Review of Professional Management
Search
1 Don Bosco College, Panaji, Goa, India
Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-Commercial use, reproduction and distribution of the work without further permission provided the original work is attributed.
Local markets are at the heart of community identity and tourism economies, rendering an understanding of shopper behaviour critical to improving consumer experience and sustainable growth. Critical factors influencing shopper behaviour in local market settings are identified in this study, including six independent variables such as proximity of markets, availability of traditional goods, influence of tourism on shopper behaviour, attitude and amicability of staff handling shoppers, cultural relevance of shopping products, and seasonal product promotions. The relationships of the variables studied are investigated by using correlation analysis, multiple regression techniques and Thurstone Case V Scaling. Results indicated that tourist activity had the highest relationship with shopper behaviour, followed by proximity to markets and staff attitude. The study provides good guidelines to develop customer engagement, utilise tourism potential and improve local products. The results add to the growing literature on consumer behaviour in local market contexts and suggest managerial strategies for fostering positive customer-staff interactions and capitalising on tourism’s influence. The dynamic nature of consumer preferences offers opportunities for further research.
Customer buying behaviour, retail landscape, proximity to local markets, Goa retail market
Introduction
Shopping behaviours in the retail industry of Goa are influenced significantly by a combination of cultural heritage, economic dynamics and changing consumer preferences. Its local markets form a colourful commercial platform for economic trading but with rich social exchanges, and more importantly, local craftsmanship preservation through which people will go for purchases, unlike mere shopping destinations—manifestations of historical richness about a region as well as variances of tastes about the demands of residents and outsiders of the markets (Abu-AlSondos et al., 2023).
The forces of globalisation have significantly transformed the retail sector in Goa. The entry of different formats of retailing, including international brands and modern shopping complexes, has increased competition and changed the expectations of consumers (Frasquet et al., 2001). These include the pricing, variety of products, quality and shopping experience as a whole.
Demographic diversity, cultural relevance and technological advancement have complicated the retail world further (Guo & Wang, 2024). Online reviews, social media and mobile commerce have transformed the way consumers interact with brands and make purchases (Kushwaha et al., 2017). The study aims to provide insights for tailoring retail strategies to local needs. The findings support data-driven decisions and sustainable growth in a culturally rich market (Sham et al., 2023).
The location of a retail store is the first variable selected, which is paramount in determining consumer shopping decisions. Areas close to popular markets, such as Mapusa and Margao, are seen to have the benefit of high foot traffic and visibility. Preferred shopping areas are considered those that offer access to multiple outlets and enable comparison among products. The proximity advantage encourages impulsive buying and also supports the selection of retailers that belong to the local community (Zulqarnain et al., 2015).
In Goa, customers have an affinity for whatever product would speak to the rich culture and heritage of the region. Availability of locally produced goods, such as Goan handicrafts, spices and traditional food items, was the second variable selected, which greatly influences the decision to shop (Méndez-Vogel et al., 2023). For the tourism market, the purchase of authentic local products is what makes them experience and take home a part of Goan culture (Chetioui et al., 2021).
Tourism, the third variable, happens to be one of the major activities in Goa and contributes substantially to the retail sector (Guo & Wang, 2024). The retailers are thus constrained to modify their product range and prices by this temporary customer. They ensure that they meet the changing tastes of residents and visitors (Zheng et al., 2020).
The attitude displayed by the staff in retail stores also forms the basis of the shopping experience was the fourth variable selected. Attentive and knowledgeable salespeople can offer information about the products to help inform customers’ purchasing decisions (Pardeshi & Khanna, 2021).
The cultural aspects of the products that are sold in retail stores were the fifth variable selected, which have especial significance whenever local festivals and functions take place. All shoppers who want to be in contact with their cultural heritage shop at retailers that sell products related to festivals such as Carnival or Ganesh Chaturthi (Méndez-Vogel et al., 2023). This provides not only a positive impulse to sales at festival times but also feeds into the emotional need of the consumer with the store (Fu et al., 2020).
