E-Personality of the Young Indian Online Shopper: A Scale Validation

Abstract

The Internet is the most important tool for online shopping. Online shopping in India is growing at an exponential rate and the trend is expected to continue in the years to come. The current research was undertaken to understand the personality traits of young online shoppers. There is a need to develop appropriate scales for measuring the traits which are dominant, and hence applicable in the Indian context. As part of the review of previous literature as well as exploratory studies conducted by various experts in the field, the author developed a pool of items related to the e-personality traits of the online shoppers. Using exploratory factor analysis (EFA), confirmatory factor analysis (CFA), reliability and validity of the scale were confirmed. A four-factor scale consisting of innovativeness, need for uniqueness, novelty seeking behaviour and opinion leadership was developed. This study is the first step towards identifying factors that influence the young Indian online shopper’s personality or e-profile.

Keywords

E-personality India scale validation online shopping

Introduction

India’s rapidly expanding Internet user base, estimated at 250 million currently, is fuelling interest of global investors in domestic e-commerce (The Times of India, 2014). E-tailing is a buzz and is emerging in a promising way, especially in emerging countries such as India (Das, 2016). The Assocham report states that the Indian Internet shopping market was worth USD 8.5 billion in 2012, grew to USD 16 billion (88 per cent growth) in 2013 and could reach USD 56 billion by 2023. Nearly 90 per cent of online shoppers in India belong to the 18–35 years age group (Misra, 2014). This segment of the young generation on the Internet has emerged as the driving force behind the growth of the e-commerce industry in India. This segment has incremental purchasing power (Kohli, 2006).

The younger generation in India use the Internet extensively and they spend a lot of time browsing websites (Chou, 2001; Chou, Wu & Chen, 2011; Comegys & Brennan, 2003; Gupta, Handa & Gupta, 2008; Jones, Johnson-Yale, Millermaier & Pérez, 2009; Odell, Korgen, Schumacher & Delucchi, 2000). Vij (2007) stated that the younger generation constitutes an attractive segment for online retail, as they spend more time browsing the Internet (Gupta et al., 2008; Vij, 2007). Chen (2012) stated that younger generation, who use the Internet for social purposes, were likely to be psychologically healthy. Kim, Cho and Rao (2000) found that consumers’ lifestyles directly and indirectly affect the consumers’ purchasing behaviour over the Internet. This transformation in the lifestyle has created a drastic change in the purchasing habits and is also reflected in the personality traits of the consumers. In such a dynamic environment, knowledge of consumers’ lifestyle and the changing purchasing personality of the consumer help marketeers to understand how consumers think and select from alternatives to serve them more effectively. However, the important question that still remains is how to measure changing personality of the consumer in the online context. Thus, the purpose of the present study is development and validation of a measurement scale of e-personality of online shoppers in India.

In the following sections, we provide an overview of the existing literature on impact of personality constructs in online shopping. Subsequently, we present two studies: In study 1, we explain the development of e-personality scale and undertake exploratory factor analysis (EFA) to examine its psychometric properties (reliability and factorial validity). In study 2, we conduct confirmatory factor analysis (CFA) to cross-validate the factor structure of e-personality scale and additionally examine its discriminant and convergent validity.

Literature Review

Personality and Its Relevance

Marketing-and psychology-based research has suggested the shoppers’ personality to be an important determinant of consumer behaviour (Aaker, 1997; D’astous & Levesque, 2003; Mccrae & John, 1992). It was argued that personality is a better measure to understand the shopping behaviour of the consumers. Consumer personality has been studied across various contexts, using various measurement instruments such as brand personality, store personality and luxury brand personality (Aaker, 1997; Das, 2014; D’astous & Levesque, 2003; Sung, Choi, Ahn & Song, 2015).

