Strathmore University SU+ @ Strathmore University Library Electronic Theses and Dissertations 2017 Influence of pricing strategies on consumer purchase decision: a case of supermarkets in Nairobi County Illuminata Mbuya Njeru School of Management and Commerce (SMC) Strathmore University Follow this and additional works at http://su-plus.strathmore.edu/handle/11071/5585 Recommended Citation Njeru, I. M. (2017). Influence of pricing strategies on consumer purchase decision: a case of supermarkets in Nairobi County (Thesis). Strathmore University. Retrieved from http://su- plus.strathmore.edu/handle/11071/5585 This Thesis - Open Access is brought to you for free and open access by DSpace @Strathmore University. It has been accepted for inclusion in Electronic Theses and Dissertations by an authorized administrator of DSpace @Strathmore University. For more information, please contact librarian@strathmore.edu i INFLUENCE OF PRICING STRATEGIES ON CONSUMER PURCHASE DECISION: A CASE OF SUPERMARKETS IN NAIROBI COUNTY ILLUMINATA MBUYA NJERU Submitted in partial fulfillment of the requirements for the Degree of Master of Commerce at Strathmore University June 2017 This thesis is available for library use on the understanding that it is copyright material and that no quotation from the thesis may be published without proper acknowledgement ii DECLARATION I declare that this work has not been previously submitted and approved for the award of degree by this University or any other University. To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made in the thesis itself. © No part of this thesis may be reproduced without the permission of the author and Strathmore University. Illuminata Mbuya Njeru ……………………. ……………………. Approval The thesis of Illuminata Mbuya Njeru was reviewed and approved for Degree award by: Dr. Tabitha Waithaka, Lecturer, School of Management and Commerce, Strathmore University Dr. David Wang’ombe, Dean, School of Management and Commerce, Strathmore University Prof. Ruth Kiraka, Dean, School of Graduate studies, Strathmore University iii ACKNOWLEDGEMENT I would like to thank the Almighty God for the gift of life and good health and for sustaining me to this point in life. Special thanks to my supervisor Dr Tabitha Waithaka for her continuous guidance, patience and constructive criticism. Thank you for your understanding and for agreeing to supervise my work despite your busy schedule. I would also like to thank my husband Denis Mugendi for the unconditional support during my studies. Special thanks to my parents Nicasio Njeru and Midren Njeru for believing in me and standing by me all along. Your encouragement gave me the strength to carry on with the project. iv ABSTRACT This study sought to address the gap in the literature concerning how pricing strategies influence consumer purchase decisions. The first objective of the study was to find out the extent to which Everyday Low Pricing Strategy and High-Low pricing strategy have been adopted in the supermarkets in Nairobi County. The second objective was to establish the extent to which Everyday low pricing strategy influences consumer purchase decisions in Nairobi County. Finally, the third objective was to determine the extent to which High-Low pricing strategies influences consumer purchase decisions in Nairobi County. Data was collected using questionnaires. The target population of this study was customers of four major supermarkets in Nairobi County. Random sampling technique was used to obtain representative sample. The study aimed at getting 315 respondents. Descriptive statistical methods were used to analyze the data. To establish if a relationship exists between pricing strategies and consumer purchase decision regression and correlation analysis was used. Results showed that pricing strategies were significant in explaining product choice, store choice, purchase amount, and purchase timing. The findings generated from the study should provide marketing managers with an understanding of the relationship between pricing strategies and consumer purchase decision in the Kenyan context and give them insights on which pricing strategies they should concentrate on in order to gain competitive advantage. Since this study only looks at two pricing strategies and their influence on consumer purchase decision, future research should examine different types of pricing strategies for example rapid skimming strategy, slow skimming strategy, rapid penetration strategy, slow skimming strategy and their impact on consumer purchase decision and also explore alternative methods of analyzing the data. v TABLE OF CONTENTS CHAPTER ONE ........................................................................................................................... 1 INTRODUCTION......................................................................................................................... 1 1.1 Background of the study ...................................................................................................... 1 1.1.1 Pricing strategies .............................................................................................................. 2 1.1.2 Consumer purchase decision ............................................................................................ 4 1.1.3 Supermarkets Sector in Kenya ......................................................................................... 6 1.2 Problem statement ................................................................................................................ 7 1.2.1 Research Objectives ......................................................................................................... 8 1.2.1.1 Main Objective ............................................................................................................. 8 1.2.1.2 Specific Objectives ....................................................................................................... 8 1.3 Research Questions .............................................................................................................. 9 1.4 Justification of the study ...................................................................................................... 9 1.5 Scope of the study .............................................................................................................. 10 CHAPTER TWO ........................................................................................................................ 11 LITERATURE REVIEW .......................................................................................................... 11 2.1 Introduction ........................................................................................................................ 11 2.2 Theoretical Foundation ...................................................................................................... 11 2.2.1 Adaptation-level theory .................................................................................................. 11 2.2.2 Theory of Reasoned action ............................................................................................. 12 2.2.3 Signaling Theory ............................................................................................................ 13 2.3 Empirical review ................................................................................................................ 15 2.3.1 Pricing strategies ............................................................................................................ 15 2.3.2 High-Low pricing strategy and consumer purchase decision .......................................... 16 vi 2.3.2 Everyday Low Price (EDLP) Strategy and consumer purchase decision ...................... 18 2.4 Research Gap ..................................................................................................................... 20 2.5 Conceptual framework ....................................................................................................... 20 2.5.1 Operationalization .......................................................................................................... 21 CHAPTER THREE .................................................................................................................... 23 RESEARCH METHODOLOGY .............................................................................................. 23 3.1 Introduction ........................................................................................................................ 23 3.2 Research philosophy .......................................................................................................... 23 3.3 Research Design................................................................................................................. 24 3.4 Population of the study ...................................................................................................... 24 3.5 Sampling design ................................................................................................................. 25 3.6 Data Collection .................................................................................................................. 25 3.7 Data analysis ...................................................................................................................... 26 3.7.1 Descriptive statistics ....................................................................................................... 27 3.7.2 Correlation analysis ........................................................................................................ 27 3.7.3 Multiple regression analysis ........................................................................................... 27 3.8 Reliability and Validity ...................................................................................................... 28 3.9 Ethical consideration .......................................................................................................... 29 CHAPTER FOUR ....................................................................................................................... 30 DATA ANALYSIS AND PRESENTATION ............................................................................ 30 4.1 Introduction ........................................................................................................................ 30 4.2 Response Rate .................................................................................................................... 30 4.3 Reliability test .................................................................................................................... 30 4.4 Respondents Demographic ................................................................................................ 31 vii 4.5 Pricing Strategies used by the supermarkets in Nairobi County........................................ 33 4.5.1 Everyday Low Pricing Strategy ....................................................................................... 33 4.5.2 High-Low pricing strategy ............................................................................................... 34 4.6 Consumer purchase decision.............................................................................................. 35 4.6.1 Product Choice ............................................................................................................... 36 4.7 Influence of pricing strategies on consumer purchase decision.............................................. 38 4.7.1 Pearson’s correlation analysis .......................................................................................... 38 4.7.2 Regression Analysis ............................................................................................................. 39 4.7.2.1 Influence of pricing strategies on product choice ......................................................... 39 4.7.2.2 Influence of pricing strategies on store choice .............................................................. 41 4.7.2.3 Influence of pricing strategies on purchase amount ...................................................... 44 4.7.2.4 Influence of pricing strategies on purchase timing ....................................................... 