0.68

0.90 0.84

0.77 0.86

0.68 0.83 0.77

0.83 0.83 0.68

0.76 0.80 0.78

0.85 0.86 0.79

0.81 0.79 0.69

CFA item loading S1 S2

Notes: S1 – calibration sample (n ¼ 624); S2 – validation sample (n ¼ 348) Source: Refer Fornell and Larcker (1981) for details of average variance extracted and composite reliability calculations

Customer interaction

Online hedonic elements

Service process

Marketing-mix

Core service

Speed

Value addition

Items

Average variance extracted S1 S2

0.81 0.68

0.53 0.67

0.46 0.61 0.54

0.64 0.62 0.46

0.65 0.70 0.56

0.68 0.70 0.69

0.69 0.63 0.47

0.81 0.71

0.60 0.75

0.46 0.69 0.60

0.69 0.69 0.47

0.58 0.64 0.62

0.73 0.74 0.62

0.66 0.62 0.47

Squared multiple correlation S1 S2

102

Construct

Coefficient a S1 S2

Composite reliability (CR) S1 S2


6.8 3.5 3.3 6.6 3.4 3.2

9.5 3.4 3.0 3.1 9.1 3.0 3.0 3.1 10.8 3.6 3.5 3.7 10.4 3.2 3.9 3.3 10.2 3.5 3.3 3.4

7.0 3.6 3.4 7.3 3.6 3.7

9.7 3.4 3.1 3.2 9.8 3.2 3.2 3.4 10.9 3.8 3.6 3.5 9.8 3.0 3.3 3.5 10.0 3.4 3.3 3.3

Scale/item mean S1 S2

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Factor

1

2

3

4

5

6

CON SS EMP OFE POOC OA CUS VA SPE CS MM SP OHE CI

0.58 0.10 0.10 0.02 0.08 0.02 0.04 0.06 0.02 0.11 0.01 0.04 0.08 0.04

0.63 0.14 0.04 0.14 0.06 0.06 0.08 0.02 0.06 0.11 0.04 0.08 0.06

0.58 0.08 0.16 0.07 0.10 0.11 0.09 0.05 0.10 0.15 0.08 0.16

0.66 0.06 0.07 0.02 0.06 0.02 0.01 0.04 0.03 0.03 0.05

0.62 0.09 0.08 0.19 0.07 0.06 0.09 0.24 0.14 0.09

0.59 0.02 0.04 0.04 0.07 0.03 0.06 0.09 0.04

7

8

9

10

11

12

13

14


0.64 0.08 0.02 0.01 0.01 0.04 0.08 0.04

0.59 0.08 0.10 0.05 0.11 0.10 0.13

0.69 0.03 0.02 0.04 0.08 0.10

0.64 0.003 0.08 0.08 0.02

0.57 0.07 0.02 0.04

0.60 0.07 0.16

0.68 0.004

0.75

Note: Average extracted variance (AVEs) is represented by diagonal values and shared variance is represented by all other entries

The results revealed their representation in all income and age categories, with approximately 56 per cent male respondents and 44 per cent female respondents, little over one-half on them were married and approximately 42 per cent respondents were graduates. To validate the developed scale, the complete CFA procedure discussed in previous section was repeated. At first, a CFA was performed on the data collected from new independent sample (validation sample) of banks’ customers and the results of the model revealed a good fit (x2ð687Þ ¼ 788.1, p , 0.00; x 2/df ¼ 1.15; GFI ¼ 0.90; AGFI ¼ 0.88; NFI ¼ 0.90; CFI ¼ 0.98; RMSEA ¼ 0.021; RMR ¼ 0.044). All the modification indices were considerably low and SMC range from 0.43 to 0.81. Further, to check the reliability and validity, the procedure applied in scale purification stage was again followed. As illustrated in Table VI, for validation sample all the values of coefficient a for each construct were 0.80 and above, composite reliability estimates range from 0.80 to 0.88 and corrected item-to-total correlations range from 0.59 to 0.79. All these estimates provide the evidence of reliability (Nunnally and Bernstein, 1994; Fornell and Larcker, 1981). The AVEs ranges from 0.58 to 0.79, all factor loadings are 0.65 and above and t-values ranging from 12.29 to 17.50 (Bagozzi et al., 1991; Hair et al., 1998). All these values evidently prove convergent validity of the validation model. To examine discriminant validity, shared variance between constructs were compared with the AVEs of the individual construct (Fornell and Larcker, 1981). For all factors, the AVEs (ranging between 0.58 and 0.76) were greater than the shared variance (0.08-0.38) between the factors, shows the evidence of discriminant validity. Further, the association between the experience factors and customer experience with a second-order CFA was inspected (Byrne, 1994; Homberg and Rudolph, 2001; Yang et al., 2005). The fit indices of the second-order measurement model of customer experience are acceptable (x2ð764Þ ¼ 892.4, p , 0.00; x 2/df ¼ 1.17; GFI ¼ 0.89; AGFI ¼ 0.88; NFI ¼ 0.90; CFI ¼ 0.98; RMSEA ¼ 0.021; RMR ¼ 0.044). Although the