Personality is described as the set of psychological characteristics that both determine and reflect how a person responds to his or her environment. The measurement of personality in terms of specific psychological characteristics is called traits. Personality traits include innovativeness (IV), dogmatism, novelty seeking behaviour (NSB), opinion leadership (OL), optimum stimulation level, need for uniqueness (NFU) and need for cognition (Schiffman & Kanuk, 2010). In addition to this, the five big personality traits are neuroticism, extraversion, openness to experience, agreeableness and conscientiousness (Costa & McCrae, 1992). Aaker (1997) defined brand personality as ‘the set of human characteristics associated with a brand’. Aaker (1997) developed a scale for brand personality and introduced the five dimensions of sincerity, excitement, competence, sophistication and ruggedness. Brand personality evolves through product-related attributes, category associations, advertising styles, symbols and logos, and price and distribution outlets.

In contrast, the human personality is formed on the basis of behaviour, physical characteristics, demographic characteristics, attitudes and beliefs (Aaker, 1997; Azoulay & Kapferer, 2003; Park, 1986). According to Sullivan (2013), personality could be defined only in terms of the reactions of an individual towards other people in recurrent interpersonal situations in life. The key differentiator is the fact that an individual’s behaviour determines perceptions of human personality, whereas behaviour plays no role in conceptualizations of brand personality.

D’astous and Levesque (2003) later introduced the concept of ‘store personality’. They argued that the brand personality concept does not apply fully to a store/service environment. They define store personality as ‘the mental representation of a store on dimensions that typically capture an individual’s personality’. They define store personality in terms of five dimensions: enthusiasm, sophistication, unpleasantness, genuineness and solidity that turned out to be different from brand personality dimensions. Das, Guin and Datta (2013) studied several store personality antecedents: store ambience, product style and variety, general attitudes towards the retailer. They also studied how store name and carried brand name affect the personality dimensions of a departmental store, such as sophistication, empathy, dependability, authenticity and vibrancy. In contrast, these studies were lacking in the online domain.

An online shopping website is much like a store and fulfils all the functions of a store. A well-developed online shopping website provides various payment services and buy and return products services. Some web stores also enable consumers to chat or even talk to a live salesperson during the purchase process. Because of the similarities between a web store and an offline store, the website personality construct is likely to be more similar to store personality than to brand personality. Therefore, the store personality scale was later applied by Poddar, Donthu and Wei (2009) to the online domain, and it formed the basis of the website personality. Website personality refers to the mental representation of a website store on dimensions that are similar to and reflect the dimensions of human personality. Poddar et al. (2009) and Shobeiri, Laroche and Mazaheri (2013) studied five dimensions of the web personality. According to Poddar et al. (2009), an enthusiastic website is one that effectively uses its structure and design to create a lively, friendly and welcoming atmosphere for the visitors. Schrammel, Köffel and Tscheligi (2009) stated that the relationship of personality traits and usage patterns in online communities provides clear and mostly expected results. Hence, it is important to delve deeper into the personality of shoppers in order to fully comprehend their online shopping behaviour.

Further, an attractive website is based on an in-depth research into getting insights in the consumers’ interest in the looks and uniqueness of the product, and how it can influence other people. Therefore, there is a need to further study the personality traits innovativeness, dogmatism, novelty seeking behaviour, opinion leadership, optimum stimulation level, need for uniqueness and need for cognition in the Internet context. Poddar et al. (2009) underlined the importance of website congruity as a fundamental principle of website management and strategy. Rezaei, Ali, Amin and Jayashree (2016) stated that website personality traits, that is, solidity, enthusiasm, genuineness, sophistication and unpleasantness, positively influence utilitarian web browsing, hedonic web browsing and online impulse buying. They do not include website personality traits or e-personality which might be manipulated to influence online shoppers. Even more beneficial would be the study which shows how to manipulate website personality traits.