Festive offers and discounts fuel peak consumer activity by instilling a sense of urgency and drawing greater footfall. These offers have a major impact on purchase decisions during peak times (Ketanbhai, 2020).
Strategically planned promotions can make retailers’ choices very crucial for shopping behaviour in the competitive retail market of Goa (Behera et al., 2023).
Theoretical Background & Literature Review
Proximity and Accessibility of Markets
The Central Place Theory proposed by Christaller in 1933 points out that consumers tend to shop in closer geographical areas because it saves time and effort (Shi et al., 2020). In places like Goa, where traffic congestion and parking constraints are prevalent, accessibility is most important, as it scores well over other factors (Mohamad et al., 2011). Also, Pei et al. (2020) claim that regular exposure to local shops boosts impulse purchasing, whereas Baker et al. (2002) point out that local retail stores possess greater potential to convert passers-by to customers.
Theory of Consumer Preferences
Based on Lancaster’s Theory of Consumer Preferences (1966), consumer behaviour is influenced by the importance consumers place on factors such as cultural relevance, uniqueness and quality. In areas such as Goa, where lifestyle is interwoven with tradition, consumer preference is towards culturally relevant products (Grönroos & Ravald, 2011).
Cultural Relevance and Consumer Preferences
The Theory of Consumer Preferences suggests that purchasing decisions are influenced by culture, distinctiveness and product quality (Feldmann & Hamm, 2015). In Goa, both locals and tourists prefer culturally significant products like local crafts and heritage goods. Studies show that consumers value authenticity and cultural identity in their purchases. Wang and Zhang (2020) and Srivastava and Thaichon (2023) argue that consumers prioritise authenticity and are more inclined to products which support cultural identity (Fisman et al., 2017). In Goa, this is noticeable for tourists and locals able to purchase goods that capture Goan traditions and lifestyle
Tourism Impact Theory
The theory by Wall (1996) claims that tourists have a particular influence on the behaviour of consumers in retail businesses. Ask anyone, and they will concede that tourists, especially those in search of authentic and local handicrafts, compel retailers to change their product mix and the way goods are sold (Wang & Chen, 2015). Tourism’s seasonal timelines create unique shopping behaviours, as noticed by Chang et al. (2023) and Rani et al. (2023), who emphasise that in addition to local gifts, clothes and food, retailers have to serve foreign patrons. The phenomenon in which tourism crosses over into retailing is apparent in Goa, where retailers take advantage of seasonal increases in tourists’ visits and use season-specific advertising and unique types of goods (Dasoomi et al., 2023).
Service Quality Theory
The Service Quality Theory emphasises that customer satisfaction is greatly driven by employee service performance and interactions (Ho & Wei, 2016). Service operators, if friendly and more attentive, help improve the shopping experience and encourage loyalty. Studies from Wu et al. (2023) and Sari (2023) demonstrate that customer service emerges as an important factor that distinguishes success in retail. According to Diaz-Gutierrez et al. (2023), with increasing competition in Goa, retailers who spend resources on training their staff to improve customer service are rewarded by feedback and referrals.
Cultural Proximity Theory
According to Hofstede (1980), Cultural Proximity Theory implies that the probability of purchase is high if products have cultural similarities and are relevant to people (Zimmermann et al., 2023). When the product belongs to the cultural values or beliefs a consumer holds, then they are more likely to make that buy (Bourg et al., 2023). In Goa, being a place of traditions and cultural heritage, the same products increase consumer involvement through culture (Kakaria et al., 2023). This is especially true for locals and tourists alike looking for authentic, culturally meaningful experiences in the region (Fisman et al., 2017).
