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Colombo Journal of Advanced Research Vol. 1, No. 2; 2019

Factors influencing Consumer decision making when selecting Modern Retail Grocery stores in Sri Lanka

Mahen B. Makalande, University of West London, e-mail; [email protected] Marcelline Alexander, ANC Sri Lanka, Overseas Academic Partner – University of West London

Abstract

Globalization is reshaping the retail landscape in unimaginable ways. Consumers are becoming more and more complicated to understand with the plethora of factors that influence their decisions. This study focuses on the factors influencing consumer decision making when selecting a Modern Retail Grocery Store for shopping, concentrating on the urban areas of the city of Colombo, Sri Lanka. A conceptual framework was developed in line with theoretical evidence and empirical studies to develop five hypotheses that tested if a relationship existed between various store attributes (independent variables) and the consumer decision of Supermarket selection (dependent variable). Findings have proven all five hypotheses to be true, however, it was discovered that two store attributes do not have a significant impact on consumer decision making. These findings which are relevant to the current context of Sri Lanka, could be used by Supermarket management to understand and prioritize areas which require the organizational resource support and utilize the same to maintain or improve competitive advantage and maximize profit margins.

Introduction

In this day and age, understanding the consumer is a complex affair as globalization brings people together through technologies, the world has become more connected than ever (Wilen, 2018), making it harder for individuals to make decisions due to the multiple influences that come their way. A Global consumer insight survey done by PricewaterhouseCoopers (2018) shows that consumers rank Social Media as the number one source of inspiration to purchase a product while 88 percent of respondents were willing to pay more for faster delivery. Further, consumers are ambivalent about new technologies and value great experiences (PricewaterhouseCoopers, 2018). In line with these expectations, AmazonGo was launched in 2016 which focuses on zero lines and no checkout, elevating consumer experience in retail stores through the use of mobile applications (Amazon, 2019)

This study focuses on the factors that influence consumer decision making in selecting a Modern Retail Grocery store. The context of this research would be in a Sri Lankan setting, based in Colombo with key information published/gathered over 2016 to 2019. Amidst political instability and economic turmoil such as depreciating currency; the rupee depreciated by over 20 percent against the US dollar during the 3rd and 4th quarter in 2018.It is important to note that traditional retailers or small retail businesses, rely on unpaid labour

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from family and friends, pay no rent because they own their storefronts, and don’t pay corporate taxes (Child, et al., 2015)

Increasing per capita income and rising urbanization is creating a change in customer buying patterns with strong growth potential for Modern retail grocery stores in Sri Lanka (Fitch Ratings, 2017, p. 1). These stores are more commonly known as, ‘Supermarkets’. Three key players namely, Keells Super, Arpico and Cargills account for 80 percent of market share with over 460 branches across the country with heavy concentration in Colombo which is located in the Western province. (Fitch Ratings, 2017, p. 3). Margins for this business are thin and float around 4- 5 percent (LMD, 2017).

There is a prevalence of a gap in knowledge about the key influencing factors which shape consumer behaviour towards the decision of selecting a Modern Retail Grocery store or Supermarket over a Traditional retail grocery store (Fitch Ratings, 2017) for shopping in Sri Lanka. Further, studies done in this area are for overseas contexts and the ones done in Sri Lanka have not considered the current economic and political context in Sri Lanka as at 2019.

The urban area of Colombo has been selected for this study which consists of a population of 2.3 million (World Population Review, 2019) which is 11 percent of the total population of the country. The average land prices have increased by 50 percent from 2009 to 2017, while the price of commercial buildings has increased by 94 percent during the same period to Rs. 204.7 Million (LMD, 2017, p. 128). forcing organizations to curtail overheads and increase efficiencies (LankaPropertyWeb, 2018). Inflation rates have increased by 57 percent on average for key construction material over the period from 2009 to 2017 making investments, renting out property a costly affair (LMD, 2017, p. 122).

This study aims to identify and explore the key influencing factors which affect consumer decision making when selecting a supermarket in Sri Lanka for grocery shopping. The relationship and impact of each store attribute will be identified. The focus being to expand the thin bottom line of 4 percent-5 percent currently seen in organizations in the supermarket industry (Fitch Ratings, 2017) by providing more of what the consumer needs and less of what they don’t, resulting in higher retention rates and the ability to increase customer lifetime value. This in turn assists in improving long term profitability of the organization (Wang, et al., 2014, p. 70).