Table VII. Discriminant validity

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values of x 2 were significant, but the results of x 2 difference test among the first-order CFA model and second-order CFA model were non-significant (Dx 2 ¼ 104.1; Ddf ¼ 77). Thus, recommended that this higher order model as shown in Figure 2 has been adequately accounted for the given data (Lages et al., 2005). All the factor loadings were significant as shown in Figure 2, which supports the convergent validity and hypothesized structure of the measurement scale. (ii) Nomological validity assessment. After, verifying the developed scale on the independent sample of the respondents. Next, the nomological validity has been investigated by examining the relationship between the 14 customer experience factors and a theoretically related measure, i.e. customer satisfaction (Lin and Hsieh, 2011). The relationship between customer experience and satisfaction had been justified by many studies (Walter et al., 2010; Wu and Liang, 2010; Hosany and Witham, 2010; Brakus et al., 2009; Sheu et al., 2009; Oh et al., 2007). To assess the nomological validity, the correlation estimates for the validation sample (n ¼ 348) has been calculated (Arnold and Reynolds, 2003) as depicted in Table VIII. As the scale consisted of 14 factors, we therefore aggregated the scale to get 14 indicators by averaging the items of each factor. Correlation estimates revealed that all the 14 indicators are positively correlated ( p , 0.01) with customer satisfaction (SAT) as shown in Table VIII, gives the evidence of nomological validity. Moreover, in the structural model as shown in Figure 3, all the path loadings are significant with good indices of model fit (x2ð118Þ ¼ 164.5, p , 0.00; x 2/df ¼ 1.38, GFI ¼ 0.95; AGFI ¼ 0.93; NFI ¼ 0.92; CFI ¼ 0.98; RMSEA ¼ 0.033; RMR ¼ 0.054). The ratings on customer experience scale describe 60 per cent of customer’s satisfaction. Thus, it is concluded that the scale revealed criterion-related verification of nomological validity. Customer experience factors and demographics The next step after validation of the developed scale was to identify the significant relationship between the factors of customer experience to the demographic variables of the respondents such as gender, marital status, age, education level and income (Table IV). On the similar steps of Lee et al. (2008), one-way ANOVA test has been applied on both factors of customer experience and on demographic variables of calibration sample (n ¼ 624). The results of the analysis is shown in Table IX, which revealed that all the variables including gender, marital status, age, education level and income have significant differences in their relatedness to some factors of customer experience. The gender of the customer significantly related to convenience, servicescape, presence of other customers, online aesthetics and service process. As compared to males, females give more importance to convenience, servicescape, online aesthetics, presence of other customers and service process of the banks. Marital status was significantly related to online functional elements and online aesthetics. Single subjects are comparatively more internet savvy and hence they put more value to the online aesthetics and online functional elements. The age of the customer affects on convenience, customization and customer interaction. The 51-65 years group possesses stronger experience on convenience and customer interaction as compared to other age-groups whereas, 36-50 years age-group has stronger values in customization than other age-groups. The education-level of the customers significantly related to servicescape, presence of other customers, online aesthetics, customization, value addition and online hedonic elements. The diploma

