Simulating Attachment to Pure-Play Fashion Retailers - Academy of ...

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presentation was only available in 5.2% of cases (Kim, Kim & Lennon, 2011). However, studies show that video can improve the attractiveness of products, build brand awareness (Pentina, Amialchuk & Taylor, 2011), and improve the mood of consumers (Park, Lennon & Stoel, 2005). Product movement can also help to overcome the limited physical experience consumers are able to have shopping online by evoking product functionality (Li, Daugherty & Biocca, 2002). Moreover, simulating touch and experience with product videos can evoke cognitive, affective and conative responses (Park et al., 2005). Product Recommendations: One method of offering an individualized service is through product recommendations (Cho & Fiorito, 2009). Recommendations can be communicated through a text based suggestion coupled with an image. Recommendations have been defined as the extent to which a website “gives suggestions, ideas or options to enable further examination of a specific product or to trigger the inspection of further products” (Demangeot & Broderick, 2007, p. 885). Senecal and Nantel (2004, p.160) posit that “online recommendation sources can be sorted into three categories including other consumers (friends, relatives, and acquaintances), human experts (salespersons, independent experts) and expert systems (such as recommender systems)”. This study will focus on expert systems, which have been found to be the most influential recommender source (Senecal & Nantel, 2004). Trust, Loyalty and Purchase Intentions: An increased need for the role of trust is covariant with increasing amounts of information and alternatives presented to sovereign consumers (Chung & Shin, 2010). Trust has been found to determine loyalty to a website (Ridings, Gefen & Arinze, 2002; Gefen, 2002) and can be a crucial factor for consumers to make a purchase decision (Winch & Joyce, 2006; Bart, Shankar, Sultan & Urban, 2005). Loyal customers are prepared to spend more, buy more, are easy to reach, repeatedly return and are positive promoters of a company (Harris & Goode, 2004). Loyalty intention is the consumer’s positive attitude towards the retailer that results in repeat purchase behaviour (Yen & Lu, 2008). Purchase intention has been defined as a mental state which results in the consumer’s choice to buy a product or service in the immediate future (Bigne-Alcaniz, Ruiz-Mafe, Aldas-Manzano & Sanz-Blas, 2008). Conceptual Model: Research hypotheses have been developed based on the existing literature (see Table I, in Appendix A), and are shown in Table II, in Appendix B. Methodology This study is focused on ASOS, the largest independent fashion retailer. ASOS is a pure-play fashion retailer and an industry high standard with 41% profit increase in 2011 to £26.8 million (IMRG, 2011). ASOS will be familiar to the target respondents of this study, their target audience being 16-34 year old fashion-conscious consumers (Mintel, 2011). The sample comprised of young female fashion consumers (specifically fashion retail students, in accordance with Kim and Park (2005) and Kim and Forsythe (2009)). Fashion students are likely to be more innovative than a sample from other disciplines (Workman & Caldwell, 2007). A cross sectional approach to this study was taken using self administered and online surveys. The survey questionnaire was composed of two sections; demographic and shopping frequency questions and attitude scales. The shopping frequency questions were used as a filter; respondents had to have visited the ASOS online store in the last six months and be 4
female. The second section of the questionnaire used scaled responses to a seven-point likert scale (Goldsmith & Flynn, 2005; Kim et al., 2009) (Strongly Agree- Strongly Disagree). A ‘No Opinion’ box was included (Collins-Dodd & Lindley, 2003) so as respondents who did not use or had not heard of an item would not affect the analysis. 22 items were developed to measure 6 constructs. Items which explicitly measure fashion website variables are limited, and therefore some development and adaptation was needed. However, items originated from academically validated sources. A review of all the item measures used in this study is shown in Table III in Appendix C. Empirical Results: A total sample of 688 was collected for this study, 331 were completed in paper format and 357 were completed online. The average age of respondents is 21 with a range between 16 and 43. 87.9% of the sample is under 24, making this data more applicable to young consumers. Data Analysis: This study uses both confirmatory factor analysis (CFA) and structural equation modelling (SEM). The measurement model tested in confirmatory factor analysis provided satisfactory evidence to accept the six factor model. Fit statistics indicated a good fit (χ² = 436.066, df = 194, p = .000, CMIN/DF = 2.248, GFI = .947, AGFI = .931, RMSEA = .043, NFI = .942, CFI = .967). Factor loadings including all six factors are sufficient with values exceeding .60 apart from one item in the static product presentation construct. Data was checked for reliability, convergent and discriminant validity, which were all found to be present. The data set for structural equation modelling contains 22 observed variables representing 6 latent factors. All latent factors have at least three observed variables. Overall, the fit statistics indicate a relatively good fit (CMIN/DF = 2.388, GFI = .943, AGFI = .927, RMSEA = .045, CFI = .962, NFI = .937). All of the fit statistics exceed their accepted thresholds. A summary of the structural paths and their p-values is shown in Table IV in Appendix D. The confirmation or rejection of the hypotheses can be evaluated by looking at the standardized regression weights and the p-values. Nine out of the twelve hypotheses are statistically significant. Discussion- Static versus Movement on pure-play fashion websites: In this study, static images are found to be more strongly related to directly building consumers purchase intentions (γ=0.17) than moving images (γ=0.03). This finding is at odds with previous research by Park et al., (2005) who found that the evaluation of images involving movement had a greater effect on purchase intentions than static images. However, moving images are more likely to relate to consumer loyalty (γ=0.23) compared with static images (γ=0.02). Moving images have increased vividness, and have been found to induce stronger enduring attitudes than images with moderate/low vividness (static) (Coyle & Thorson, 2001). Trust can be developed by using static (γ=0.26) and moving images (γ=0.19) in pure-play fashion websites, although there is a slightly stronger relationship between static images and trust. Expert Product Recommendations: Analysis shows that product recommendations generated by an expert source (such as a retailer) are found to directly relate to developing consumer trust and loyalty towards a pureplay fashion retailer. Studies have found links between product recommendations and trust (Hsaio, Lin, Wang, Lu & Yu, 2010; Pu & Chen, 2006; Gershoff, Mukherjee, & 5
Page 1 and 2: Abstract Purpose Simulating Attachm
Page 3: etailer) has understood the needs (
Page 7 and 8: References: Aaker, J., Fournier, S.
Page 9 and 10: IMRG. (2011). Profits jump 41% at A
Page 11 and 12: Senecal, S., & Nantel, J. (2004). T
Page 13 and 14: Appendix Appendix A Table I: Hypoth
Page 15 and 16: Appendix C Table III: Measurement S

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