Literature Review

Consumers are moving away from traditional markets and opting to select modern retail grocery stores to fulfil their shopping requirements. This trend is seen visibly in the urban areas driven by the people in the upper middle-class tier. Not only do they look for quality products at competitive prices, but also seek convenience in the shopping process (Leonnard, 2017). The Sri Lankan climate for Modern Grocery Retail stores looks positive with strong growth momentum forecasted which is driven by aggressive expansion, rising income levels and rapid urbanization. Industry revenue has grown by 7.5 percent between 2013 and 2017 (Fitch Ratings, 2017). Positive customer perception means that customers

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are willing to shop at Modern grocery stores as opposed to five years ago. Competitive pricing is offered by Supermarkets leveraging scale and ability to negotiate favourable prices with suppliers. Modern retail grocery stores accounted for 12 - 15 percent of FMCG sales in Sri Lanka in 2016 according to a study done by consumer research company Neilson (2017).

Multiple Theoretical frameworks which are built to understand consumer behaviour and the consumer decision making process will be critically evaluated to identify factors which influence consumers to make decisions about where to shop. Further, empirical evidence would be considered to understand current trends, and develop hypothesis.

1) Black Box Model: Examining the buyers mind and understanding how consumers

choose one product or brand over another is key (Diasz, 2017, p. 525). The buyer’s characteristics influence how he/she perceives the stimuli; the decision-making process determines what buying behaviour is undertaken. This model considers how various stimuli such as marketing stimuli and environmental stimuli contribute to influencing buyer response by affecting consumer decision making process (Jisana, 2014).

2) Five-step Consumer Decision Making Process: Understanding the key areas the

organization should focus on, when inducing the customer to select their Supermarket over competition.

3) Involvement theory states that there exist purchases which require high involvement of

the consumers due to high perceived risk associated with that decision, and on the other and there is little or low involvement related to purchased which the consumers perceive as having low risk. (Diasz, 2017, p. 526).

4) Conceptual Framework in sensory marketing: The power of sensory marketing in

influencing consumer decision making is significant (Minh & Thi Truc Ly, 2017) A consumer’s emotional and perceptual aspects could be influenced through sensory marketing which includes; visual, auditory, olfactory, gustatory and tactile marketing (Krishna, et al., 2016). Studies have proven that the contemporary consumer is more emotional than rational (Minh & Thi Truc Ly, 2017). This is one of the main differentiators between Modern Retail from Traditional retail.

5) The Tri-Component model of consumer attitude: This model considers three aspects.

Firstly, the cognitive component (Think) which consists of knowledge, values, awareness etc. Secondly, the affective component (Feel) or emotional states may contribute to positive or negative experiences and recollections of the same may influence how an individual may react. Thirdly, the conative component (Do) refers to the action that relate to the decisions which enable consumers to decide to purchase (Diasz, 2017, p. 526).

6) The FCB Grid: (Vaughn 1980) categorizes decision making into four quadrants based

on the level of involvement and if the decision involves rational thinking or merely

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emotional (Cheong, 2017) providing a basis to decide which type of advertising planning strategies should be used for different types of consumers so that the level of influence will be significant. Four types of advertising strategies can be considered namely; Informative, Affective, Habitual and Self-satisfied can be considered (Diasz, 2017, p. 530).

Supermarket selection - Multiple factors influence consumers when it comes to the decision of selecting a modern retail grocery store (Supermarket). Four key aspects were highlighted by researchers. First, consumers usually shop at Supermarkets which are in close proximity to where they live (Sunanto, 2012). Second, consumers would revisit stores that provide assurance or credibility that the same service levels will be upheld (Leonnard, 2017). Third, consumers enjoy shopping at Supermarkets which hold a variety of products (Asuquo & Igbongidi, 2015). Finally, it was evident that consumers recommend Supermarkets which offer special prices on loyalty cards, to their friends and family (Child, et al., 2015).