Personality Traits and Online Consumer Behaviour

The Internet is changing the way people work, live and learn with one of its biggest impact being on the way people shop. Bosnjak, Galesic and Tuten (2007) studied online purchase intention using personality constructs. They investigated the hierarchical model of personality of four different levels. These are surface traits (closely related to behaviour intention), situational traits (associated with involvement), compound traits (self-efficacy) and elemental traits (neuroticism, conscientiousness, extraversion, openness and agreeableness) and suggested that the higher one’s affective involvement, the lower one’s agreeableness, and the more open one is for new experiences, the higher is one’s willingness to buy products and services online. Kalmus, Realo and Siibak (2011) focused on the strongest predictors of individual differences in the Internet usage, taking into account personality traits, socio-demographic variables and indicators of habits and lifestyle. They suggested that people, higher in openness to experience and in neuroticism tend to use the Internet more for social media and entertainment. This observation is also consistent with other studies, where openness to experience was found to be positively related to entertainment usage. Das, Echambadi, McCardle and Luckett (2003) found that the need for cognition as a trait has a strong direct effect on information seeking behaviour on the web. Sahney, Ghosh and Shrivastava (2010) found that extraversion and introversion, risk-taking, excitement and pleasure seeking, and being tech-savvy affected the intention to reserve railway tickets online in India for long distance tours and travels. Sharma (2008) stated that consumers with low dogmatism tend to engage in online consumption.

Innovativeness (IV) as a personality trait has been well recognized in the literature. Wang and Cho (2012) found that fashion IV is more involved in formulating behavioural intentions that influence attitude towards customized online apparel. IV has a strong effect on consumer adoption of the Internet for shopping (Citrin, Sprott, Silverman & Stem Jr, 2000; Hui & Wan, 2004). Global IV, involvement with the Internet and Internet IV all play a role in explaining online buying and buying intentions (Goldsmith, 2002).

Therefore, there is a need for an in-depth study of the impact of IV on current and future buyers, especially for the more adventuresome and innovative consumers. IV and OL predict the somewhat acceptance of new computer technology (Girardi, Soutar & Ward, 2005). Personal influence has been conceptualized as OL in studies of the diffusion process, and many marketing researchers have attempted to assess the relationship between OL and product IV and adaption behaviour (Childers, 1986; Robertson & Myer, 1969). The term ‘opinion leader’ was developed by Lazarsfeld, Berelson and Gaudet (1944) and defined as ‘individuals who are likely to influence other persons in their immediate environment’ (Katz & Lazarsfeld, 1955). OL scale was originally developed by King and Summers (1970). This scale was used in the study of online pharmacy shopping (Wiedmann et al., 2010). OL with reference to their knowledge and experience refer to the ability of certain consumers to influence other individuals as a source of advice concerning the subject of online shopping. They feel comfortable purchasing products via the Internet and communicate their online shopping experiences to others (Wiedmann et al., 2010).

Novelty seeking has two components: inherent novelty seeking and actualized novelty seeking. Inherent novelty seeking is viewed as the desire of the individual to seek out novel stimuli; actualized novelty seeking represents the actual behaviour by the individual to acquire novel stimuli. ‘An individual’s novelty-seeking behaviour affects his or her behaviour of seeking new information about products and plays a role in initial product adoption’ (Hirschman, 1980). Khare, Khare and Singh (2010) stated that most Indian youth feel confident about the Internet for accessing information and interacting with their friends. This construct related to search of new information is effectively novelty-seeking and is an important determinant of online shopping behaviour.

Internally, people tend to be satisfied when they find themselves to be unique and separable from others and externally, through posing independence, people can be evaluated positively since independence is considered a quality of strong character as well as autonomy. This perception of uniqueness from others is referred to as ‘a need for uniqueness’ (Snyder & Fromkin, 1977). Lynn and Harris (1997) found a positive relationship between the self-attributed NFU and the tendency to be a consumer-innovator. NFU in this research is important because young generations try to differentiate themselves from preceding generations through their consumption behaviour. NFU and status aspiration have significant impact on forming favourable attitudes towards e-customized products, and they may be directly associated with the purchase behaviour (Park, Han & Park, 2013). Ruvio, Shoham and Makovec Brencic (2008) proposed 31 items and developed the NFU scale to determine cross-cultural invariance in Israel, Palestine and Slovenia. Lee and Leizerovici (2011) stated that unique individuals have a desire to widen their personal network, but not if they have a need to avoid similar others.