46 4.7.2.5 Influence of pricing strategies on consumer purchase decision overall regression model ................................................................................................................................................... 47 CHAPTER FIVE ........................................................................................................................ 50 DISCUSSIONS, CONCLUSION, AND RECOMMENDATION .......................................... 50 5.1 Introduction ........................................................................................................................ 50 5.2 Discussions ........................................................................................................................ 50 5.2.1 The extent of adoption of pricing strategies by the supermarkets .................................. 50 5.2.2 Everyday Low pricing strategy and consumer purchase decision ................................. 51 5.2.3 High-Low pricing strategy and consumer purchase decision .......................................... 52 5.3 Conclusion ...................................................................................................................... 52 5.4 Recommendations .......................................................................................................... 53 viii 5.5 Limitations of the study and suggestions for future research ......................................... 54 REFERENCES ............................................................................................................................ 56 APPENDICES ............................................................................................................................. 62 APPENDIX I: PARTICIPANT LETTER .................................................................................... 62 APPENDIX II: QUESTIONNAIRE ............................................................................................. 63 APPENDIX III: LIST OF SUPERMARKETS ............................................................................ 70 ix LIST OF TABLES Table 2.1: Operationalization of Study variable ........................................................................... 21 Table 3.1: Summary of data analysis method ............................................................................... 28 Table 4.1: Respondents demographics ......................................................................................... 31 Table 4.2: Respondents demographic II (Size of family, choice of supermarket and employment status) ..................................................................................................................................... 32 Table 4.3: Everyday Low Pricing Strategy ................................................................................... 34 Table 4.4: High-Low Pricing Strategy .......................................................................................... 35 Table 4.5: Product choice ............................................................................................................. 36 Table 4.6: Store choice ................................................................................................................. 37 Table 4.7: Purchase amount .......................................................................................................... 37 Table 4.8: Purchasing timing ........................................................................................................ 38 Table 4.9: Correlation results ........................................................................................................ 39 Table 4.10: Pricing strategies and product choice regression results ........................................... 41 Table 4.11: Pricing strategies and store choice regression results ................................................ 43 Table 4.12: Pricing strategies and purchase amount regression results ........................................ 45 Table 4.13: Pricing strategies and purchase timing regression results ......................................... 47 Table 4.14: Pricing strategies and consumer purchase decision overall regression ..................... 49 x LIST OF FIGURES Figure 2.1: Pricing strategies and consumer purchase decision ................................................... 21 Figure 4.1: Shopping frequency of the study population .............................................................. 33 1 CHAPTER ONE INTRODUCTION 1.1 Background of the study Price is a measure by which customers judge the value of any offer from retailers and they use it to make choices between competing brands (Rosa et al., 2011). Price is also one of the most flexible elements of the marketing mix and can be adapted easily in the changing environmental conditions (Lancioni, 2005). Organizations spend a lot of time and resources figuring out the best pricing strategy for their products because a wrong strategy can cost them important customers and therefore result into loss of revenue. Organizations remaining indifferent or frustrated around pricing strategies stay behind by allowing the competition to set market prices (Dolan & Simon, 1996). This may have a negative influence on the way consumers view them because they have to go by the prices fixed by their competitors otherwise their market share will be affected adversely. As Dolan & Simon (1996) assert, price therefore becomes a competitive element in the goal for market share and an influencing factor on consumer purchase decision. Consumer purchase decision is the study of how individual customers, groups or organizations select, buy, use, and dispose goods and services to satisfy their needs and wants (Peter & Donnelly, 2003). It refers to the actions of the consumers in the marketplace and the underlying motives for those actions. Organizations expect that by understanding what causes the consumers to buy goods and services, they will be able to determine the best price for their products, the price that will make them have a competitive edge over their rivals. There are various factors that influence the consumer purchase decision. They include marketing factors such as product design, price, promotion, packaging, position and distribution and personal factors such as age, gender, education and income level (Tang et al., 2001). Kotler (2001) posits that a number of factors influence consumer purchase decision, namely product choice, brand choice, dealer choice, purchase timing, and purchase amount. Many studies have attempted to understand the relationship between price and consumer purchase decision, concluding that product pricing is a complex matter and that there are many strategies that influence consumer perceptions and purchase intentions (Alba et al., 1994; Chandrashekara et al., 2003; Hardesty et al., 2003; Hildalgo et al., 2008; Manzur et al., 2011). Other studies have 2 focused on the service industry with little focus on the product industry For instance Kane (2007) studied the effect of different pricing strategies on consumer purchase decision in the insurance industry, which is a service industry. The study tried to find out how pricing strategies influence the choice of insurance policy that consumers purchase. In the study, it was found out that consumers purchased policies which had lower prices compared to higher priced policies. Kane (2007) therefore concluded that consumers consider the pricing strategy adopted by the insurance company when purchasing a policy. A number of studies on pricing strategies and consumer purchase decision have been carried out in the western part of the world. The study of Kopalle et al., (2009) provides literature review on retailer pricing with a focus on the interaction between pricing strategies and competitive effects in Europe. Gauri et al., (2010) looked at the framework of online and offline retail pricing in the developed world. There are yet other researchers who have studied the concept of pricing strategy in the western world (Ellickson et al., 2008; Goldrick et al, 2000). Besides there has not been consistency in the research methodology used to analyze data in the previous studies. For instance, Jung, (2014) used time-series methods to exam the effects of pricing strategies. In her study, Kane, (2007) used online survey instrument managed through the Web-survey service zoomerang to obtain data. Ellickson & Misra (2008) used the Bayesian structure in their study. There have been mixed finding in the area. Some academic research has established that temporary price reductions substantially increase short-term brand sales as a result of consumers’ perception (Blattberg et al., 1995). On the other hand, recent studies have found out that promotion effects die out in subsequent weeks or months (Srinivasan et al., 2008). There also seems to be scanty of studies on the influence of pricing strategies on consumer purchase decision in the Kenyan context particularly focusing on supermarkets in Nairobi hence the need for the study. 1.1.1 Pricing strategies Price is the amount a customer pays for the product or the sum of the values that consumers exchange for the benefits of having or using a product or service (Bearden et al., 2004). According to Rosa et al., (2011), the importance of price as a purchase stimulus has a key role in price management since not only does it determine the way prices are perceived and valued , but it also 3 influences consumer purchase decision. Lichtenstein et al., (1993) points out that price is central to consumer purchase decision due to its presence in all purchasing situations. Marketers realize that consumers use price to differentiate products with almost similar characteristics and therefore they use pricing as a differentiating element between substitute products. Pricing strategy is paramount to every organization involved in the production of consumer goods and services because it gives a cue about the company and its products because a company does not set a single price but rather a pricing structure that covers different items in its line (Kotler et al., 2001). A pricing strategy takes into account segments, ability to pay, market conditions, competitor actions, trade margins and input costs. A good pricing strategy also includes the perspectives of the consumer, the organization, and the competition (Cram, 2006) thus ensuring that an organization has a sustainable competitive advantage (Dutta et al., 2002). Tang et al., (2001) observes that there is nothing more important in business than the right pricing strategy. There are various pricing strategies that retailers can adopt and they vary across industries (Hinterhuber, 2008). These strategies can be categorized into three groups namely cost-based pricing, competition-based pricing and customer value-based pricing. Cost-based pricing primarily uses data from cost of production to determine prices. It does not take competition into consideration and also does not examine consumer purchase decision (Hinterhuber, 2008). Competitor oriented pricing uses competitors’ price as a starting point for price setting (Blythe, 2005). It uses anticipated or observed price levels of competitors as primary source for setting prices (Hinterhuber, 2008). Customer value-based pricing uses the value that a product or service delivers to a segment of customers as the main factor for setting prices (Hinterhuber, 2008). Customer value-based pricing is increasingly recognized in the literature as superior to all other pricing strategies (Ingenbleek et al., 2003). Retailers using this strategy may choose to use Everyday Low pricing strategy or High-low pricing strategy, which are particularly well implemented in the supermarkets due to the fact that they are easy to manage. There are also pricing strategies for new products. They include the price skimming strategy and the penetrating pricing strategy. Price skimming is a pricing policy whereby a firm charges a high introductory price, often coupled with high promotion (Lam et al., 2004). It refers to setting the highest initial price that customers really desiring the product are willing to pay (Kerin et al., 2004). Penetrating pricing is setting a low initial price on a new product to appeal immediately to the 4 mass market (Kerin et al., 2004. Penetrating pricing is used when an organization aims at setting low prices for its new product in order to attract large number of customers and a large market share (Kotler et al, 2001) The two strategies on which this study focuses are anchored on customer-related and competition- related categories. This is because they mainly focus on what the consumer needs and how they interpret the pricing signals and also on what the competitors are doing so that they can have a competitive advantage. These two pricing strategies are Everyday Low Pricing strategy (EDLP), and High-Low (HI-Lo) strategy. An EDLP retailer tends to offer lower average prices, whereas a Hi-Lo retailer offers frequent discounts (PopkowskiLeszczyc et al., 2004. 