Store Convenience - The one stop shopping concept acts as a competitive advantage for modern retailers to attract consumers to switch their buying preferences from traditional markets to Supermarkets (Sunanto, 2012) Customers are gradually prioritizing convenience and quality over price (LMD, 2017).Location is one of the factors considered vital by retail stores as it is crucial in the success of operations as this involves high investment (Berman & Evans, 2005, p. 61). Availability of parking is also a concern among urban supermarket shoppers (). Proximity is an important aspect in consumer consideration as further the location, higher the amount of time and money spent (Sunanto, 2012). Population density, income levels of the region/locations selected would assist in strategy deployment and reasonably forecasting market potential (Berman & Evans, 2005, p. 215).

Service Quality - Improving service quality would help in achieving long term profitability and business stability (Leonnard, 2017). One of the factors influencing consumer decision making has been identified as Social factors (Berman & Evans, 2005). Shoppers would evaluate the store personnel such as cashiers, checkers, stock boys etc. based on if they were friendly or indifferent, cooperative or brusque (Martineau, 1958, p. 107, cited in Willems, 2012). Trustworthiness is considered one of the most influential indicators of service quality (Leonnard, 2017). Gilbert (2006) identified five dimensions which could be used to measure service quality while, these five dimensions have been used in the study of service quality by (Qin & Prybutok, 2009) as well. Quality of service is a key criteria in selecting supermarkets (Diasz, 2017, p. 276). Positive experiences could be utilized as a differentiation strategy in a highly competitive retail business. Creating pleasant experiences for consumers while they are actively engaging with the store at every touch point in the retail setting is key as retailers could leverage these happy customers to feel emotionally attached and bonded with the retail store (Muhammad, et al., 2014).

Store Atmosphere - Store atmosphere is defined by Kotler (1973, p. 50) as “buying environments [designed] to produce specific emotional effects in the buyer that enhance his purchase probability”, while Carpenter & Moore (2006) agree that store attributes affect consumer decision making. Three main factors affecting consumer behaviour through store atmosphere could be identified. Firstly, Ambient factors or environmental Cues such as

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music (Krishna, et al., 2016, p. 145) and scent which Gilbert (2006) classify as Aural and Olfactory respectively influence the emotional state of the consumer, resulting in a change in behaviour which may or may not be positive. (Bohl, 2012). Secondly, design factors which can be split into Aesthetic aspects such as architecture (Martineau, 1958, p. 105, cited in Willems, 2012), colours used etc (Haryono & Sihombing, 2018, p. 42) and Functional aspects such as Layout and Signage (Baker, 1987, p. 80, cited in Haryono & Sihombing, 2018). Thirdly, Social factors such as the number of other customers (K. A Machleit, et al., 1994). Store Atmosphere is strategically used to affect sensory elements of the shopper to induce positive purchase behaviour which is complemented by enhanced customer experience (Krishna, et al., 2016).

Store Merchandise - Store merchandise plays a vital role in consumer decision making when selecting a supermarket to shop at (Reddy, et al., 2011) It is key for Modern Retail Grocery stores to have a wide variety of products in order to gain competitive advantage (Khraim, et al., 2011). Competitive advantage can be created when the store meets consumer requirements such as a wide range of high-quality products from well-known brands (Haryono & Sihombing, 2018). Asuquo & Igbongidi, (2015) also argue that merchandise assortment is a key influencing factor in consumer decision making. There is also a tendency for consumers to choose Supermarkets which have the freshest vegetables. This means that the Supermarket should obtain their fruits and vegetables directly from the farmers (Bond, et al., 2009).

Store Pricing - Shoppers have different psychological outlooks and each market segment looks for a different emphasis. The lower status shopper is frugal and is not concerned very much about the image of store, but the competitive pricing it offers. Conversely, upper-status shoppers are concerned if their lifestyle is reflected in what is purchased, store image and most likely would be less price conscious (Martineau, 1958, p. 103, cited in Willems, 2012). Loss leader pricing has also been a strategy used by grocery retail stores and focus on temporarily pricing products low or below retail cost to stimulate consumer behaviour towards them (Walters & MacKenzie, 1988, cited in Von del Poel, 2003). Value for money is another strategy Modern grocery stores are striving to offer while offering better or differentiated products/services than competitors and complementing the same with more efficient cost structures (Dennis, 2016). An interesting insight to it all according to Diasz (2017, p. 276) is that one of the key criteria in selecting a supermarket in Sri Lanka is the price of vegetables.