CON 1

0.83

CON 2

0.72

CON 4

0.83 0.84 0.75

SS 2 SS 3 SS 4 EMP 1 EMP 2 EMP 3


OFE 1 OFE 2 OFE 4

0.83 0.77 0.83 0.75 0.69 0.79

POOC 3

0.83 0.83 0.70

POOC 4

0.78

OA 1

0.66 0.84 0.76

POOC 2

OA 2 OA 3 OA 4

0.84

CUS 1

0.81 0.73

CUS 2 CUS 3 VA 1 VA 2 VA 4 SPE 1 SPE 2 SPE 3 CS 1 CS 2 CS 3 MM 1 MM 2 MM 3 SP 1 SP 2

0.73 0.81 0.79 0.69 0.85 0.86 0.79 0.76 0.81 0.78 0.83 0.83 0.69 0.68 0.83

OHE 1

0.77 0.78

OHE 2

0.86

CI 1

0.91

CI 2

0.84

SP 3

Customer experience in banks

Convenience

0.74

105

Servicescape 0.71

Employees 0.72

Online functional elements

0.68

Presence of other customers

0.67

Online aesthetics

0.63

0.60

Customization

0.68

Value addition

Customer Experience

0.69

Speed 0.68

Core service 0.62

Marketing-mix 0.69

Service process 0.56

Online hedonic elements 0.70

Customer interaction

Note: The items of final experience scale are depicted in Appendix (in bold)

Figure 2. CFA standard coefficients for higher order model

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Table VIII. Nomological validity assessment

Factor

1

2

3

4

5

6

7

8

9

10

11

CON SS EMP OFE POOC OA CUS VA SPE CS MM SP OHE CI SAT

1 0.46 0.45 0.41 0.39 0.37 0.34 0.36 0.41 0.41 0.46 0.40 0.35 0.43 0.29

1 0.46 0.39 0.40 0.40 0.36 0.41 0.42 0.40 0.40 0.39 0.32 0.40 0.35

1 0.38 0.39 0.43 0.36 0.37 0.41 0.37 0.35 0.43 0.30 0.43 0.20

1 0.38 0.39 0.32 0.38 0.43 0.40 0.39 0.38 0.27 0.42 0.36

1 0.46 0.35 0.37 0.37 0.37 0.36 0.37 0.34 0.37 0.36

1 0.32 0.32 0.39 0.43 0.26 0.32 0.39 0.31 0.32

1 0.36 0.32 0.39 0.25 0.32 0.32 0.39 0.24

1 0.41 0.38 0.36 0.38 0.40 0.46 0.38

1 0.40 0.34 0.37 0.33 0.45 0.27

1 0.28 0.44 0.31 0.38 0.40

1 0.34 0.25 0.35 0.25

12

13

14

1 0.25 1 0.50 0.24 1 0.31 0.27 0.27

15

1

Note: All correlations are significant at: 0.01 level (two-tailed)

CON 0.66

SS 0.64

EMP

0.63

POOC

0.62

OA

0.60

CUS

0.54

VA SPE

Figure 3. Nomological validity assessment model

0.64

OFE

S1

0.62 0.64

Customer Experience

0.60

Customer Satisfaction

0.79

S2

0.78

0.63

S3

CS

0.55

MM

0.62

SP

0.50

OHE

0.73

0.65

CI

holders put importance on servicescape of the banks, whereas the graduates gave more value to presence of other customers, customization and value addition than other education-levels. The professionals were more inclined towards online environment as online aesthetics and online hedonic elements as compared to others. The income-level


Factor

Gender (F-value)

Marital status (F-value)

Age (F-value)

Education level (F-value)

Income (F-value)

CON SS EMP OFE POOC OA CUS VA SPE CS MM SP OHE CI

8.218 * * 3.884 * 0.086 1.453 5.553 * 10.454 * * * 0.009 2.767 3.110 0.469 0.877 4.757 * 0.832 2.448

3.492 2.246 0.107 4.049 * 3.800 7.708 * * 0.278 2.226 2.172 0.223 0.148 0.673 0.492 0.013

2.487 * 1.878 1.181 1.359 1.546 1.743 4.382 * * 2.263 2.086 1.967 0.549 0.764 0.076 2.413 *

2.198 2.996 * 0.965 1.070 3.441 * * 2.695 * 2.960 * 3.455 * * 0.601 2.998 * 0.829 1.655 2.754 * 0.980

2.631 * 3.487 * * 1.617 0.590 1.401 2.352 3.654 * * 0.662 1.498 1.790 0.912 1.034 0.142 1.735