1.1.2 Consumer purchase decision Consumer purchase decision is a complex, dynamic issue which cannot be defined easily and commonly (Engel et al., 2006). It has therefore been defined in different ways by different researchers depending on the focus of the research. According to Peter & Donnelly (2003) consumer purchase decision is an individual’s purchase and consumption decision influenced by culture, social class and reference group, and price. Schiffman & Kanuk (1997) define consumer purchase decision as the study of individuals, groups, or organizations and the process they use to select, secure, and dispose of products, services, experiences, or ideas to satisfy needs and the impacts that these processes have on the consumer and society. In totality, consumer purchase decision reflects on consumers’ decisions with respect to their acquisition, consumption and disposition of goods, services, time and ideas (Schiffman & Kanuk, 2004). Kotler (2001) came up with a list of factors that influence consumer purchase decision. He categorized them into two categories, the market stimuli and the buyer characteristics. The market stimuli include the product, price, place and promotion, while the buyer characteristics include culture, social, personal and psychological factors. Kotler (2001) further posits that buyers decisions are characterized by the product choice, brand choice, dealer choice purchase timing and purchase amount. In this study consumer purchase decision will be measured in terms of brand choice, store choice, purchase timing and purchase amount. Brand choice is the behavior that involves goals requiring an action, imposing upon the buyer’s intention and also the attitudes about the existing brand alternatives in the buyer’s evoked set that results from an arrangement of a 5 preferential order regarding that brand (Aaker, 1991). Store choice is the best alternative of store that consumers settle on after considering important factors like pricing strategy. Purchase timing is the actual time that a consumer buys from his store choice. Purchase amount is the total amount of products a consumer purchases at one particular time. With the fast pace of product introductions, spurred by technological development, organizations need to understand what really motivates consumers to choose the product they buy and where to buy it. Ellickson & Misra (2009) found that pricing strategy is very important when it comes to consumer purchase decision since it influences the choice of store, the purchase amount and the choice of brand. Grewal, (2014) asserts that research into consumer purchase decision with regard to pricing is ubiquitous in the marketing literature and that literature suggests that consumers perceive price in both positive and negative roles that ultimately influence purchase decision. In both positive and negative respects, perceptions of price operate as marketplace cues that aid the consumer in their decision making process within increasingly complex market situations (Dodds, 1995). Consumer purchase decision is explained and understood in terms of the signaling theory, adaptation level theory and theory of reasoned action. These theories explain the reasons why consumers behave the way they do when faced with different situations. They also explain the marketing stimuli which influence consumer actions. Marketing stimuli relate to the activities and inputs of retailers, in particular the components of their market mix, namely, product, price, place and promotion. Other stimuli include economic, political, and technological elements in the marketing environment. These impact on buyers whose social and cultural background, lifestyles, and group membership influence their purchase decision. Marketers strive to understand this behavior so that they can better formulate appropriate marketing strategy that will result in increased sales. Marn et al., (2003) affirm that understanding how pricing strategies affect consumer purchase decision is very important for retailers because it allows organizations to develop appropriate strategies. With the current growth in technology, consumers have become more knowledgeable since information is easily accessible. Consumers who are more knowledgeable understand price for value and adjust decision towards a product and service provider accordingly (Huchzermeier et a., 2002). Organization therefore need to keep abreast with these changes since failure to understand 6 the relationship between pricing strategies and consumer purchase decision may result in loss of customers to competitors who understand the relationship (Cram, 2006). It is important to study the consumer purchase decision because marketers gain a good insight into understanding what makes consumer prefer one product over another and what price they are willing to pay for a particular product. By obtaining a view into how consumers think, feel, reason and choose, marketers can use this information to not only design products and services that will be in demand, but also how to present these options to the consumer base in an attractive fashion and the best price to offer (Schiffman & Kanuk, 1997). Organizations recognizing the voice of the customer when developing pricing strategies provide additional competitive advantage (Dutta et al., 2002). Studies have linked the pricing policies of an organization to consumer value perceptions and ultimately shopping intentions (Biswas et al., 2002). 1.1.3 Supermarkets Sector in Kenya Supermarkets sector in Kenya is arguably still in its formative stage though the first supermarket was established in the 1960s, with many having since rapidly been opened all over the country with record count standing at around 220 supermarkets (Neven & Reardon, 2005). In East Africa, Kenya’s supermarket industry is considered the most developed and rapidly expanding with an annual growth rate of 18% (Kibwage et al., 2008). The supermarket sector in Kenya comprises of a number of retail chains but the four main retail chains are Uchumi, Tuskys, Nakumatt and Ukwala (Ouma et al., 2013). Ouma et al., (2013) note that although the foregoing is the case, the market concentration has kept increasing and several independent supermarkets have also come up, with Nairobi accounting for many of the supermarket chains owing to its population. This is the reason why the research was based in Nairobi. One main reason why the research was based on the four supermarkets mentioned above is the fact that they have branches distributed all over Nairobi County and this helped in giving a representative data of the whole County. The supermarkets sector in Kenya is characterized by stiff and increasing competition and therefore a good pricing strategy is ideal to make any supermarket have a competitive advantage. Supermarkets in Nairobi are exposed to many pricing strategies. Among the many pricing strategies, EDLP and Hi-Lo are the best defined strategies that most supermarkets have adopted. 7 This study seeks to find out how these strategies influence the consumer purchase decision in Nairobi. 1.2 Problem statement The challenge facing most organizations today is stiff competition in the market which is not only local but also global. There are many retailers in the market who all aim at attracting the same customer base and therefore for an organization to have a competitive advantage, it has to come up with good strategies that will ensure that it has a competitive advantage. One of the strategies that many organizations have to focus on is the pricing strategies. Many organizations do not understand how price influences consumer purchase decision. In his quest to explain the nature of relationship between price and consumer purchase decision, Smith et al., (2000) argues that creating a price perceived by the consumer to be too high may lead the consumer to a competitor. On the other hand, pricing too low may bring the wrong type of consumer (Smith et al., 2000). An organization should consider other factors when coming up with a pricing strategy because the pricing strategy an organization chooses must blend with the goals, culture, and market of the organization (Shoemaker, 2003). Pricing must also provide a perception of value to a consumer in order to influence their purchase decision (Hellier, 2002; Nagle & Cressman, 2002; Shoemaker, 2003). There are studies which have used different methodologies when looking at the influence of pricing strategies on consumer purchase decision in the past. For instance, Jung, (2014) used time- series methods to exam the effects of pricing strategies. In her study, Kane, (2007) used online survey instrument managed through the Web-survey service zoomerang to obtain data. Ellickson & Misra (2008) used the Bayesian structure in their study. It is therefore important for this a study to use a different method to find out whether the results will be similar. This study adopted the Pearson Correlation and mean and standard deviations to analyze data. Fassnacht & Husseini (2013) in their study, found out that there are several studies which have been published on pricing strategies in retailing during the last years, but no comprehensive literature review of this topic with its determinants and outcomes exists. A gap also exists in the elements used to define consumer purchase decision. Kane, (2007) used customer retention and customer loyalty to measure consumer purchase decision. On the other hand, Chen, (2009) used 8 belief, trust and perceived risk to measure consumer purchase decision. Therefore, it was important for a study to be carried out using other elements to find out whether the results would tally. There are three studies by Levy et al., (2004), Grewal and Levy, (2007) and Ailawadi et al, (2009) that center the topics of pricing and retailing, but without focusing on pricing strategy. There are other researchers who have also looked at the influence of different pricing strategies on consumer purchase decision in different contexts but few have focused on the influence of pricing strategies on consumer purchase decision in the retail industry particularly in the supermarkets in the Kenyan context (Hellier et al., 2002; Shoemaker, 2003; Thomas, Blattberg, & Fox, 2004). Kane (2007) studied the effect of different pricing strategies on consumer purchase decision in the insurance industry and concluded that the behavior of consumers is influenced by the pricing strategy the insurance company adopts. In that regard, this study sought to assess the influence of pricing strategies on consumer purchase decision particularly in the retail industry in Nairobi. 1.2.1 Research Objectives 1.2.1.1 Main Objective The main objective of this research was to examine the influence of pricing strategies and consumer purchase decision in Nairobi County. 1.2.1.2 Specific Objectives 1. To determine the extent to which Everyday Low Pricing strategy and High-Low pricing strategy have been adopted by the supermarkets in Nairobi County 2. To establish the extent to which Everyday low pricing strategy influences consumer purchase decisions in Nairobi County 3. To determine the extent to which High-Low pricing strategy influences consumer purchase decisions in Nairobi County 9 1.3 Research Questions This research aimed at answering the following questions: 1. To what extent are Everyday Low and High-Low pricing strategies adopted by the supermarkets in Nairobi County? 2. To what extent does Everyday low pricing strategy influence consumer purchase decision in Nairobi County? 3. To what extent does Hi-Lo pricing strategy influence consumer purchase decision in Nairobi County? 