Therefore, a need has been found to develop a comprehensive scale in order to measure e-personality; the first reason being that, after examining the existing scales, the observation that most of the developed instruments were industry-specific and were designed while concentrating on the individual constructs (IV, dogmatism, NSB, OL, NFU and need for cognition). Different researches have attempted to make out factors which can be used for the evaluation of e-personality. These personality factors (IV, NSB, OL and NFU) directly or indirectly impact the purchasing behaviour of a consumer over the Internet. For example, nowadays consumers take advice and latest information from friends and relatives through website communities before buying the products and services. Therefore, these existing scales cannot be fully applied in the e-personality for online shopper. Second, 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, this study is to develop a systematic and psychometric scale to measure the e-personality of the Indian online shoppers.

Instrument Development

In this study, we use the well-accepted scale development procedure from the existing literature (Seo & Green, 2008; Varshneya & Das, 2017). The procedure was conducted to develop the e-personality scale of the Indian online shoppers. In study 1, the potential items were identified from the literature. These items were then classified and refined to eliminate redundancies and highlight possible omissions. Data were collected and analyzed via EFA method to generate a 4-factor, 13-item scale. Study 2 used CFA method to further refine the scale and to establish the validity and reliability of the scale.

In the following section, we discuss these two studies in detail.

Study 1

Study 1 consisted of two phases: a qualitative phase and a quantitative phase. The items were generated in the qualitative phase and refined into coherent subscales in the quantitative phase.

Qualitative Phase

Item Generation: From literature review, 31 items were identified which were relevant to the personality scale associated with the Internet shopping. Out of 31 items, 8 were for IV, 8 were for NFU, 8 were for NSB and 7 were for OL. The initial item pool included items from the existing scales measuring IV (Citrin et al., 2000; Goldsmith, 2002), NFU (Park et al., 2013), NSB (Khare et al., 2010) and OL (Wiedmann et al., 2010).

Item Evaluation: The basic objective of content validity was to ensure that the items reflect the content areas encompassed by the target construct. According to Netemeyer, Bearden and Sharma (2003), development of a measure with content validity is enhanced during the early stages of scale development through the effective creation of an item pool and the subsequent evaluation of items by expert judges involving evaluation of the item pool by eight experts (two academicians, two industry experts and four PhD students). The purpose of this stage was to make the item wording clear and unambiguous. Hence, the expert panel thoroughly examined the 31 items. They identified redundant items and suggested some rewording of items and the addition of some other items. As a result of the item analysis, the scale finally consisted of 21items, which were then used to create a survey of e-personality of online shoppers for use in the quantitative phase of the study.

Quantitative Phase: Exploratory Factor Analysis (EFA)

In the quantitative phase of study 1, we explore the dimensionality of the initial e-personality (21 items) using Statistical Package for the Social Sciences (SPSS) 20. The scale items selected and the corresponding number of factors specified is the result of item analyses and extensive EFA. This analysis led the scale developer to a set of items that can be confirmed successfully.

Method

Procedure and Participant

A structured questionnaire with 21 items was distributed among undergraduate and postgraduate students. Apart from that, some demographic variables were linked to the frequency of online apparel purchase, and amount spent on online apparel purchased were also included in the questionnaire. Convenience sampling was used for data collection. The participants responded to each item measured using a 7-point Likert scale (1 = strongly disagree to 7 = strongly agree).

Responses were received from 200 individuals comprising 95 females and 105 males. Participants ranged in age from 18 to 30 years. Approximately 55 per cent of participants were between 22 and 25 years. The majority of respondents’ family annual income was from INR 0.5 to 1.5 million (65.5 per cent). Education level wise, undergraduates were 46 per cent and postgraduates were 54 per cent. For further details, refer to Table 1.

Table 1.