Consumer Behaviour Theory
Consumer behaviour theory (Schiffman & Kanuk, 2007) emphasises promotions, discounts and limited-time offers as being different from other psychological stimuli that create urgency and excitement, and thus induce higher purchase rates (Zhang & Benyoucef, 2016). Consumer purchasing behaviour in Goa is particularly driven by seasonal festivals and peak travel seasons (Ligaraba et al., 2023). Studies by Kakaria et al. (2023) indicate that successful promotional campaigns entice tourists.
Objectives & Hypotheses
H1: Accessibility of local markets is positively related to consumer shopping behaviour in Goa.
H2: The availability of local and traditional products significantly enhances customer attraction toward retail stores.
H3: The presence of tourists influences product assortment and pricing strategies in Goa’s retail sector.
H4: Positive staff interactions, including friendliness and attentiveness, contribute to customer loyalty and an enriched shopping experience.
H5: The presence of culturally significant products during local festivals significantly impacts consumer purchasing behaviour.
H6: Seasonal promotions and discounts boost shopping intentions among consumers in Goa’s retail industry.
Research Methodology
Research Design
An exploratory research design has been adopted in this study for the purpose of unearthing the major characteristics of the organised retail formats that attract consumers in Goa, India. This approach easily offers an understanding of consumer preferences and behaviour in the view of dynamics developed in local retail environments (Rehman et al., 2024). The research explores consumer shopping habits as the dependent variable, controlled by six independent variables: locality to local markets, presence of local and traditional products, effect of tourism, attitude and helpfulness of staff, cultural significance of products and seasonal promotion and sales.
Target Population
The sample population for the study are the regular customers who regularly visit the organised retail outlets in Goa. It includes local residents as well as tourists visiting and interacting with the retail sector in this region (Kar, 2023).
Sample Size
A total of 320 questionnaires were distributed among the customers, but after close scrutiny, 20 responses were found wanting and were excluded due to incompleteness, and therefore, the sample size for this study was set at 300 respondents. The sample is divided evenly into 150 representing the north Goa area, including Porvorim and Baga, while 150 represent the south Goa region, including Margao and Cavelossim, thus evenly distributing representation among various geographies and demographics (Hossan et al., 2023).
Sampling Technique
Convenience sampling was adopted in this study to ensure that the best possible data could be collected from participants who were convenient to access in some of the well-organised retail outlets across Goa (McDowell et al., 2016). It was adopted because of time and resource constraints, there was a real need to ascertain quickly something that would be relevant (Selvarajan & Chandran, 2024). The study captured customer preferences in key high-traffic areas like Margao, Cavelossim, Porvorim, Baga and Panaji.
Data Collection Methods
In retrieving data for this study, primary and secondary sources were used.Primary Data: The structured questionnaire was prepared to collect primary data from the participants about their shopping behaviour, preferences and perceptions related to the identified six independent variables of the study. Participants were approached in high-traffic retail areas within Goa, especially in Margao, Cavelossim, Porvorim, Baga and Panaji, thereby creating a diverse representation of shoppers (Seock, 2009).
Secondary data: The secondary data were sourced from journals, books, internet sources and relevant previous research studies that have taken place concerning retail consumer behaviour and shopping preferences. Such data sets helped establish context and support the primary results, and therefore overall robustness of the study (Khoa et al., 2023).
Area of Survey
A set of such strategic locations was surveyed across Goa to understand consumer preferences in both North and South Goa. The chosen areas for data collection are: Margao, which is perceived to have a vibrant market feel and lots of retailing outlets; Cavelossim, which is a tourist area having shops to cater to the locals as well as visiting tourists; Porvorim, an area fast developing with the presence of both organised and relatively more traditional retail stores; and Panaji, which is the capital city of Goa, representing different kinds of consumers and retailing options (Ghosh et al., 2010).
Data Analysis Techniques
In this study, the relative importance of the six independent variables that affect shopping behaviour was assessed by using the Thurstone Case V Scaling for data analysis (Figure 1) (Turner et al., 2024). Thurstone scaling utilises pairwise comparisons wherein respondents rated factors by preference; the results were analysed using the formula below:
Figure 1. Spider Graph Representing Final Thurstone Case V Scaling Values.