Notes: Significant at: *0.05, * *0.01 and * * *0.001 levels; n ¼ 624

of the customers were significantly related to convenience, servicescape and customization. As compared to the lower income-levels, higher income-levels as Rs 30,000 and above gave high importance to convenience, servicescape and customization as compared to other income-levels. Discussion and implications A 41-item scale has been developed to measure retail banking experiences of the customers in India. The results reveal that customer experience has been conceptualized and evaluated as a 14 factor construct consisting of convenience, servicescape, employees, online functional elements, presence of other customers, online aesthetics, customization, value addition, speed, core service, marketing-mix, service process, online hedonic elements and customer interaction. Psychometrically, the scale exhibits internal consistency and remains consistent across different samples. The scale surpasses all the reliability and validity tests (construct, convergent, discriminant and nomological). The results of the study demonstrated that out of 14 factors, 11 factors appears as highly significant factors among which “convenience” emerges out to be as the most important factor of customer experience in banking sector, which is followed by other highly significant factors as customer interaction, employees, speed, servicescape, core service, online functional elements, presence of other customers, value addition, service process and online aesthetics. While the remaining three factors, i.e. marketing-mix, customization and online hedonic elements are moderately significant factors of customer experience. The importance order of these factors reflects some similarities and disparities with prior studies including Grace and O’Cass (2004), Al-Eisa and Alhemoud (2009), Jain and Bagdare (2009), Knutson et al. (2009), Walter et al. (2010), Karatepe et al. (2005) and Athanassopoulos et al. (2001). Among these 11 highly significant factors, four factors, i.e. convenience, servicescape, online aesthetics and presence of other customers are highly associated with the demographic variables as shown in Table IX whereas five highly significant factors, i.e. online functional elements, value addition, core service,


Table IX. Relationship of customer experience factors and demographic variables

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service process and customer interaction are reasonably related with these demographic variables. And remaining two highly significant factors, i.e. employees and speed factors are unaffected from the demographic differences of the respondents. Despite this, out of three moderately significant factors (marketing-mix, customization and online hedonic elements) two factors namely customization and online hedonic elements are significantly affected by the demographics of the customers. According to Berry et al. (2002), experience of the customer is affected at its every touch point with the organization. Therefore, it can be concluded that along with the highly significant factors the moderately significant factors possess equal importance in assessing the experiences of the customer. The implications of the study are This study methodologically contributes to existing customer experience measurement studies. In each phase of scale development, different samples are used to test and validate the developed model, which gives strong empirical evidence of robustness of the developed model. This measurement scale can be used to measure customer experience in today’s banking environment. With this rapidly changing environment, in comparison to branch banking; customers are also availing the banking services through online channels. Therefore, the total experience delivered by the organizations depends on both the offline and online environment provided by the banks to their customers. So, the results of the current study provide a more absolute coverage and understanding of various touch points, i.e. both online and offline used in measuring customer experience. To the best of our knowledge, this study also represents the foremost studies for developing a validated tool to measure the experiences of the bank’s customers. Along with academic field, this scale will also be very useful in marketing practices. First, the banking organizations can easily rely on this sector-specific measurement scale for measuring experiences of their customers. Therefore, improvement gaps in the organizations can be identified, by assessing the performance scores on every element. Second, in addition to value, quality and satisfaction level, this scale will now be a tool for the banking organizations to measure customer experience. This implies that organizations can evaluate the statistical association between all these four entities for their customers. In this manner, organizations will be able to comprehensively understand the entire journey of customer’s decision, i.e. from pre-purchase to post-purchase. Third, this scale will be very important from strategic point of view. The bank managers can find out the relative importance of all 14 factors of customer experience in forecasting customer’s satisfaction. Therefore, at the time of scarcity the management can pay attention to the important factors and can satisfy their customers even with the limited available resources. Next, with the help of this scale bank managers can compare their current performance scores with the past performance scores or can set a benchmark across all the factors on a customary basis and can set up some early warning systems which feedback into the decisional system of resource allocation. For remedial purpose, some acceptable level of thresholds can be defined for these factors. Fifth, banking organizations can utilize the scale in order to determine discrete customer segments possessing varied perceptions in relation to customer experience. The cluster profiles of each segment can offer vital information about how to work on these segments for improvement initiatives of customer experience. Sixth, bank managers can utilize the scale on a longitudinal basis or on an