1.4 Justification of the study This study will be beneficial to retail outlet managers with strategic knowledge on how pricing strategies influence consumer purchase decision. It will also enable them know how many of their competitors adopt similar pricing strategies and therefore they will be able to position themselves competitively. Secondly, the study will provide knowledge to the retail managers on the influences of Everyday Low pricing Strategy and High-low pricing strategy on consumer purchase decision and therefore they will be able to adopt the right pricing strategy for their target market. Customers and the public in general are also likely to benefit from the research by understanding the various pricing strategies that are available. This will come in handy when they are making decision in regard to what products they want to purchase, where to purchase and the amount they will purchase. By use of the product choice, store choice, purchase amount and purchase timing as the elements measuring consumer purchase decision, this study will help in filling the existing knowledge gap on elements of consumer purchase decision. The study will benefit academicians searching for information in this area of marketing by providing yet another method of analyzing the pricing strategies and consumer purchase decision variables. Future scholars will also benefit from this study as they continue in the pursuit of further studies in this topic. 10 1.5 Scope of the study The study focused on the major retail supermarkets in Nairobi County, namely Ukwala, Tuskys, Nakumatt and Uchumi. Pricing strategy is important in the retail sector as one of the influencing factors of consumer purchasing decisions. 300 customers from the supermarkets were interviewed and they provided data on consumer reaction to the pricing strategy. The study was limited to Nairobi area. 11 CHAPTER TWO LITERATURE REVIEW 2.1 Introduction This chapter focuses on studies done by other researchers with regard to how pricing strategies influence consumer purchase decision. The chapter focuses on theoretical foundation of the study. Three theories were focused on namely adaptation-level theory, theory of reasoned action and signaling theory. It also looks at the relationship between High-low pricing strategy and Everyday low pricing strategy and consumer purchase decision. The chapter also looks at the research gap the study sought to fill and lastly the conceptual framework which links the specific pricing strategies to consumer purchase decision. 2.2 Theoretical Foundation The study was anchored on three theories, namely the adaptation-level theory, the reasoned action theory and the signaling theory. These theories explain the two variables, pricing strategies and consumer purchase decision. 2.2.1 Adaptation-level theory Adaptation-level theory has been used to explain how consumers perceive different pricing strategies. To clarify the current knowledge about how subjects process numbers and how consumers interpret the different pricing strategies, Monroe (1990) and Lee (1999) established a concise list of principles of price perception. The first principle they established states that price perceptions are relative to other prices. The second principle states that consumers have differing reference prices within product categories based on discerning quality levels. The third principle states that there is a region of indifference surrounding a reference price, one in which changes in price within the region will not change a subject’s perception. A reference price may be an average of a range of prices for similar products and not one actual price. These principles explain how consumers interpret different pricing strategies that retailers use. This comes in handy with the objectives of this study since they will shed some light on how pricing strategies influence the brand choice, the purchase timing and the store choice of consumers in Nairobi. 12 For any individual, the adaptation level for a specific category is a function of the frequency of different values for that category (Kalyanara & Winer, 1995). For instance, the adaptation level of consumer purchase is a function of price, brand, place and promotion. However, price plays a bigger role in consumer purchase decision since it gives value to the product and consumers make their decision based on the perceived value. In fact, recent research finding suggest prices paid for previously purchased products indirectly influence consumer evaluations by bringing about shifts in consumer’s reference price (Chandrashekaran, 2011). One major strength of this theory is that it explains how consumers perceive different prices and how that influences their purchase decision. The theory goes further to suggest that consumers compare previous knowledge of the price to the current price in order to make a decision on whether to buy or not. Marketers should therefore ensure that information concerning the prices of their products is communicated clearly in order to erase doubts in the minds of consumers. This theory is applicable to this research as it helps in understanding how consumers translate pricing strategies and the impact the pricing strategies have on consumer purchase decision. This theory however fails to explain how consumers arrive at the purchase amount at any particular time. It clearly explains how they perceive prices but does not explain how much they are willing to sacrifice when they are shopping. This therefore led the researcher to use other theories to explain this phenomenon in this study. 2.2.2 Theory of Reasoned action The theory of reasoned action (TRA) is a well-established theory developed in the late 1950s by Martin Fishbein. Expansion of TRA by Fishbein and Ajzen occurred throughout the 1960s and 1970s and has foundation in social psychology regarding consumer behavior (Njite & Parsa, 2005). Many research studies have used this theory of reasoned action to explain different behaviors (Armitage & Christian, 2003; Bartee, Grandjean, & Beiber, 2004; Miniard & Page, 1984; Njite & Parsa, 2005; Strader & Katz, 1990; Trafimow & Finlay, 2002; Tuncalp & Sheth, 1975). The main reason why the researcher settled on this theory to base the study is because first, the TRA is a parsimonious model because it uses only three constructs to explain behavior. Shugan (2002) maintained that less parsimonious models not only present weak answers but they are also less 13 responsive to testing. Second, the TRA is the best-known social-psychological attitude-behavior model which incorporates external factors on intention to adopt an overt behavior (Prager, 2012). The major strength of this theory, as explained by Ajzen and Fishbein (1980), is the fact that it can be used to forecast, clarify, and sway people’s actions since it focuses on predicting and understanding an individual’s action. This theory was useful in this study in that it helped understand what drives consumer purchase decision. It was also useful in determining the extent to which pricing strategies could sway consumer purchase decision. The theory has different elements which explain the attitudes and behavior of an individual. The first element in the theory is the identification and measurement of interest in the behavior (Ajzen & Fishbein, 1980). It is important to identify the triggers of consumer purchase decision and to what extent those triggers influence the purchase decision. According to this theory, an individual’s purpose to make a decision is a direct determinant of the decision (Ajzen & Fishbein, 1980). The second element of the theory is the understanding of an individual’s actions and requires an understanding of two determinants, the personal and social influences (Ajzen & Fishbein, 1980). The perceived value of any pricing strategy is a personal thing because every individual places a certain value on the price. Understanding the evaluative criterion an individual uses in purchasing a product is one aspect the theory of reasoned action can address (Ajzen & Fishbein, 1980; Njite & Parsa, 2005). Consumers have greater sensitivity to price changes than changes in other product or service elements and therefore price is said to be a major determinant of consumer purchase decision. Pricing information adds to a consumer’s understanding of the product or service presented and the consumer is able to make an informed decision based on that understanding. The study used this theory to explain how pricing strategy influences consumer purchasing decision. 2.2.3 Signaling Theory Signaling theory emerged from the study of information economics under conditions in which buyers and sellers possess asymmetric information when facing a market interaction (Boulding and Kirmani, 1993; Spence, 1974). The theory involves three primary elements i.e. the signaler, the receiver and the signal itself (Connellt et al., 2011), where signalers own the information about a product which is transmitted to receivers. In the case of this study, the signaler is the retailer, the 14 receiver is the consumer and the signal is the price. The main reason why the researcher settled on the signaling theory as a basis of this research is because this theory has been used extensively in domains such as finance (Zhang & Wiersema, 2009) and marketing (Rao et al., 1999) as a framework of understanding how two parties (e.g. a buyer and seller) address limited or hidden information in a pre-purchase context (Wells et al., 2011 Consumers may use signals from the sellers when making their search and purchase decisions Inspired by this argument, several authors have suggested that consumers perceive Hi-Lo and Everyday Low Pricing strategies to be signals of low price. Using this perception, consumers then make decisions as to which of the pricing strategies appeal more to them. Retailers may anticipate consumer reactions and define their price strategies accordingly, trying to affect consumer perceptions and behavior even going to the extent of sending false signals (Nakamura & Steinsson, 2011). However, consumers can punish firms for sending these false signals using different ways (Srivastava & Lurie, 2001). In particular, consumers can withhold repeat purchases, engage in negative word-of-mouth, and call for regulatory action (Ford et al., 1990; Rao et al., 1999; Srivastava & Lurie, 2001; Wernerfelt, 1988). These disciplinary mechanisms are likely to be stronger for some attributes like price because they can be evaluated and verified before purchase (Manzur et al., 2013). This therefore is able to tame retailers and they thus avoid giving wrong signals. Retailers may choose to avoid this punishment by avoiding the signals all together. When that happens, consumers look for information through other cues. According to Manzur et al (2013) when consumers lack important information, they gather additional evidence or interpret signs and cues that have some apparent information value. One of the main strengths of this theory is the fact that it has been used in many fields of study over the years the predict behaviors of people. This theory also explains how consumers interpret signals like price when they are set by retailers. It was useful in this study in explaining how consumers interpret the various pricing strategies. It is however not without weakness. Its main weakness is that it can only be used where there is asymmetric information. EDLP and Hi-Lo pricing strategies can be used by consumers as a market signal since they help them differentiate between retailers competing using policies such as EDLP and Hi-Lo from those 15 that cannot provide such policies. Sellers who fail to fulfill the promise of offering EDLP and Hi- Lo will lose part of their reputation (Boulding & Kirmani, 1993). 2.3 Empirical review This chapter discusses the pricing strategies that the study is based on. It provides the relationship between High-Low pricing strategy and consumer purchase decision and the relationship between Everyday Low Pricing strategy and consumer purchase decision. 