Source: Primary data.
where Pij is the preference scale of respondents for factor i in comparison with factor j, while Si and Sj denote the factor scale for each factor. Furthermore, Pearson’s correlation analysis was performed to analyse the relationships among the six independent variables. The correlation coefficient was calculated (Cleophas et al., 2018):
In addition, multiple regression analysis was used to find out how much the independent variables affected consumer shopping behaviour (Sarstedt & Mooi, 2019).
where Y is consumer shopping behaviour, X1, X2, ..., Xn are independent variables (e.g., accessibility, tourism, seasonal promotions); β0 is the intercept, β1, β2, ..., βn are coefficients quantifying each variable’s effect, and ε is the error term (Sarstedt & Mooi, 2019).
Results and Discussion
Results
The Thurstone Case V Scaling Technique
Thurstone Case V scaling, an established means of analysing ordinal data by pairwise comparison, was utilised in the current study to score six factors affecting consumer preferences in organized retail. The variables measured included: (A) Proximity to Local Markets, (B) Availability of Local and Traditional Products, (C) Tourist Influence, (D) Attitude and Friendliness of Staff, (E) Culture Relevance and (F) Seasonal Offers. The method begins with raw paired preference data in Table 1 which is then converted to decimal proportions in Table 2 and further on in z-cores using standard normal table appearing in Table 3, the z-scores are summed up in Table 4 and normalized in Table 5 to produce final value ranked items.
Table 1. Initial Values of the Thurstone Case Scaling.
Source: Primary data and self-computed.
Table 2. Decimal Conversion of Initial Data.
Table 3. Values Derived from the Thurstone Case V Table.
Source: Thurstone Case V table.
Table 4. Added Values of Thurstone Case V Scaling.
Source: Primary data and self-computed.
Table 5. Final Value Conversion.
Source: Primary data and self-computed.
Correlation Analysis
The correlation matrix shown in Table 6 presents the detailed interrelation among the six variables studied. Therefore, the high correlation values above indicate that Proximity to Local Markets, Availability of Local and Traditional Products and Influence of Tourism are the key drivers of the customers’ buying behaviour in Goa’s retailing landscape.
Table 6. Correlation Matrix of Variables.
Source: Correlation analysis computed through SPSS (Version 29.0) based on primary data.
Notes: Significance levels: p < .01: Highly significant (indicated with **).
N (sample size for all correlations): 300.
Regression Analysis
A multiple linear model regression analysis was conducted in order to check the impact of several independent variables on the dependent variable-consumer shopping behaviour. The dependent variable list related to those independent variables was Proximity to Local Markets, Availability of Local and Traditional Products, Influence of Tourism, Staff Attitude and Friendliness, Cultural Relevance of Products and Seasonal Sales and Promotions. Every independent variable has been tested as to how much contribution it gives toward the dependent variable, and then all together tested for their overall significance.
The model summary in Table 7 shows a high correlation between the independent variables and consumer shopping behaviour, with an r value of 0.789 and R2 of 0.622, indicating that 62.2% of the variance is accounted for by the model. The adjusted R2 of 0.605 verifies a good fit of the model after adjusting for the number of predictors, and the standard error of 0.421 indicates the average deviation from the regression line.
Table 7. Summary of the Model R for Regression Analysis.
Source: Model summary computed through SPSS (Version 29.0) based on primary data.
The ANOVA test in Table 8 indicates a highly significant F-statistic (F = 12.641, p = .000) that demonstrates the overall significance of the model. Regression sum of squares (14.327) and residual sum of squares (8.500) indicate that the model accounts for a large percentage of the total variance (22.827), which justifies the predictive relevance of the predictors.
Table 8. ANOVA Results for the Regression Model.
Source: ANOVA analysis computed through SPSS (Version 29.0) based on primary data.