ad hoc temporal basis, so that they can be able to compare customer’s perception about the services of their respective bank with that of the competitors. Subsequently, in multi-branch banking organizations this scale can be employed to evaluate the experiences of different branch customers and hence the relative performance of the branches can be tracked over time. Eighth, bank managers can use this scale to compare the perceptual ratings of customer’s minimum and desired experience levels. Such a comparison will help the bank managers to pinpoint those specific areas where the gap exists. Ninth, this experience scale can be used to evaluate the strengths and weaknesses of a bank relative to its competitive banks. Finally, in banks frontline employees are the ones who directly affect the experiences of a customer. Thus, this experience scale can be administered to both the front-line employees and the customers concurrently in order to compare their experiences related to service encounter. Limitations and directions for future research There are some cautions, which must be raised with any of the scale development processes. These are first; we have developed a sector-specific scale for the banking industry of a developing country but due to the cultural differences the results of the study may vary in case of developed countries. Hence, further research should be accomplished to validate this measurement scale in developed countries. Second, the instrument is validated on the retail banking customers. Further research should be carried out on the customers on other banking business segments such as wholesale banking, treasury, etc. Third, this current scale does not show the positivity or negativity of the experience. So, in future the positive and negative worded adaptation of the scale should be explored and their impact on customer’s satisfaction can be investigated. Fourth, we have employed factor analysis, but with factor analysis certain level of subjectivity is required for identifying and labelling of variables. Fifth, a generalize scale should be developed so that it can be applied in other service sectors. Next, the scale can be employed individually in different types of banks as public, private, foreign, etc. Their results can be further compared and analyzed to investigate that which types of banks are lacking in delivering positive experiences to their customers. Finally, we have considered only one outcome variable, i.e. customer satisfaction in this study, more outcome variables such as loyalty, value, profitability and others should be added in the measurement model to get more concrete results. References Addis, M. and Holbrook, M.B. (2001), “On the conceptual link between mass customization and experiential consumption: an explosion of subjectivity”, Journal of Consumer Behavior, Vol. 1 No. 1, pp. 50-66. Aldas-Manzano, J., Lassala-Navarre, C., Ruiz-Mafe, C. and Sanz-Blas, S. (2009), “The role of consumer innovativeness and perceived risk in online banking usage”, International Journal of Bank Marketing, Vol. 27 No. 1, pp. 53-75. Al-Eisa, A.S. and Alhemoud, A.M. (2009), “Using a multiple-attribute approach for measuring customer satisfaction with retail banking services in Kuwait”, The International Journal of Bank Marketing, Vol. 27 No. 4, pp. 294-314. Alfansi, L. and Sargeant, A. (2000), “Market segmentation in the Indonesian banking sector: the relationship between demographics and desired customer benefits”, International Journal of Bank Marketing, Vol. 18 No. 2, pp. 64-74.


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Table AI. List of items

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

(CON 1) (CON 2) (CON 3) (CON 4) (CON 5) (SS 1) (SS 2) (SS 3) (SS 4) (SS 5) (EMP 1) (EMP 2) (EMP 3) (EMP 4) (EMP 5) (OFE 1) (OFE 2) (OFE 3) (OFE 4) (POOC 1) (POOC 2) (POOC 3) (POOC 4) (OA 1) (OA 2) (OA 3) (OA 4) (CUS 1)

The location of the bank is at a convenience place The operating hours of the bank are convenient and sufficient The ATMs of the bank is at the convenient locations The bank provides you proper information The statements and letters sent by the bank are clear The cleanliness of the bank is excellent The exterior appearance of the bank is visually appealing The physical layout of the equipments and furnishings in the bank are comfortable The ambient conditions such as temperature, ventilation, noise and odour of the branch are good The signs, symbols, advertisements, boards, pamphlets and other artifacts in the bank are properly placed The employees of the bank are social and friendly The employees of the bank are capable enough to deliver you error-free services The employees of the bank deliver services promptly The employees of the bank are willing to solve the problems of the customers The employees of the bank always help out the customers You can easily login/logout on the bank’s web site The links are problem free, accurate and pages download quickly The functioning of the web pages is proper The web site of the bank possesses up-to-date and error-free information The presence of other customers in the bank irritates you The presence of other customers in the bank gives you social surrounding The number of customers affects the reputation of the bank in your mind The recommendation made by other customers affects you The presentation quality of the bank’s web site is high The design elements of the bank’s web site are innovative The information architecture of the bank’s web site is clear The language of the bank’s web site is easily understandable The bank offers a range of credit facilities that meets your specific requirements (continued)