2.3.1 Pricing strategies A pricing strategy is a strategy that an organization adopts in order to position its products competitively in the market. There are various pricing strategies that retailers can adopt and they vary across industries (Hinterhuber, 2008). Among these strategies are Everyday Low pricing strategy and High-Low pricing strategy. In Everyday Low Pricing Strategy, the retailer charges a constant, lower, everyday price with no temporary price discounts, in contrast to competitors who offer periodic unilateral promotions (Hoch, 1994). Tang (2001) also defined EDLP as a pricing strategy involving setting lower average prices and eliminating the difference between regular and promoted prices. High-Low (Hi-Lo) pricing strategy is a type of pricing strategy adopted by retailers where a firm charges a high price for an item and later when the item’s popularity has passed, sells it to customers by giving discounts or through clearance sales (Elickson & Misra, 2008). According to Hoch (1994) the Hi-Lo retailer charges higher prices on an everyday basis, but then runs frequent promotions where prices temporarily are lowered below EDLP. Promotional pricing strategy is by far the most common form of sales promotion employed in both the service and goods industry (Hartley & Cross,1988) with an estimated number of organizations using it standard at 17 percent (Guerreiro, 2004). Other pricing strategies include price skimming strategies and introductory pricing strategy. These are mainly used when introducing a new product to the market. Another pricing strategy is the penetrating pricing whereby an initial price on a new product is set in order to appeal immediately to the mass market. 16 2.3.2 High-Low pricing strategy and consumer purchase decision High-Low (Hi-Lo) pricing strategy is a type of pricing strategy adopted by retailers where a firm charges a high price for an item and later when the item’s popularity has passed, sell it to customers by giving discounts or through clearance sales (Elickson & Misra, 2008). High-Low pricing strategy is most of the time adopted when a retailer wants to make a product popular especially where there is stiff competition from other retailers with similar products. There has been debate that High-Low pricing strategy is the most the most common form of sales promotion employed in both the service and goods industry (Hartley and Cross, 1988). Therefore, this study sought to find out whether in deed this applies to the Kenyan market and in particular in Nairobi County. The study also sought to find out the extent to which High-Low pricing strategies has been adopted by supermarkets in Nairobi. There has been debate on the effectiveness of High-Low pricing strategy in regard to influencing consumer purchase decisions. Gilbert and Jackaria (2002) assert that in many instances price discounts have been found to be effective tools in influencing purchase acceleration and product trial. Ellickson & Misra (2008) argue that Hi-Lo pricing strategy is effective when applied on high income earning customers. The study sought to find out if in deed discounts influenced the product choice and store choice of the consumer. The consumers also indicated their level of income and this was necessary for the study in order to find whether in deed High-Low pricing strategy was preferred by high income earning customers. There is a school of thought that states that the amount of products purchased is influenced by the type of pricing strategy applied with small basket shoppers preferring Hi-Lo pricing strategy (Tang et al 2001). Bell & Latin (1998) also seem to belong to this school of thought with their argument that small basket shoppers prefer Hi-Lo pricing strategy. The choice of brand is another determinant of consumer purchase decision and it is determined by the perception the customer has in regard to the quality and the value he gets from that particular brand. Suri et al., (2002) investigated the influence of pricing strategy on the perception of quality, value and sacrifice of a product. They stated that the perceived quality/ value is higher when the price is presented in a fixed price format than in a discounted format. Furthermore, both studies found that the perceived sacrifice is significantly higher when the price is presented in a discounted format than as a fixed price. 17 According to Fassnacht & Husseini (2013) customers are generally segmented into time constrained service or price seekers, expected price-shoppers and cherry pickers. They further defined cherry pickers as customers who are actively searching for price promotions and willing to shop opportunistically and accelerate the purchase when a better price comes available. They defined expected price shopper as a shopper who wants to shop at a reasonable price but doesn’t want to spend time monitoring day to day price changes or time their purchases during the retailer’s deal interval (Lattin & Ortmeyer, 1991). Finally Fassnacht & Husseini (2013) defined the time constrained shoppers as having high opportunity costs for shopping (PopkowskiLeszczyc et al., 2004). Lattin Ortmeyer (1991) affirmed that the Hi-Lo retailer can discriminate between cherry pickers and the expected-price shoppers. There are other researchers who have joined the debate with Lal & Rao (1997) stating that Hi-Lo attracts the cherry pickers. Pechtl (2004) examined the influence of pricing strategy on store choice and concluded that Hi- Lo prone consumers prefer Hi-Lo stores. Bohlman (2008) stated that the pricing strategy of a retailer to discount deeply or frequently is driven by the ratio of the size of switchers segments for which the retailer competes to the size of its loyal segment. This means that the retailers acknowledge that actually the pricing strategy influences the choice of store for the consumer. A number of studies have been conducted in the area of Hi-Lo pricing strategy. Agwu (2013) argued that an organization can combine high prices with high promotion whereby it seeks to maximize profits as much as possible while at the same time attracting more customers. Martinez- Ruiz et al (2006) argues that promotional offers on high-priced/quality brands have a stronger impact on sales of low-priced/quality brands than the reverse. Studies carried out in this area have come up with varied outcomes. Some studies suggested that the behavioral outcomes of promotions are positive while others suggested negative outcomes. According to Martinez-Ruiz et al., (2006), the overall consumers’ reaction to price promotions may take different behavioral responses such as brand switching, store switching, stockpiling, purchase acceleration, product trial, and more spending. Overall, this perception depends on whether consumers had examined the brand previously, whether consumers had previous knowledge about the brand and its pricing and what consumers think about their behavior (Tybout and Scott, 1983). There are studies that suggest that consumers are likely to switch their preferred brand if it is over-promoted (Lattin and Bucklin, 1989). Other studies have shown that there is a 18 positive strong correlation between the intent to purchase and the discount offered (Marshall & Leng, 2002). These studies infer that price promotions affect consumers’ purchase decision in different ways. Therefore, it is important for researchers to understand the appropriate match between price promotions and the specific product types. These mixed findings have necessitated a study in this area to find out the influence pricing strategies have on consumer purchase decision in Nairobi. 2.3.2 Everyday Low Price (EDLP) Strategy and consumer purchase decision In Everyday Low Pricing strategy, the retailer charges a constant, lower, everyday price with no temporary price discounts, in contrast to competitors who offer periodic unilateral promotions (Hoch et al., 1994). Tang et al., (2001) also defined EDLP as a pricing strategy involving setting lower average prices and eliminating the difference between regular and promoted prices. Going by these definitions, consumers are therefore able to take advantage of lower prices at all times instead of having to wait for a sale (Tom & Ruiz, 1997; Voss & Seiders, 2003) which in the long run affects their overall buying behavior. EDLP (Tang et al., 2001). EDLP is advantageous to both consumers and retailers in that retailers using this pricing strategy benefit through stable sales, customer loyalty, lower inventory carrying costs, reduced advertising costs and lower sales associate salary overheads (Lal & Rao, 1997; Ortmeyer et al., 1991; Tom & Ruis, 1997; Voss & Seiders, 2003).On the other hand, the consumers benefit from the consistent low prices and. This pricing strategy also appeals to consumers as a more honest strategy (Levy & Weitz, 1995). Previous research exists in this area of study with experiments being carried out in some parts of the world and conclusions being drawn. Hoch et al., (1994) who based their research on two large experiments in a Chicago-based supermarket, concluded that EDLP policies produce higher volume sales but may reduce benefits to the retailers, since the increase in volume does not completely make up for the lower margin (Voss & Seiders, 2003).Past research also distinguished between several determinants of consumer purchase decision in regard to pricing strategies. Ellickson & Misra (2008) found that pricing strategies are very important in determination of consumer purchase decision and they came up with some indicators of consumer purchase decision. One of the indicators that they identified is the amount of products purchased. This amount is also influenced by the type of pricing strategy applied with large basket shoppers 19 preferring EDLP pricing strategy (Tang et al 2001). This finding was affirmed by Bell & Latin (1998) who detected that large basket shoppers prefer EDLP pricing strategy. Another research carried out on this area was done by Tang et al., (2001) and they argued that large basket shoppers perceive a higher total utility at EDLP stores. Further support for this indicator is drawn from Pechtl (2004) who stated that EDLP prone consumers tend to have larger shopping baskets. The choice of brand is determined by the perception the customer has in regard to the quality and the value he gets from that particular brand. Suri et al., (2002) investigated the influence of pricing strategy on the perception of quality, value and sacrifice of a product. They stated that the perceived quality, value is higher when the price is presented in a fixed price format than in a discounted format. Furthermore, both studies found that the perceived sacrifice is significantly higher when the price is presented in a discounted format than as a fixed price. As previously mentioned, customers are generally segmented into time constrained service or price seekers, expected price-shoppers and cherry pickers (Fassnacht & Husseini, 2013). Lattin & ortmeyer (1991) argue that EDLP appeals the expected-price shoppers. On the other hand, Lal & Rao (1997) believe that EDLP stores attract time constrained customers. The pricing strategy of a retailer to discount deeply or frequently is driven by the ratio of the size of switchers segments for which the retailer competes to the size of its loyal segment (Bohlman, 2008). This means that the retailers acknowledge that actually the pricing strategy influences the choice of store for the consumer. Bailey (2008) examined the influence of store choice on consumers’ response to EDLP and discovered that high sale-prone consumers respond more favorably to EDLP than low sale-prone consumers. From the above findings, one can say that consumer knowledge of prices plays an important role in price management since it not only determines how prices are perceived and valued but also influences consumers’ purchase decisions (Brinkley & Bejnarowicz, 2003; Dolan, 1995; Mesak & Clelland, 1979; Monroe, 1973,1992; Shapiro, 1968; Simon, 1989; Turley & Cabaniss, 1995; Vanhuele & Dreze, 2002) and so organizations have to go an extra mile and find a fit in between the pricing strategies and strike the best balance that will give them a competitive advantage. 20 2.4 Research Gap Pricing strategies and consumer purchase decision are multi-facet phenomenon, there is no standard way of conceptualizing and measuring them across all industries. Therefore every organization needs to develop its own configuration of pricing strategies that are rooted in the realities of its competitive market, past commitments and anticipated requirements. Previous researchers have agreed that firms that implement the right pricing strategies enjoy sustainable competitive advantage which reflects on their performance. Previous studies have used various methodologies to examine the relationship between pricing strategies and consumer purchase decision. There was therefore a need to use a yet another methodology to address the issue in order to see whether the researcher will get similar outcome. A number of studies in this area have been carried out in the western part of the world (Ellickson. & Misra. 