Table 9 for coefficients shows a better view of the amount of contribution each independent variable can make to the regression model. The constant is 1.256 and represents the value that would be anticipated for consumer shopping behaviour if all other variables were at zero. The unstandardised coefficients, B, indicate the change in the dependent variable, consumer shopping behaviour, due to an increase of a unit of each of the independent variables. Of the significant predictors, Proximity to Local Markets B = 0.212, p = .003 exerts a positive influence, suggesting that consumers care about how close they are to local markets. Availability of Local and Traditional Products B = 0.184, p = .015 is of high significance, which confirms that consumers have value on locally sourced products. This makes tourism the strongest influence with B = 0.276, p = .000, to show that, on the whole, this is what is first considered when coming to make decisions over purchases. Staff attitude and friendliness were the second most powerful determinant of behaviour, at B = 0.195, p = .013, and the Cultural Relevance of Products proved to be the weakest influence at B = 0.129, p = .074, and sat just above the 0.05 significance level. Finally, Seasonal Sales and Promotions positively influence consumer behaviour at B = 0.167, p = .010.
Table 9. Coefficients of the Regression Model.
Source: Regression coefficients computed through SPSS (Version 29.0) based on primary data.
N (sample size): 300
Significance Levels:
p < .05: Significant (indicated with respective p values).
p < .01: Highly significant (indicated in the ANOVA table).
Discussion
The present study aimed to analyse factors that have been influencing consumer shopping behaviour by utilising Thurstone Case V Scaling, correlation analysis and regression analysis, while keeping six independent key variables: proximity to local markets, availability of local and traditional products, tourist influences, staff attitude and friendliness, cultural relevance of products and seasonal sales and promotions.
By the use of the Thurstone scaling technique, the relative importance of the influencing factors varying in purchase decision-making was found. The Influence of Tourism seems to be the most preferred factor of the respondents, so the consumers are significantly influenced by this tourism factor while purchasing the products. Following the Thurstone scaling, the correlation matrix was used to study the inter-relationships among the independent variables. All the variables showed strong correlations with each other. The two most strongly correlated factors were Availability of Local and Traditional Products (r = 0.520, p < .01), while the Staff Attitude and Friendliness (r = 0.520, p < .01). However, Seasonal Sales and Promotions showed the weakest relationship with Proximity to Local Markets at a mere r = 0.230, p < .01, which indicates that though both are associated with each other, their effect on the shopping behaviour is less pronounced compared to the effects of other variables. Regression analysis was carried out to test hypotheses H1-H6 with respect to independent variables and their impact on consumer shopping behaviour. Thus, in the process, many independent factors showed significant influences on the dependent variable. Proximity to Local Markets (B = 0.212, p = .003) confirmed H1, while Availability of Local and Traditional Products (B = 0.184, p = .015) validated H2. Influence of Tourism (B = 0.276, p = .000) had the largest impact, supporting H3. Effects of Staff Attitude and Friendliness (B = 0.195, p = .013) confirmed H4. Cultural Relevance of Products (p = .074) did not meet the significance threshold, leading to the rejection of H5. Seasonal Sales and Promotions (B = 0.167, p = .010) were significant, validating H6. Findings, therefore, indicate that the focus should be put on tourism-based strategies with greater numbers of local products and enhanced employee engagement to enhance the experience of consumers for retailers. The study enriches existing literature as it shows how Proximity to Local Markets significantly affects consumer choices, supporting more recent research on the impact of location convenience (van der Lee et al., 2020). Last but not least, it stresses the role of Staff Attitude and Friendliness as a key determinant in reaching consumer satisfaction through a connection to current modern work on the impact of service quality on customer loyalty (Jin et al., 2017).