Demographic Profile and Internet Shopping Characteristics for Sample 1 and Sample 2

Sample Size	Sample1	Sample 2
	200	250
Gender
Male	52.5%	51.6%
Female	47.5%	48.4%
Age
18–21	28%	25%
22–25	55%	60%
26–29	16%	15%
30 and above	1%	0%
Annual income
INR 0.3 to 0.5 million	8%	7%
INR 0.5 to 1 million	27.5%	28%
INR 1 to 1.5 million	38%	32%
INR1.5 to 2. million	19.5%	23%
1 > INR 2 million	7%	10%
Education
Undergraduate	46%	46%
Postgraduate	54%	54%
Time spent/day on the Internet
Less than 1 hour/day	1%	1%
1–4 hours/day	82%	73%
5–8 hours/day	14%	14%
More than 8 hours/day	3%	12%
Frequency of online apparel purchase in last 12 months
0 times	1%	1%
1–3 times	66%	59%
4–6 times	20%	19%
7–10 times	10%	11%
More than 10 times	3%	10%
Amount spent on online apparel purchase in last 12 months
Rs 500–1500	1%	0%
Rs 1600–2100	23%	22%
Rs 2200–2700	19%	20%
Rs 2800–3100	18%	18%
More than Rs 3100	40%	39%

Source: Authors’ findings.

Results

The assumption of a normal distribution was tested and satisfied before an EFA was conducted. We used principle component factor analysis followed by the varimax (orthogonal) rotation. The key statistics associated with factor analysis are being explained briefly.

Bartlett’s test of sphericity is a test used to examine the hypothesis that variables are uncorrelated in the population, that is, the population correlation matrix is an identity matrix. Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy is an index used to examine the appropriateness of factor analysis. High values (between 0.5 and 1.0) indicate that factor analysis is an appropriate analysis. For the given data, Bartlett’s test of sphericity (approx. chi-square = 663.927, df = 78, p < 0.05) was found to be significant, which implied that the variables are correlated in the population. The KMO measure of sampling adequacy of 0.69 indicated that the sample could be subjected to factor analysis. Multiple criteria were used to determine the number of factors. More specifically, items with low factor loading (<0.40) and high cross-loading (>0.40) were eliminated (Forsythe, Liu, Shannon & Gardner, 2006). After inspection of item content for domain representation, eight items (PER4, PER5, PER6, PER7, PER8, PER13, PER17 and PER18) were deleted. The remaining items were submitted for further EFA. The resulting scales demonstrated good reliability with coefficient alphas from 0.69 to 0.75 (see Table 2).

The four dimensions emerging for personality scales in the online shopping included IV, NFU, NSB and OL. The four-factor analysis of personality accounted for approximately 62.50 per cent of the total variance. The total variance explained exceeded the minimum of 50 per cent, as suggested by Hair, Black, Babin, Anderson and Tatham (2010). Furthermore, all retained items had 0.62 or greater loading on the dominant factors and less than 0.40 loading on other factors, confirming the independence of the constructs and ensuring maximum internal consistency. These are considered to be excellent loadings (Comrey, 1988).

Study 2: Confirmatory Factor Analysis (CFA)

The purpose of study 2 was to conduct CFA test to confirm the relationships between observed variables under each hypothesized construct. This technique is more flexible and more powerful as compared to EFA. Analysis of moment structure (AMOS) version 20 (Byrne, 2013) was used to perform this analysis with new a set of 250 respondents.

Method

Procedure and Participant

A structured questionnaire with 13 items was distributed among undergraduate and postgraduate students. Apart from that, some demographic variables were linked to the frequency of online apparel purchase, and amount spent on online apparel purchased were also included in the questionnaire. Convenience sampling was used for data collection. Participants responded to each item measured using a 7-point Likert scale (1 = strongly disagree to 7 = strongly agree).

Responses were received from 250 individuals comprising 121 females and 129 males. Participants ranged in age from 18 to 30 years. Approximately 60 per cent of participants were between 22 and 25 years. The majority of respondent’s family annual income was INR 0.5 to 1.5 million (60 per cent). Education level wise, undergraduates were 46 per cent and postgraduates were 54 per cent. For further details, refer to Table 2.

Table 2.