2008; Goldrick , 2000). Very few studies on this area have been done in the African context. The few studies done in the African context have focused on the service industry (Kane, 2007) leaving a research gap in the goods industry. There is therefore a need to carry out a study within the Kenyan context focusing on the retail industry. A comprehensive understanding of how pricing strategies are linked with consumer purchase decision will add to the body of knowledge in the retail industry and marketing in general. 2.5 Conceptual framework The conceptual framework below explains that pricing strategies which is the independent variable will be analyzed using two constructs: High-Low pricing strategy and Everyday Low Pricing strategy and consumer purchase decision which is the dependent variable will be analyzed using four constructs: product choice, store choice, purchase timing and purchase amount. Then the study will seek to explain the relationship between the two. 21 Figure 2.1: Pricing strategies and consumer purchase decision Pricing strategies Consumer purchase decision Independent variable Dependent variable Source: Author (2017) 2.5.1 Operationalization This sub-section explains how the researcher will measure pricing strategies and consumer purchase decision and the scales to be used. Operationalization facilitates reduction of abstract notions of construct into observable characteristics so that they can be measured by using multi- items indicators. These indicators have been used in the studies as mentioned in the table below. Table 2.1: Operationalization of Study variable VARIABLE CONSTRUCTS OPERATION DEFINITION MEASUREMENT INDICATOR SOURCE Independent variable (Pricing strategies) 1.Everyday low pricing (EDLP) strategy Process whereby a business sets and maintains a low price at which it will sell its products and services Five point Likert scale 1. Strongly disagree 2. Disagree 3. Agree 4. Strongly agree Tang et al., 2001; Tom and Ruiz, 1997 2.High-Low pricing strategy A strategy where a firm charges a high initial price and later gives discounts for certain items Five point Likert scale 1. Strongly disagree 2. Disagree 3. Agree 4. Strongly agree Martinez-Ruiz et al., 2006  High-Low pricing strategy  Everyday Low Price Pricing strategy  Product choice  Store choice  Purchase timing  Purchase amount 22 VARIABLE CONSTRUCTS OPERATION DEFINITION MEASUREMENT INDICATOR SOURCE Dependent variable (Consumer purchasing decision) 1.Brand choice This refers to the decisions consumers make in regards to products and services. Five point Likert scale 1. Strongly disagree 2. Disagree 3. Agree 4. Strongly agree Harrison & Ansell,2002;Hellier, Geursen, Carr, & Rickard,2003 2. Purchase timing This refers to the time a consumer sets aside for doing the actual purchasing Five point Likert scale 1. Strongly disagree 2. Disagree 3. Agree 4. Strongly agree Sekaran & Roger, 2009 3.Store choice This is the preferred store based on consumers' analysis of alternatives available Five point Likert scale 1. Strongly disagree 2. Disagree 3. Neutral 4. Agree 5. Strongly agree Sekaran & Roger, 2009 4. Purchase amounts This is the amount of products that a consumer purchases at any particular time Five point Likert scale 1. Strongly disagree 2. Disagree 3. Neutral 4. Agree 5. Strongly agree Sekaran & Roger, 2009 Source: Author (2017) 23 CHAPTER THREE RESEARCH METHODOLOGY 3.1 Introduction This chapter discusses the research methodology that was used to carry out this research. It details the research design used, the target population, location, sample size, sample procedure, data collection procedure, the validity and reliability of the data as well as showing how data was analyzed and presented. 3.2 Research philosophy This study was underpinned under the positivism philosophical framework. This approach seeks to use existing theory to develop hypotheses that are tested and confirmed wholly, in part, or otherwise refuted leading to further development of theory to be tested with further research. Saunders et al. (2009) argues that through positivism the researcher is concerned with facts and not impressions. Positivism is a highly objectivist view of a common, single reality. Positivists hold that anything that can be perceived through the senses is real (Sarantakos, 2005) and so reality is an externality which exists independently of human thought and perception. The positivist form of realism is referred to as naive realism (Guba & Lincoln 1998) and rests on the assumption that the external world can be accurately described and causally explained. From a methodological perspective, positivist requirements for universal principles and generalizability imply the use of quantitative methodology, and the precision and usefulness of theories derived in this manner consequently are judged by their capacity to explain and/or predict phenomena. However, instrumentalism, a sub-set of the positivist view (Friedman, 1953), regards predictive ability rather than explanatory power to be paramount. In its purest form, positivism suggests that human behaviors can be reduced to the state of generalized laws in which the individual is not of significance (nomothetic). Such research is scientific, structured, has a prior theoretical base, seeks to establish the nature of relationships and causes and effects, and employs empirical validation and statistical analyses to test and confirm theories hence why this study will use a positivist approach. 24 Positivists stress that reliability, validity, and generalizability form the cornerstone for judging the adequacy and quality of research (Sarantakos 1993; Abernethy et al., 1999; Bordens& Abbott 1999). In this type of research, reliability is usually assessed in terms of the stability of results generated through the application of some measurement instrument, such as a survey questionnaire. Validity includes the ability to test hypotheses adequately (internal validity) and the ability to extend the results obtained to wider settings (external validity). 3.3 Research Design Research design refers to the structure of an enquiry. The study used descriptive research design. Descriptive research design is used when collecting information about people’s attitude, habit or any other variety of education or social issues and the design reports the way things are at present. According to Chandran (2004) descriptive studies portray an accurate profile of persons, events or situations, describing the existing conditions and attitudes through observation and interpretation techniques. Utilization of basic descriptive, correlational, regression, and content analysis tests will be used to examine the nature of the relationship between the pricing strategies and consumer buying behavior. The survey design enabled comprehensive analysis by respondents on the influence of pricing strategies on consumer buying behavior in Nairobi. 3.4 Population of the study A population refers to the combination of elements that have similar characteristics or behavior (Mugenda & Mugenda, 2003). The population of this study was customers of Ukwala, Nakumatt, Tuskys and Uchumi supermarkets in Nairobi and the supermarket managers with a sampling representation of 315 respondents to avoid any systematic bias. These selected supermarkets were considered ideal for this research as obtaining a sample from these supermarkets was considered to constitute a fair representation given their size and location. 25 3.5 Sampling design The sampling plan describes the sampling unit, sampling frame, sampling procedures and the sample size for the study. The sampling frame describes the list of all population units from which the sample will be selected (Cooper & Schindler, 2003). A simple random sampling method was used to select participants to the survey. Slovins (1960) method was used to arrive at the sample size N= N 1+Ne2 Where, n- Sample size N- Population 1-Constant e- Margin of error. The study used 0.05 as the e 1500 1+ (1500*0.052) 1500=315 4.75 A total of at least 79 questionnaires were targeted for each supermarket. However, due to the high number of supermarket branches in Nairobi County, only those branches within the central business district were considered. The study had a total of 300 respondents. The inclusion criteria for the sample population was, frequent retail supermarket shoppers those who have visited the four supermarkets to do their shopping. The exclusion criteria consisted of customers who do not make their purchases at any of the four supermarkets. The respondents were customers of the four supermarkets. 3.6 Data Collection This study used primary data which was collected using self-administered semi-structured questionnaires accompanied by introduction letter informing the respondents who the researcher is and the purpose of conducting the research. Primary data was used because it is authoritative 26 and original. The self-administered questionnaire was used because they are commonly used instruments to collect important information about the population (Orodho, 2004) especially when the respondent can be reached. Respondents selected their answers guided by a four point Likert scale. The Likert scale is a psychometric response scale primarily used in questionnaires to obtain participants’ preferences or degree of agreement with a statement or set of statements. The researcher will provide an opportunity to include a category ‘other’ to capture any other important dimension of the variables that the respondent may suggest (Ahlstron & Westbrook, 1999). For the purpose of this study, a five point scale was used to assess these statements i.e. strongly Agree (4 points), Agree (3 points), somewhat Agree (3), Disagree (2 points) and strongly Disagree (1 point). The reason for selection of a five point even number scale is to avoid the neither type of answers. This made the respondents take a stand on a particular issue being assessed. The questionnaire was divided into two sections. The first section collected demographic information about the age, gender, occupation, approximate income per month of the customers, and level of education of the customers. The second section focused on the influence of the two pricing strategies on consumer purchase decision. These questionnaires were hand-delivered to the respondents and the drop and wait method of administration was used. It took each respondent approximately twenty minutes to complete the questionnaire. 3.7 Data analysis Data analysis is the process of data reducing, summarizing, pattern examination, and statistical evaluation necessary to prove or disapprove hypotheses (Cooper & Schindler, 2006). Before processing the responses, the data collected was edited for completeness and consistency. Primary data that was collected from the surveys administered to the respondents was in putted into the computer system for analysis using SPSS Software. The data collected was coded and categorized to make it easy to analyze and make conclusions and meaning from the data. There was checking of errors before data analysis to check for correctness of data input to the system. 27 3.7.1 Descriptive statistics This was used to analyze the first objective that is about the pricing strategies adopted by the supermarkets in Nairobi. It was also used to analyze the information regarding the consumers who were interviewed by the researcher. This information includes the mean age of the consumers, the mean size of the families of the respondents, and the mode number of visits to the supermarket. 