Policy Implications & Conclusion
The research provides some managerial insights for retailers in local markets. Focusing on market proximity can promote convenience-based marketing campaigns. Prioritising old-style product availability can enhance brand differentiation and consumer attraction. Involving investments in employee training to enhance service quality can promote customer satisfaction and loyalty. Cooperating with tourism organisations and coordinating seasonal sales can increase tourist demand and revenue. Coordinating retail strategies with these elements ensures correspondence with consumer requirements, promoting growth and retention.
Conclusions
This research study highlights the multi-faceted nature of consumer shopping behaviour in local markets, indicating strong relationships between different influencing factors and consumer preferences. A very strong relationship was established between Proximity to Local Markets, Availability of Local and Traditional Products and the Influence of Tourism as major factors in shaping shopping behaviour, but the influence of tourism is the most powerful predictor. Staff Attitude and Friendliness, and Seasonal Sales and Promotions are two of the most influential factors that determine consumer satisfaction and choice, etc. These results need to be blended with both the internal retail factors and with influences of the external market. The local retailers must adopt these in their strategies so that they can attract consumers more effectively, utilise marketing efforts efficiently, and progress in customer loyalty.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.
Funding
The authors received no financial support for the research, authorship and/or publication of this article.
Abu-AlSondos, I., Alkhwaldi, A., Salhab, H., Shehadeh, M., & Ali, B. (2023). Customer attitudes towards online shopping: A systematic review of the influencing factors. International Journal of Data and Network Science, 7(1), 513–524.
Baker, J., Parasuraman, A., Grewal, D., & Voss, G. B. (2002). The influence of multiple store environment cues on perceived merchandise value and patronage intentions. Journal of Marketing, 66(2), 120–141.
Behera, R. K., Bala, P. K., Tata, S. V., & Rana, N. P. (2023). Retail atmospherics effect on store performance and personalised shopper behaviour: A cognitive computing approach. International Journal of Emerging Markets, 18(8), 1948–1977.
Bourg, L., Chatzidimitris, T., Chatzigiannakis, I., Gavalas, D., Giannakopoulou, K., Kasapakis, V., Konstantopoulos, C., Kypriadis, D., Pantziou, G., & Zaroliagis, C. (2023). Enhancing shopping experiences in smart retailing. Journal of Ambient Intelligence and Humanized Computing, 15(4), 1–19.
Chang, Y.-W., Hsu, P.-Y., Chen, J., Shiau, W.-L., & Xu, N. (2023). Utilitarian and/or hedonic shopping–consumer motivation to purchase in smart stores. Industrial Management & Data Systems, 123(3), 821–842.
Chetioui, Y., Lebdaoui, H., & Chetioui, H. (2021). Factors influencing consumer attitudes toward online shopping: The mediating effect of trust. EuroMed Journal of Business, 16(4), 544–563.
Cleophas, T. J., Zwinderman, A. H., Cleophas, T. J., & Zwinderman, A. H. (2018). Bayesian Pearson correlation analysis. Modern Bayesian Statistics in Clinical Research, 111–118. https://doi.org/10.1007/978-3-319-92747-3_11
Dasoomi, M., Naderan, A., & Allahviranloo, T. (2023). Predicting the choice of online or offline shopping trips using a deep neural network model and time series data: A case study of Tehran, Iran. Sustainability, 15(20), 14764.
Diaz-Gutierrez, J. M., Mohammadi-Mavi, H., & Ranjbari, A. (2023). COVID-19 impacts on online and in-store shopping behaviors: Why they happened and whether they will last post pandemic. Transportation Research Record. https://doi.org/10.1177/03611981231155169
Feldmann, C., & Hamm, U. (2015). Consumers’ perceptions and preferences for local food: A review. Food Quality and Preference, 40, 152–164.
Fisman, R., Paravisini, D., & Vig, V. (2017). Cultural proximity and loan outcomes. American Economic Review, 107(2), 457–492.
Frasquet, M., Gil, I., & Molla, A. (2001). Shopping-centre selection modelling: A segmentation approach. The International Review of Retail, Distribution and Consumer Research, 11(1), 23–38.