116

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JM2 9,1 Appendix

Item Item Item Item Item Item Item Item Item Item Item Item Item Item Item Item Item Item Item Item Item Item

29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50

(CUS 2) (CUS 3) (CS 1) (CS 2) (CS 3) (VA 1) (VA 2) (VA 3) (VA 4) (SPE 1) (SPE 2) (SPE 3) (MM 1) (MM 2) (MM 3) (SP 1) (SP 2) (SP 3) (OHE 1) (OHE 2) (CI 1) (CI 2)

The bank is capable to alter its products/services to meet your needs The bank helps you at the time of financial emergencies The bank is capable to handle the complaints The transactions of the accounts are proper and confidential The bank provides all types of services The employees of the bank gives you personalized attention The bank offers some types of gifts or incentives The additional services provided by the bank are valuable for you Your bank provides useful innovative services You do not have to stand in the queues for the long time The bank gives the prompt responses for your queries The bank delivers its promises on time The bank promotes its products/services effectively The promotions of the bank are attractive The bank offers its products/services at competitive prices The bank has standardized and simplified delivery process The bank is capable to tell you the exact time of service completion The grievance procedure of the bank is effective The web pages of the bank do not freeze any information given by you You feel secure while transacting through bank’s web site You try to avail the self-banking services offered by the bank willingly You communicate with other customers of the bank freely



Table AI.


This article has been cited by: 1. Imran Khan, Zillur Rahman. 2017. Brand Experience and Emotional Attachment in Services: The Moderating Role of Gender. Service Science 9:1, 50-61. [CrossRef] 2. Lova Rajaobelina. 2017. The Impact of Customer Experience on Relationship Quality with Travel Agencies in a Multichannel Environment. Journal of Travel Research 3, 004728751668856. [CrossRef] 3. GargRuchi Jain Ruchi Jain Garg [email protected] KumarVinod Vinod Kumar [email protected] Vandana Vandana [email protected] School of Business, ITM University, Gwalior, India Department of Marketing, International Management Institute, New Delhi, India Department of Management, New Delhi Institute of Management, New Delhi, India . 2017. Factors affecting usage of e-resources: scale development and validation. Aslib Journal of Information Management 69:1, 64-75. [Abstract] [Full Text] [PDF] 4. DeshwalPankaj Pankaj Deshwal Division of Management, Netaji Subhas Institute of Technology, New Delhi, India . 2016. Customer experience quality and demographic variables (age, gender, education level, and family income) in retail stores. International Journal of Retail & Distribution Management 44:9, 940-955. [Abstract] [Full Text] [PDF] 5. Imran Khan Department of Management Studies, Indian Institute of Technology Roorkee, Roorkee, India Zillur Rahman Department of Management Studies, Indian Institute of Technology Roorkee, Roorkee, India . 2016. E-tail brand experience’s influence on e-brand trust and e-brand loyalty. International Journal of Retail & Distribution Management 44:6, 588-606. [Abstract] [Full Text] [PDF] 6. Sonal Daulatkar Department of Decision Sciences and Information Systems, National Institute of Industrial Engineering, Mumbai, India Purnima S. Sangle Department of Decision Sciences and Information Systems, National Institute of Industrial Engineering, Mumbai, India . 2016. Proposed re-conceptualization of IT business value benefits. Business Process Management Journal 22:3, 522-545. [Abstract] [Full Text] [PDF] 7. Vishal Singh Patyal National Institute of Industrial Engineering (NITIE), Mumbai, India Maddulety Koilakuntla National Institute of Industrial Engineering (NITIE), Mumbai, India . 2015. Infrastructure and core quality practices in Indian manufacturing organizations. Journal of Advances in Management Research 12:2, 141-175. [Abstract] [Full Text] [PDF]