3.7.2 Correlation analysis Correlation analysis was conducted for the second and third objective. This is done to determine whether there is a relationship between the dependent and the independent variables and the strength if present (Cooper & Schindler, 2014). The correlation coefficient value determines the measures of linear association between two variables where the coefficient is always between -1 and +1. A coefficient of -1 means that variables are perfectly related in a negative linear sense, 0 means that there is no relationship between the variables and +1 indicated that the variables are perfectly related in a positive linear sense (Cooper & Schindler, 2014) 3.7.3 Multiple regression analysis This analysis is used when there is more than one independent variable. A model of relationship is hypothesized in the form Y= β0 + β1X1 + ε where β0 and β1 are model parameters and ε= is a probabilistic error term that accounts for any variability in Y that cannot be explained by the linear relationship with x (Cooper & Schinder, 2014) The relationship between pricing strategies and consumer purchase decision was established using individual regression equations. 28 Table 3.1: Summary of data analysis method Item Data analysis Consumer profile Descriptive statistics Objective 1 To determine the pricing strategies adopted by the four supermarkets in Nairobi Descriptive statistics Objective 2 To establish the extent to which Everyday Low pricing strategies influences consumer purchase decision in Nairobi Correlation analysis and regression analysis Objective 3 To determine the extent to which High-Low pricing strategy influences consumer purchase decision Correlation analysis and regression analysis Source: Author (2017) 3.8 Reliability and Validity Reliability focuses on the correctness and exactness of the testing methods and design (Cooper & Schindler, 2006) while assessing the coefficients of stability, equivalence, and internal consistency (Cooper & Schindler, 2006). Measurement of reliability coefficients occurs numerically through correlation formulas (Cooper & Schindler, 2006). In this study, reliability was ensured through the use of a standard survey questionnaire which was administered to all customers who formed the sample selected (Saunders et al., 2012). Validation of internal reliability occurred by calculating Cronbach’s Alpha for all combinations of the variables tested. Cronbach’s alpha determines the internal consistency or average correlation of items in a survey instrument to gauge its reliability. The Alpha can take values from zero (no internal consistency) to one (complete internal consistency). Cronbach’s Alpha coefficient of 0.70 and above indicates sound and reliable measures for further analysis (Hair et al., 1998, Gliem & Gliem, 2003). In this study, a lower limit of 0.70 was accepted as a sound and reliable measure. 29 Validity refers to how accurately the data obtained captures what it was purported to measure. The indicator is developed to measure a concept of genuine measures, which also means, that is the correct data and methods of research, but also reflects the real problem or not (Bryman et al., 2007). To ensure content validity, the collection instrument was subjected to a pilot test to check for any weaknesses in design and development of the questionnaire and then the final questionnaire constructed (Page et al., 2007). To ensure generalizability, representative sampling of the population was used. 3.9 Ethical consideration The study was undertaken within ethical frameworks of social research. In particular, the researcher was guided by legal and moral principles of social research as outlined by Bryamn, (2001) which are; where there is lack of informed consent, whether deception is involved, whether there is harm to the participants, and whether there is an invasion of privacy. The researcher acted openly and truthfully to promote accuracy guided by the ethical principles of integrity and objectivity. At the onset, an introductory letter requesting access and outlining in brief the purpose of the research was presented to respondents. The confidentiality of information supplied by research subjects and the anonymity of respondents was respected. Research participants participated in a voluntary way, free from any coercion, any harm to research participants was avoided and the independence of research was clear, and any conflicts of interest or partiality was explicit (Economic and Social Research Council, 2005). Respondents to the survey were informed before consenting to participation of the survey of their right to determine how they would participate in the data collection process, including rights not to answer any question or set of questions, rights not to provide any data requested and possibly to withdraw data they have provided. 30 CHAPTER FOUR DATA ANALYSIS AND PRESENTATION 4.1 Introduction This chapter presents an analysis and report of the results from the current study. To begin with, preliminary analysis was performed on the data so as to determine the response rate. This was followed by descriptive statistics, correlation analysis and multiple regression analysis which were performed on items in the questionnaire to examine the influence of pricing strategies on consumer purchase decision. The data for this study was collected using a questionnaire in the months of March and April, 2017. 4.2 Response Rate The data collected for this research was from the customers who frequently shopped in the four supermarkets. A total of 315 questionnaires were sent out, however, 15 were not fully completed, thus not used for the study. In that regard, only 300 responses were used in the analysis of the study, thus the sample response rate was 95%. Fowler (1984) stated that, a response rate of 60% is representative enough. 4.3 Reliability test A reliability test was performed on the four components of consumer purchase decision using the Cronbach’s Alpha test. This was done to establish whether they were significant in explaining pricing strategies. A Cronbach’s Alpha value (α) greater than or equal to 0.5 is usually considered reliable. All four variables met the Cronbach’s Alpha criterion and were therefore considered reliable in explaining the dependent variable. Product choice had a Cronbach’s Alpha value of (α) 0.977, store choice (α) = 0.953, purchase amount (α) = 0.939 and purchase timing (α) = 0.965. The overall Cronbach’s Alpha value for the four variables was 0.990. This value slightly exceeds the value recommended by Theodosiou et al. (2012) of above 0.6. Therefore the items measuring consumer purchase decision were found to be reliable. The Cronbach’s Alpha value for the four variables measuring pricing strategies was (α) = 0.989. Content validity was confirmed as the scale was originally developed after a thorough review of the literature to include all the theoretical dimensions of the concept, and it was used and validated 31 in several subsequent studies (Hooley et al., 1999; White et al., 2003, Moore & Fairhurst, 2003; Hooley et al., 2005). 4.4 Respondents Demographic The demographic profile of the respondents looked at; their age bracket, gender, levels of income, size of family, occupation and frequency of shopping. The target population for this study was those who visited the four supermarkets mentioned above within Nairobi County. The data revealed that out of the respondents who shop in the four supermarkets the majority are the middle aged people falling within the age bracket of 31-35 years at 35%, followed by those within the age bracket of 25-30 years at 33%. Respondents aged between 18-24 years are at 14%, while those between 36-40 years having 10%. The minority are the ones above 40 years with 7%. Gender differences were also collected during the study. 72% of the respondents are female, while 28% were male. Data on monthly income of the respondents reveals that a majority of the respondents earn above Ksh 50,000 with a 38%, those between kshs 11,000-50,000 have 34% while the minority earn below ksh 10,000 with 28%. The data collected is shown in Table 4.1 below. Table 4.1: Respondents demographics Characteristic Option Frequency Percentages Age group 18-24 43 14% 25-30 98 33% 31-35 106 35% 36-40 31 10% Above 40 22 7% Total 300 100% Gender Female 217 72% Male 83 28% Total 300 100% Monthly income (Ksh) Below 10,000 84 28% 11,000- 50,000 103 34% More 50, 000 113 38% Total 300 100% Source: Survey data (2017) 32 From the Table 4.2 below, 55% had big families (above four members), 25% had three family members, 10% were single and families with two and four members had 5% each.Among the group interviewed, 30% shop in Nakumatt, 30% shop in Tuskys, 20% shop in Uchumi and the remaining 20% shop in Ukwala. Table 4.2: Respondents demographic II (Size of family, choice of supermarket and employment status) Source: Survey data (2017) The Figure 4.1 below is a graph showing the data on the shopping frequency of the respondents. From the data collected, 50% of respondents shop monthly, 40% shop weekly and the remaining 10% shop in every two weeks. Options Frequency Percentage Size of family single 41 10% Two members 9 5% Three members 34 25% Four members 106 5% Above four 110 25% Supermarket of choice Tuskys 124 33% Nakumatt 118 30% Uchumi 23 9% Ukwala 35 27% Employment status Employed 261 60% Not Employed 39 40% other 0 0% 33 Figure 4.1: Shopping frequency of the study population Source: survey data 4.5 Pricing Strategies used by the supermarkets in Nairobi County 4.5.1 Everyday Low Pricing Strategy The respondents were asked to express their degree of agreement with the following items in relation to Everyday Low Pricing Strategy. In Table 4.3 below, the frequency scores as well as the percentages of the responses for each item were as shown. The overall mean score was 3.3237 and standard deviation was 0.8994. 40.0 10.0 50.0 Weekly Every Fortnight Monthly How often do you shop? 34 Table 4.3: Everyday Low Pricing Strategy Everyday low pricing strategy Frequency and percentages 1 2 3 4 5 Statement F % F % F % F % F % Mean Std Dev Our prices are always low 0 0 2 1 200 67 54 18 44 15 3.4667 0.74666 We do not offer frequent discounts 6 2 123 41 97 32 47 16 27 9 2.8867 1.0146 We do not review prices frequently 8 3 23 8 54 18 167 56 48 16 3.7467 0.9084 We offer lower prices than our competitors 14 5 28 9 34 11 135 45 89 30 3.8567 1.0894 We reach out to our customers for feed back 0 0 7 2 184 61 106 35 3 1 3.3500 0.5433 Our existing customers are always buying new products 0 0 0 0 54 18 206 69 40 13 3.9533 0.5587 Our supermarket branding attracts new customers 4 1 10 3 99 33 178 59 9 3 3.5933 0.6704 Our prices are always in line with customer preference 25 8 57 19 186 62 32 11 0 0 2.7500 0.7846 We always compare our prices with the competitors’ prices 106 35 97 32 31 10 30 10 36 12 2.3100 1.3289 Overall mean score 3.3237 0.8494 Source: Survey data (2017) 4.5.2 High-Low pricing strategy The respondents were asked to express their degree of agreement with the following items in relation to High-Low Pricing Strategy. In Table 4.4 below, the frequency scores as well as the percentages of the responses for each item were as shown. The overall mean score was 3.39704 and standard deviation was 0.2724. 