Fu, H., Manogaran, G., Wu, K., Cao, M., Jiang, S., & Yang, A. (2020). Intelligent decision-making of online shopping behavior based on internet of things. International Journal of Information Management, 50, 515–525.
Ghosh, P., Tripathi, V., & Kumar, A. (2010). Customer expectations of store attributes: A study of organized retail outlets in India. Journal of Retail & Leisure Property, 9, 75–87.
Grönroos, C., & Ravald, A. (2011). Service as business logic: Implications for value creation and marketing. Journal of Service Management, 22(1), 5–22.
Guo, B., & Wang, D. (2024). Will online shopping lead to more brand loyalty than offline shopping? The role of uncertainty avoidance. Journal of Marketing Research, 61(1), 92–109.
Ho, C.-T., & Wei, C.-L. (2016). Effects of outsourced service providers’ experiences on perceived service quality: A signaling theory framework. Industrial Management & Data Systems, 116(8), 1656–1677.
Hossan, D., Dato’Mansor, Z., & Jaharuddin, N. S. (2023). Research population and sampling in quantitative study. International Journal of Business and Technopreneurship (IJBT), 13(3), 209–222.
Jin, H., Moscardo, G., & Murphy, L. (2017). Making sense of tourist shopping research: A critical review. Tourism Management, 62, 120–134.
Kakaria, S., Saffari, F., Ramsøy, T. Z., & Bigné, E. (2023). Cognitive load during planned and unplanned virtual shopping: Evidence from a neurophysiological perspective. International Journal of Information Management, 72, 102667.
Kar, P. S. (2023). Luxury home market and pre-sales challenges: Aldeola-de-Siolim, Goa. Emerald Emerging Markets Case Studies, 13(1), 1–25.
Ketanbhai, M. D. (2020). Exploring the factors influencing online shopping convenience in the Indian e-tailing sector. International Journal of Management, 11(10), 1062–1076.
Khoa, B. T., Hung, B. P., & Hejsalem-Brahmi, M. (2023). Qualitative research in social sciences: Data collection, data analysis and report writing. International Journal of Public Sector Performance Management, 12(1–2), 187–209.
Kushwaha, T., Ubeja, S., & Chatterjee, A. S. (2017). Factors influencing selection of shopping malls: An exploratory study of consumer perception. Vision, 21(3), 274–283.
Ligaraba, N., Nyagadza, B., Dӧrfling, D., & Zulu, Q. M. (2023). Factors influencing re-usage intention of online and mobile grocery shopping amongst young adults in South Africa. Arab Gulf Journal of Scientific Research, 41(3), 389–415.
McDowell, W. C., Wilson, R. C., & Kile Jr, C. O. (2016). An examination of retail website design and conversion rate. Journal of Business Research, 69(11), 4837–4842.
Méndez-Vogel, G., Marianov, V., Lüer-Villagra, A., & Eiselt, H. A. (2023). Store location with multipurpose shopping trips and a new random utility customers’ choice model. European Journal of Operational Research, 305(2), 708–721.
Mohamad, M., Ali, A. M., & Ab Ghani, N. I. (2011). A structural model of destination image, tourists’ satisfaction and destination loyalty. International Journal of Business and Management Studies, 3(2), 167–177.
Pardeshi, V. H., & Khanna, V. (2021). Factors influencing online apparel shopping orientation among women in Mumbai. Journal of Global Entrepreneurship Research, 11(1), 163–174.
Pei, X.-L., Guo, J.-N., Wu, T.-J., Zhou, W.-X., & Yeh, S.-P. (2020). Does the effect of customer experience on customer satisfaction create a sustainable competitive advantage? A comparative study of different shopping situations. Sustainability, 12(18), 7436.
Rani, A., Chaudhary, N., & Gusai, O. P. (2023). Impact of social media influencers’ credibility and similarity on Instagram consumers’ purchase intention. A Journal of Management, 1, 20.