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Consumer Decision Making on Store

Selection

Store ConvenienceLMD (2017)

Service Quality Leonnard(2017)

Store Atmosphere Muhammad, et al. (2014)

Store MerchandiseMuhammad, et al. (2014)

Store PricingLeal (2014)

H1

H2

H3

H4

H5

H = Hypothesis

Dependent Variable

Independent Variables

Figure 1: Conceptual Framework

Hypotheses

H1; There is a relationship between Store Convenience and the decision to select a Supermarket.

H2; There is a relationship between Service Quality and the decision to select a Supermarket.

H3; There is a relationship between Store Atmosphere and the decision to select a Supermarket.

H4; There is a relationship between Store Merchandise and the decision to select a Supermarket.

H5; There is a relationship between Store Pricing and the decision to select a Supermarket.

Methodology

A Positivist research paradigm would be followed with a deductive approach as the focus is to use data to test theory (Sauders, et al., 2016, p. 166) while the research design was cross-sectional as data will be collected at one particular time only and will follow a quantitative approach to analysing data while the research instrument considered for this endeavour is a self-completed questionnaire.

The total Sri Lankan population which stands at 21 million (World Population Review, 2018), while Colombo district which has the highest concentration of Supermarkets or Modern Retail Grocery stores (LMD, 2017) and population selected is 18 years & above which is in line with University Ethics guidelines and who are living and/or working in the Colombo city. The Colombo Metropolitan Region (CMR) accounts for 45 percent of GDP of Sri Lanka and is home to 28 percent of total population (LMD, 2017, p. 128). while the

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urban area of the city accounts for a population 2.3 million people (World Population Review, 2019) which is 40 percent of CMR. Based on the household mean expenditure per month, Colombo district ranks the highest out of 25 districts with an average spend of Rs. 90,670 while the average for Sri Lanka is Rs. 54,999 (Department of Census and Statistics, Sri Lanka, 2016). the research instrument uses the Online Survey (Formerly BOS) software compliant with UK Laws. This Questionnaire is a self-completed questionnaire which is cheaper and quicker to administer and provides convenience to respondents in terms of discretion (Bryman & Bell, 2015). The survey was launched on the 23rd of February 2019 and concluded a month later on the 23rd of March 2019 while 389 Respondents participated in the survey which satisfied and exceeded the minimum number of respondents required for this study which was 385 responses (Whitley and Ball, 2002).

Snowball sampling method which is a non-probability sampling technique (Hair, et al., 2009) was selected by the researcher for this study. The main reason being the acceptance of by the Human Resource manages of four offices in Colombo to share this questionnaire with their employees who in fact meet the selection criteria considering the population of urban areas in Colombo. Furthermore, this method is relatively less time consuming and costly, while is provides convenient access to the potential respondents of the survey.

Sample Profile

Section A (Table 1) of the questionnaire attempts to ascertain information on demographics, while Section B (Table 2) below has asked four questions under the dependent variable (consumer decision on store selection) and twenty-three questions under five store attributes (Independent variables) which influence the consumer decision making with regard to store selection would be supported by a 5-point Likert scale ranging from one (strongly disagree =1) to five (strongly agree=5) would be used to rate each opinion.

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Table 1: Sample Profile Source: Author generated through SPPS Survey data

The instrument used for data collection consists of modified questions from various published studies as referenced. The variables were measured with the use of questions from different published research articles modified to this study’s context (Table 2).

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Table 2: Questionnaire Source: Author generated through SPPS Survey

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Reliability and Validity

Reliability which refers to replication and consistency (Sauders, et al., 2016, p. 202) has been carried out for the pilot test as well as the main study using the Cronbach’s Alpha test. Table 3 provides evidence that both for the pilot test and the main study, the values are above 0.7 which implies an acceptable level of internal reliability or the fact that the aggregated responses which form an overall score does not lack coherence (Bryman & Bell, 2015, p. 168).