Exploratory Factor Analysis Findings: Factor Loadings and Cronbach’s Alpha Values

Items	Cronbach’s Alpha	Factor 1-IV	Factor2-NFU	Factor3-NSB	Factor4-OL
PER1: Know more about shopping over the Internet than other people.	0.75	0.818
PER2: Compared to my friends, I do little shopping over the Internet.		0.815
PER3: If I heard that a new product that I was interested in is available over the Internet, I would be interested enough to buy it.		0.789
PER9: I would prefer to have things custom-made than to have them ready-made.	0.73		0.671
PER10: I am very attracted to rare products.			0.741
PER11: I enjoy online shopping at stores that carry merchandise which is different and unusual.			0.814
PER12: I often seek out information about new products and services.			0.622
PER14: When I go for shopping, I find myself spending very little time checking out new products and services.	0.69			0.628
PER15: I take advantage of the first available opportunity to find out about new and different products.				0.804
PER16: I am continually seeking new product experiences.				0.857
PER19: If I talk with my friends or others about online shopping, I try to convince them of my ideas.	0.73				0.651
PER20: I think that others regard me a good source of advice on the subject of online shopping.					0.856
PER21: 1So far as the subject of online shopping is concerned, I would rather tell others about it than being told about it.					0.746

Source: Authors’ findings.

Notes: IV = Innovativeness, NFU = need for uniqueness, NSB = novelty seeking behaviour and OL= opinion leadership;

items deleted from item-total correlation: PER4, PER5, PER6, PER 7, PER 8, PER 13, PER 17 and PER 18.

Results

CFA was done to cross-validate the four-factor model of e-personality obtained from EFA: IV, NFU, NSB and OL. The indices of chi-square, goodness-of-fit index (GFI), comparative fit index (CFI) and root mean square residual (RMSEA) were assessed to evaluate the overall fit of one measure. Further, criteria were established for fit indices based on a review of ‘rule of thumb’ and empirical research (Hu & Bentler, 1999; Joreskog & Sorbom, 2004). Specific cut-offs were set for CFI (>0.90) and RMSEA (<0.06). The CFA indicated that the personality scale initially fit the model: chi-square = 142.02, df = 59; GFI = 0.92; CFI = 0.90 and RMSEA = 0.07. Further modification was needed for this scale. One item (PER 14) was removed because it was not considered to be conceptually valid for NSB. In the final 12 items, four dimensions scale of personality had a good model fit: χ² = 82.65, df = 47; GFI = 0.94; CFI = 0.95 and RMSEA = 0.05 (Hu & Bentler, 1999; Joreskog & Sorbom, 2004). The finalized factors, post CFA along with their reliability measures are shown in Table 3 with the final model shown in Figure 1.

Table 3.

Confirmatory Factor Analysis: Items, Average Variance Extracted and Composite Reliability

Items	CR	AVE	IV	NFU	NSB	OL
PER1: Know more about shopping over the Internet than other people.	0.90	0.70	0.77
PER2: Compared to my friends, I do little shopping over the Internet.			0.78
PER3: If I heard that a new product that I was interested in is available over the Internet, I would be interested enough to buy it.			0.71
PER9: I would prefer to have things custom-made than to have them ready-made.	0.86	0.56		0.50
PER10: I am very attracted to rare products.				0.59
PER11: I enjoy online shopping at stores that carry merchandise which is different and unusual.				0.84
PER12: I often seek out information about new products and services.				0.64
PER15:I take advantage of the first available opportunity to find out about new and different products.	0.70	0.61			0.61
PER16: I am continually seeking new product experiences.					0.78
PER19: If I talk with my friends or others about online shopping, I try to convince them of my ideas.	0.84	0.70				0.64
PER20: I think that others regard me a good source of advice on the subject of online shopping.						0.75
PER21: So far as the subject of online shopping is concerned, I would rather tell others about it than being told about it.						0.61

Source: Authors’ findings.

Notes: CR = composite reliability, AVE = average variance extracted,

IV = innovativeness, NFU = need for uniqueness, NSB = novelty seeking behaviour and OL = opinion leadership; items deleted = PER 14.

Figure 1.

Source: Authors’ findings.