Rehman, A., Behera, R. K., Islam, M. S., Elahi, Y. A., Abbasi, F. A., & Imtiaz, A. (2024). Drivers of metaverse adoption for enhancing marketing capabilities of retail SMEs. Technology in Society, 79, 102704.
Sari, N. W. (2023). Consumer preference for product categories sold online and offline. Inspirasi & Strategi (INSPIRAT): Jurnal Kebijakan Publik & Bisnis, 13(2), 96–109.
Sarstedt, M., & Mooi, E. (2019). Regression analysis. In A concise guide to market research: The process, data, and methods using IBM SPSS statistics (pp. 209–256) Springer Nature.
Selvarajan, S. K., & Chandran, V. G. R. (2024). Financial inclusion trajectories: Geographical dispersion, convergence, and development implications. The European Journal of Development Research, 36(4), 1–28. https://doi.org/10.1057/s41287-023-00616-7
Seock, Y.-K. (2009). Influence of retail store environmental cues on consumer patronage behavior across different retail store formats: An empirical analysis of US Hispanic consumers. Journal of Retailing and Consumer Services, 16(5), 329–339.
Sham, R., Chong, H. X., Aw, E. C.-X., Thangal, T. B. T., & binti Abdamia, N. (2023). Switching up the delivery game: Understanding switching intention to retail drone delivery services. Journal of Retailing and Consumer Services, 75, 103478.
Shi, L., Wurm, M., Huang, X., Zhong, T., & Taubenböck, H. (2020). Measuring the spatial hierarchical urban system in China in reference to the Central Place Theory. Habitat International, 105, 102264.
Srivastava, A., & Thaichon, P. (2023). What motivates consumers to be in line with online shopping? A systematic literature review and discussion of future research perspectives. Asia Pacific Journal of Marketing and Logistics, 35(3), 687–725.
Turner, M. M., Wade, R. B., Heo, R., Ye, Q., & Jang, Y. (2024). Development and validation of the commitment to social activism scale using the Thurstone scaling procedure. Communication Research Reports, 41(3), 178–192.
van der Lee, J., Oosting, S., Klerkx, L., Opinya, F., & Bebe, B. O. (2020). Effects of proximity to markets on dairy farming intensity and market participation in Kenya and Ethiopia. Agricultural Systems, 184, 102891.
Wang, S., & Chen, J. S. (2015). The influence of place identity on perceived tourism impacts. Annals of Tourism Research, 52, 16–28.
Wang, X., & Zhang, J. (2020). Factors influencing Chinese online shopping distributions of fresh agricultural products. Journal of Distribution Science, 18(10), 53–64.
Wu, Z., Aw, E. C.-X., & Chuah, S. H.-W. (2023). Webrooming as the new retail experience: A smart-shopping perspective. International Journal of Retail & Distribution Management, 51(6), 736–754.
Zhang, K. Z. K., & Benyoucef, M. (2016). Consumer behavior in social commerce: A literature review. Decision Support Systems, 86, 95–108.
Zheng, Q., Chen, J., Zhang, R., & Wang, H. H. (2020). What factors affect Chinese consumers’ online grocery shopping? Product attributes, e-vendor characteristics and consumer perceptions. China Agricultural Economic Review, 12(2), 193–213.
Zimmermann, R., Mora, D., Cirqueira, D., Helfert, M., Bezbradica, M., Werth, D., Weitzl, W. J., Riedl, R., & Auinger, A. (2023). Enhancing brick-and-mortar store shopping experience with an augmented reality shopping assistant application using personalized recommendations and explainable artificial intelligence. Journal of Research in Interactive Marketing, 17(2), 273–298.
Zulqarnain, H., Zafar, A. U., & Shahzad, M. (2015). Factors that affect the choice of consumers in selecting retail store for grocery shopping. International Journal of Multidisciplinary and Current Research, 3(1), 1167–1172.