Table 3: Reliability of Main Study Source: Author, generated using survey data through SPSS Validity - The accuracy of analysis, generalisability and the appropriateness of measures used can be used to define validity (Bryman & Bell, 2015) . Kaiser-Meyer-Olkin (KMO) and Bartlett’s tests were conducted so that the adapted measurement scales used from existing research was valid and usable to assess constructs used in the study (Duffet, 2015). KMO test values range between 0 and 1 (Field, 2005) and values above 0.5 are acceptable (Kaiser, 1974). Bartlett’s test should have a significance value less than 0.05 if relationships between variables are to be established, testing null hypotheses (Field, 2005).

Table 5: Validity analysis for pilot study Source: Author generated through SPSS Survey Data

Significant

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Depicting significance, all variables were found to have KMO values above 0.5 and Bartlett’s test values less than 0.05, partially ensuring convergent validity All indicators of dependent variable consumer decision on store selection, underwent factor loading tests and all factor explained a total variance of above 56 percent. All the variables were tested, and the extracted variances were calculated which distinguished each factor from the other factors. Meeting factor loading requirements, convergent validity was established.

Research Findings & Analysis

An Exploratory data analysis was conducted, with descriptive tests, normality test, correlation analysis to test hypotheses developed and regression analysis. Pearson’s correlation coefficient was utilized to measure bivariate correlations, the strength of relationships between two or more variables as per the hypotheses was measured through correlation analysis. The coefficient should range from 0-1 where 0 will indicate zero or no relationship (closer to 0 would be a weak relationship) while 1 will indicate a perfect relationship (closer to 1 would be a strong relationship). These relationships could be explained as positive or negative which indicates the direction of the relationship. A p value of less than 0.05 indicates a significance in statistics while the correlation test run was significant at 0.001 level. Cohen (1988) states that if the correlation value is more 0.3 then it is a sizeable level of association (Cohen, 1988, cited in Hemphill, 2003). There are moderate, positive correlations between consumer decision on store selection and the independent variables: store convenience (0.316), store atmosphere (0.361) while strong, positive correlations are seen between consumer decision on store selection and the independent variables: service quality (0.626), store merchandise (0.552) and store pricing (0.549). Hence, all hypotheses are accepted.

Table 6: Regression Analysis Source: Author generated through SPSS Survey Data

It is observed that other than store convenience and store atmosphere, the other predictor variables have a significance level of <0.0.5 indicating a significant impact on the dependent variable which is consumer decision on store selection. Further, this is cemented by the fact that the 95% confidence interval (CI) for these variables do not have the value zero falling in between them. As far as store convenience and store atmosphere are concerned, p values of above 0.05 and CIs where the value zero fall in between are present.

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This indicates that these two predictor variables are not significant in terms of impact it has on supermarket selection. The VIF values fall below 5, indicating there is no problem of multicollinearity.

Table 7: Multicollinearity Analysis Source: Author generated through SPSS using Survey Data

Figure 2: R Square Source: Author generated through SPSS using Survey Data

The R- Square value is 0.639 which means 63.9 percent of the variance in Consumer Decision on Store Selection can be explained by the predictors or the independent variables which are the store attributes considered such as Store Convenience, Service Quality, Store Atmosphere, Store Merchandise and Store Pricing. However, it is advised to use the adjusted R Square value of 0.635 for analysis as it has been adjusted for errors. The Durbin-Watson statistic is very close to 2. Therefore, it is possible to conclude that there is a positive relationship between all 5 predictor variables and consumer decision on store selection.

CDStrSel StrCon ServQual StrAtmos StrMerch StrPricn

Pearson Correlation 1 0.316** 0.626** 0.361** 0.552** 0.549**Sig. (2-tailed) 0.000 0.000 0.000 0.000 0.000N 389 389 389 389 389 389Pearson Correlation 0.316** 1 0.368** 0.261** 0.285** 0.271**Sig. (2-tailed) 0.000 0.000 0.000 0.000 0.000N 389 389 389 389 389 389Pearson Correlation 0.626** 0.368** 1 0.425** 0.498** 0.350**Sig. (2-tailed) 0.000 0.000 0.000 0.000 0.000N 389 389 389 389 389 389Pearson Correlation 0.361** 0.261** 0.425** 1 0.445** 0.316**Sig. (2-tailed) 0.000 0.000 0.000 0.000 0.000N 389 389 389 389 389 389Pearson Correlation 0.552** 0.285** 0.498** 0.445** 1 0.358**Sig. (2-tailed) 0.000 0.000 0.000 0.000 0.000N 389 389 389 389 389 389Pearson Correlation 0.549** 0.271** 0.350** 0.316** 0.358** 1Sig. (2-tailed) 0.000 0.000 0.000 0.000 0.000N 389 389 389 389 389 389