Convergent and Discriminant Validity

Factor loading, composite reliability and the average variance extracted (AVE) are used to assess the convergent validities. All loadings, ranging from 0.50 to 0.84, were statistically significant (p < 0.001). The composite reliability exceeds the acceptable criteria of 0.7 (Fornell & Larcker, 1981; Hair et al., 2010) for most of the factors, and the AVE for all latent variables is greater than the threshold value of 0.5 (Fornell & Larcker, 1981) (see Table 3). Overall, the model supports the convergent validity. As suggested by Fornell and Larcker (1981), the discriminant validity can be assessed by comparing the AVE with the corresponding inter-construct square correlation estimates. AVE values for each latent construct were found more than the square of the inter-construct correlations as given in Table 4. Thus, the measurement model reflects good discriminant validity (Hair et al., 2010).

Conclusion

This study measured four personality dimensions impacting online shopping behaviour, namely, IV, NFU, NSB and OL. The results from the two samples supported the proposed measure of personality scale associated with online shopping. These scales demonstrated further evidence of construct validity, as the findings revealed that major online shoppers are young and aged from about 22 to 25 years. They shopped more frequently and spent more money on buying apparel. The Indian youth uses the Internet extensively and enjoys shopping online. With increase in household income coupled with changed lifestyle, these online shoppers look for innovative products and services on the Internet.

Table 4.

Discriminant Validity

	IV	NFU	NSB	OL
IV	(0.70)
NFU	0.23	(0.56)
NSB	0.21	0.21	(0.61)
OL	0.06	0.26	0.30	(0.70)

Source: Authors’ findings.

Notes: IV = Innovativeness, NFU = need for uniqueness, NSB = novelty seeking behaviour and OL = opinion leadership;

AVE (Average Variance Extracted) value for each construct appears on the main diagonal in parentheses.

Implications

A 12-item scale has been developed to measure e-personality for Indian online shoppers. The results reveal that e-personality has been conceptualized and evaluated as a sum of four factors consisting of IV, NFU, NSB and OL. Psychometrically, the scale exhibits internal consistency and remains consistent with two samples. The scale passes all the reliability and validity tests (construct, convergent and discriminant).

This study has several theoretical and managerial contributions. A theoretical contribution to knowledge is that this research is the first to develop the four dimensions of an e-personality of online shoppers. Previous research mostly focused on the effects of brand personality, store personality and website personality. The findings of this research help managers, about the ways to enhance the e-personality of a consumer. In contrast, with the majority of prior studies, which were empirical in nature and discussed individual construct related to personality, this research revealed what it takes to improve each of the four personality dimensions. Therefore, this study would serve as an understanding of the behavioural spectrum of different consumer personalities and will certainly help in formulating marketing strategies that are better targeted and more effective.

In this regard, managers need to identify the influential groups and individuals aligned with consumer personality related to their products and devise strategies (for example, giving special discounts to drive more traffic to the online stores) to get positive recommendations from such groups/individuals in order to influence potential female consumers. The attitude of female consumers is influenced more by peer recommendations than males (Nadeem, Andreini, Salo & Laukkanen, 2015). Therefore, the managers should aim to evaluate and control the quality and content of peer reviews/recommendations to influence potential female consumers. Managers should also place emphasis on improving website quality, services it offers and provide access to all social media plug-ins to target novelty seeking consumers.

Limitations and Future Research

This study focussed on the relevance of understanding e-personality of online shoppers in India. However, another approach that can be used in future research is to understand e-personality of online non-shoppers (potential shoppers) and thereby analyze ways to inﬂuence them. Secondly, even though the authors aimed at adverse population inclusion for the purpose of this study, it still is a result of convenience sampling, and therefore may require further veriﬁcation through random and independent samples. Thirdly, India is a vastly heterogeneous country; therefore, future research should examine the e-personality scale in larger and more varied samples spread across different geographical regions in India. It is also recommended that this scale be tested in other parts of the country, including rural areas by preparing a questionnaire in Hindi and other regional languages. Therefore, the factors provided in our research may be limited and other contexts or categories should be tested out to ensure generalizability. Future research should be tested with addition of more factors like need for cognition, risk-taking personality and five big factors which are neuroticism, conscientiousness, extraversion, openness and agreeableness.

Footnotes

Acknowledgements

The authors are grateful to the anonymous referees of the journal for their extremely useful suggestions to improve the quality of the article. Usual disclaimers apply.

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