Store Merchandise(StrMerch)

Store Pricing(StrPricn)

Correlations

Consumer Decision on Store Selection(CDStrSel)

Store Convenience(StrCon)

Service Quality(ServQual)

Store Atmosphere(StrAtmos)

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Figure3: ANOVA Source: Author generated through SPSS using Survey Data

The p- value from the ANOVA table is than 0.001, therefore it can be concluded that the five predictor variables represent the consumer store selection decision very well This means that at least one independent variable could be used to explain the outcome of the hypotheses and that this model can be used to successfully test the factors influencing consumer decision making when selecting a supermarket for grocery shopping.

Further Analysis

A detailed analysis of each sub factor forming the main variables was carried out in order to develop robust recommendations which can be seen below.

Conclusion & Implications

H1 can be accepted: Store convenience has a positive and statistically significant (p<0.05) relationship with consumer decision on store selection (dependent variable). The strength of this relationship is moderate with a Pearson’s r value of 0.316, However, the regression analysis indicates a positive impact while highlighting the fact that it is not a significant predictor due to the p value larger than 0.05 (B=0.046, p=0.140) and cannot be considered a significant influencing factor to consumer decision on grocery store selection. Ample parking and location are key based on the detailed analysis. However, in the current context other factors which are significant influencers should be given priority and resource allocation.

H2 can be accepted: Service Quality has a positive and statistically significant (p<0.05) relationship with consumer decision on store selection (dependent variable). The strength of this relationship is strong with a Pearson’s r value of 0.625, The regression analysis indicates a positive impact and that service quality is a significant predictor variable (B=0.323, p=0.001). Therefore, Resources should be mobilized to ensure continuous training for staff to uphold consistency of service levels and satisfy consumer expectations of store experience

H3 can be accepted: Store Atmosphere has a positive and statistically significant (p<0.05) relationship with consumer decision on store selection (dependent variable). The strength of this relationship is moderate with a Pearson’s r value of 0.361, However, the regression analysis indicates a slightly negative impact while the p value larger than 0.05 indicates

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that store atmosphere is not a significant predictor when considering consumer decision of store selection. (B= -0.110, p=0.758). Store design through simpler layouts and ambience though suitable scent, music and lighting conditions are important aspects to consider. However, in the current context other factors which are significant influencers should be given priority and resource allocation.

H4 can be accepted: Store Merchandise has a positive and statistically significant (p<0.05) relationship with consumer decision on store selection (dependent variable). The strength of this relationship is strong with a Pearson’s r value of 0.552, The regression analysis indicates a positive impact and that store merchandise is a significant predictor variable (B=0.237, p=0.001). Maintaining and improving on the selection of high-quality products from well-known brands and ensuring the freshest fruits/foods and vegetables are sourced is key and resourced should be diverted to ensure this is done to achieve a differentiated position among supermarkets which would entice more footfall to the store.

H5 can be accepted: Store pricing has a positive and statistically significant (p<0.05) relationship with consumer decision on store selection (dependent variable). The strength of this relationship is strong with a Pearson’s r value of 0.549, The regression analysis indicates a positive impact and that store pricing is a significant predictor variable (B=0.249, p=0.001). Focus should be given for providing a price for goods in the grocery store which reflect value for money. Hence, it is important that effort is made to market the Supermarket as a chain offering value for consumers. Further, offering discounts on products in a systematic manner would draw more footfall to the store. Finally, focusing on offering low prices may reflect lower quality and since a significant percentage of respondents are not keen on buying low priced products it is not advised to price products low as it will also bring margins further down. Value for money is an offering consumer expect in the current context. Therefore, when making decisions on pricing strategies it should be done.

Suggestions for future research

Approaching future research with a Mixed research methodology in data collection and analysis would allow to gather more informative and insightful data as a combination of both quantitative and qualitative data pave the way for a more robust analysis. Also, this study has not gone into detail about store promotions using Loyalty cards which if included into future research may provide further insights into this area of consumer decision making when selecting supermarkets which will add more value to the recommendations provided.

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