35 Table 4.4: High-Low Pricing Strategy High low pricing strategy Frequency and percentages 1 2 3 4 5 Statement F % F % F % F % F % Mean Std Dev We frequently run promotional campaigns for all our products 0 0 9 3 54 18 147 49 90 30 4.0600 0.7742 We always run promotions to increase store awareness 24 8 58 19 174 58 29 10 15 5 2.8433 0.8949 We normally run promotions on slow moving products 0 0 17 6 43 14 154 51 86 29 4.0300 0.8098 We regularly run promotions for new products 0 0 54 18 75 25 68 23 103 34 3.7333 1.1169 We frequently offer price discounts 0 0 71 24 146 49 83 28 0 0 3.0400 0.7154 We are quick to revise our prices when need be 6 2 23 8 87 29 137 46 47 16 3.6533 0.9051 We regularly give quantity discounts 35 12 60 20 97 32 98 33 10 3 2.9600 1.0672 We offer different price ranges for selected products to attract variety of customers 20 7 32 11 97 32 99 33 52 17 3.4367 1.1019 We frequently give promotions during holiday seasons 43 14 101 34 67 22 46 15 43 14 2.8167 1.2724 Overall mean score 3.39704 0.2724 Source: Survey data (2017) 4.6 Consumer purchase decision The respondents were asked to express their degree of agreement with the following four items in relation to product choice, store choice, purchase amount and purchase timing 36 4.6.1 Product Choice The respondents were asked to express their degree of agreement with the following four items in relation to product choice, store choice, purchase amount and purchase timing, Table 4.5: Product choice Product choice Statement Frequency and percentages 1 2 3 4 5 F % F % F % F % F % Mean Std Dev I always decide what to buy on my own without external influence 6 2 30 10 83 28 97 32 84 28 3.7433 1.0365 My decision to buy a product is influenced externally 54 18 65 22 97 32 65 22 19 6 2.7667 1.1648 I don't think much when buying something 43 14 98 33 76 25 45 15 38 13 2.7900 1.2319 I take time to think before I buy something 14 5 37 12 93 31 80 27 76 25 3.5567 1.1333 Overall mean score 3.2142 1.141625 Source: Survey data (2017) The respondents were asked to express their degree of agreement with the following four items in relation to store choice. In Table 4.4 below, the frequency scores as well as the percentages of the responses for each item were as shown. The overall mean score was 3.02 and standard deviation was 0.9999 37 Table 4.6: Store choice Store choice Frequency and percentages 1 2 3 4 5 Statement F % F % F % F % F % Mean Std Dev I always compare prices before settling on a supermarket 13 4 29 10 88 29 113 38 57 19 3.5733 1.04 I just walk to any supermarket whenever I need to buy something 5 2 98 33 107 36 57 19 33 11 3.0500 1.0121 My friends recommend a supermarket whenever I need to shop 69 23 85 28 89 30 43 14 14 5 2.4933 1.1318 I am quick to detect changes in prices in various supermarkets 6 2 75 25 143 48 68 23 8 3 2.9900 0.8158 Overall mean score 3.0267 0.9999 Source: Survey data (2017) The respondents were asked to express their degree of agreement with the following four items in relation to purchase amount. In Table 4.5 below, the frequency scores as well as the percentages of the responses for each item were as shown. The overall mean score was 3.0767 and standard deviation was 1.0434. Table 4.7: Purchase amount Purchase amount Frequency and percentages 1 2 3 4 5 Statement F % F % F % F % F % Mean Std Dev I always buy standard amount of products whenever am shopping 7 2 9 3 153 51 94 31 37 12 3.4833 0.8359 The size of my shopping basket is influenced externally 67 22 91 30 87 29 33 11 22 7 2.5067 1.1667 My shopping basket varies seasonally 5 2 79 26 104 35 72 24 40 13 3.2100 1.0309 I don't think much about how much I buy 23 8 75 25 85 28 81 27 36 12 3.1067 1.14 Overall mean score 3.0767 1.0434 Source: Survey data (2017) 38 The respondents were asked to express their degree of agreement with the following four items in relation to purchase timing. In Table 4.6 below, the frequency scores as well as the percentages of the responses for each item were as shown. The overall mean score was 2.9833 and standard deviation was 1.11915. Table 4.8: Purchasing timing Purchase timing Frequency and percentages 1 2 3 4 5 Statement F % F % F % F % F % Mean Std Dev I do not decide on my own when to shop 34 11 87 29 98 33 76 25 5 2 2.7700 1.0069 My time of shopping is determined by external factors 35 12 35 12 90 30 97 32 43 14 3.2600 1.1904 I have a standard shopping schedule 0 0 78 26 123 41 79 26 20 7 3.1367 0.8797 Any time is shopping time 76 25 65 22 55 18 61 20 43 14 2.7667 1.3996 Overall mean score 2.9833 1.11915 Source: Survey data (2017) 4.7 Influence of pricing strategies on consumer purchase decision This research aimed at investigating the relationship between pricing strategy (independent variable) and consumer purchase decision (dependent variable). This analysis was achieved by using correlation analysis first and then multiple regression analysis. 4.7.1 Pearson’s correlation analysis A correlation analysis was carried out to find out if there is a correlation between pricing strategies and consumer purchase decision. In the Table 4.9 below correlation between two adjacent variables was shown using the asterisks sign (*). As cited by Wileman & Jary (1997) the spearman correlation coefficient (rs) ranging from 0.00 to 0.1 is considered very weak, from 0.20 to 0.39 is considered weak, from 0.4 to 0.59 is considered moderate, 0.6 to 0.79 is strong and lastly 0.8 to 1.0 is very strong. 39 Based on the results on the above table, the Spearman correlation coefficient for consumer purchase decision and EDLP was rs = .986, p-value =.000 which showed a very strong, positive monotonic relationship (p <0.01 for a two-tailed test), that of consumer purchase decision and HLP was rs = .987, p-value =.000 which showed a very strong, positive monotonic relationship (p < .001 for a two-tailed test), both based on N=300 complete observations. Table 4.9: Correlation results Correlations CONSUMER PURCHASE DECISION EDLP HLP Spearman's rho CONSUMER PURCHASE DECISION Correlation Coefficient 1.000 .986** .998** Sig. (2-tailed) . .000 .000 N 300 300 300 EDLP Correlation Coefficient .986** 1.000 .987** Sig. (2-tailed) .000 . .000 N 300 300 300 HLP Correlation Coefficient .998** .987** 1.000 Sig. (2-tailed) .000 .000 . N 300 300 300 **. Correlation is significant at the 0.01 level (2-tailed). 4.7.2 Regression Analysis Multiple regression was done to further explain the relationship between pricing strategies and each of the four variables of consumer purchase decision. 4.7.2.1 Influence of pricing strategies on product choice Multiple regression was done to further explain the relationship between pricing strategies and consumer purchase decision. From the results in Table 4.10 below, the Beta (B) values were the coefficients used in coming up with the regression model. Therefore, the regression model equation was as follows: Y1= 0.140+ 0.020EDLP + 0.234HLP 40 The Table 4.10 below shows the results of the analysis. In the first section of the table; the model summary; provides the R values, The R value explains how well the whole model describes the data. In this case the model explained 98.2% (R) of the data. R square explains the extent to which the variability of the dependent variable is explained by the independent variables. In this case 96.4% of the variability in customer product choice was explained by the independent variables namely EDLP and HLP. Sometimes the R squared can be overestimated so the adjusted R squared corrects the values. So in our case, the adjusted R square value was 96.4%. This means that accurately, 96.4% of the total variability of the dependent variable was explained by the independent variables. In the second section of the same table we have the analysis of variance (ANOVA). This section provides statistics about the overall significance of the model being fit. By looking at the significant value also known as the p-value, one is able to know if there are any independent variables explaining the dependent variable, in this case the p-value is 0.000 which is less than 0.05. This tells us that there are independent variables explaining product choice therefore the model is statistically significant. P-values tell us if we should reject or accept the null hypothesis. In ANOVA, the null hypothesis always states that the model has no explanatory power so by getting a significant p-value (p˂0.05) one rejects the null hypothesis. In the last section of table, we were able to see which specific independent variables were significant in explaining product choice by looking at the p-values. For a variable to be significant in the model its p-value should be less than 0.05. In this case EDLP (p=0.163) was not significant in explaining product choice but HLP (p=0.000) positively significant in explaining product choice. After eliminating the insignificant variables, the final regression model was as shown below: Y1= 0.140+ 0.234HLP 41 Table 4.10: Pricing strategies and product choice regression results Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .982a 0.964 0.964 0.839 a. Predictors: (Constant), HLP, EDLP ANOVAa Model Sum of Squares Df Mean Square F Sig. 1 Regression 5641.935 2 2820.967 4010.63 .000b Residual 208.902 297 0.703 Total 5850.837 299 a. Dependent Variable: PRODUCT CHOICE b. Predictors: (Constant), HLP, EDLP Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 0.14 0.162 0.862 0.389 EDLP 0.02 0.014 0.074 1.397 0.163 HLP 0.234 0.014 0.91 17.242 0 a. Dependent Variable: PRODUCT CHOICE Source: Survey data (2017) 4.7.2.2 Influence of pricing strategies on store choice From the results in the above Table 4.11, the Beta (B) values were the coefficients used in coming up with the regression model. Therefore, the regression model equation was as follows: Y1= 0.159+ 0.049EDLP + 0.168HLP Table 4.11 shows the results of the analysis. In the first section of the table; the model summary; provides the R values, The R value explains how well the whole model describes the data. In this 42 case the model explained 98.0% (R) of the data. R squared explains the extent to which the variability of the dependent variable is explained by the independent variables. In this case 96.1% of the variability in customer product choice was explained by the independent variables namely EDLP and HLP. Sometimes the R squared can be overestimated so the adjusted R squared corrects the values. So in our case, the adjusted R square value was 96.1%. This means that accurately, 96.1% of the total variability of the dependent variable was explained by the independent variables. From the analysis of variance (ANOVA) the significant value also known as the p-value is 0.000 which is less than 0.05. This tells us that there are independent variables explaining store choice therefore the model is statistically significant. P-values tell us if we should reject or accept the null hypothesis. In ANOVA, the null hypothesis always states that the model has no explanatory power so by getting a significant p-value (p˂0.05) one rejects the null hypothesis. In the last section of table, we were able to see which specific independent variables were significant in explaining store choice by looking at the p-values. For a variable to be significant in the model its p-value should be less than 0.05. In this case all variables were less than 0.05 therefore they were all significant in explaining store choice. The final regression model was as shown below: Y2= 1.159 + 0.049EDLP + 0.168HLP + ε 43 Table 4.11: Pricing strategies and store choice regression results Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .980a 0.961 0.961 0.746 a. Predictors: (Constant), HLP, EDLP ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 4077.154 2 2038.577 3659.843 .000b Residual 165.433 297 0.557 Total 4242.587 299 a. Dependent Variable: STORE CHOICE b. Predictors: (Constant), HLP, EDLP Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 1.159 0.145 8.017 0 EDLP 0.049 0.013 0.215 3.9 0 HLP 0.168 0.012 0.769 13.945 0 a. Dependent Variable: STORE CHOICE Source: Survey data (2017) 44 4.7.2.3 Influence of pricing strategies on purchase amount From the results in the Table 4.12 below, the Beta (B) values were the coefficients used in coming up with the regression model. Therefore, the regression model equation was as follows: Y3=1.193 + 0.047EDLP + 0.174HLP +ε In this case the model explained 97.1% (R) of the data. R squared explains the extent to which the variability of the dependent variable is explained by the independent variables. In this case 94.3% of the variability in product amount was explained by the independent variables namely EDLP and HLP. Sometimes the R squared can be overestimated so the adjusted R squared corrects the values. So in our case, the adjusted R square value was 94.2%. This means that accurately, 94.2% of the total variability of the dependent variable was explained by the independent variables. In the second section of the same table we have the analysis of variance (ANOVA). In this case the p-value is 0.000 which is less than 0.05. This tells us that there are independent variables explaining product choice therefore the model is statistically significant. In the last section of table, we were able to see that all the variables were significant since all the p-values were less than 0.05. The final regression model was as shown below: Y3=1.193 + 0.047EDLP + 0.174HLP +ε 45 Table 4.12: Pricing strategies and purchase amount regression results Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .971a 0.943 0.942 0.929 a. Predictors: (Constant), HLP, EDLP ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 4211.689 2 2105.845 2442.181 .000b Residual 256.097 297 0.862 Total 4467.787 299 a. Dependent Variable: PURCHASE AMMOUNT b. Predictors: (Constant), HLP, EDLP Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 1.193 0.18 6.631 0 EDLP 0.047 0.016 0.2 2.987 0.003 HLP 0.174 0.015 0.775 11.588 0 a. Dependent Variable: PURCHASE AMMOUNT Source: Survey data (2017) 46 4.7.2.4 Influence of pricing strategies on purchase timing From the results in the Table 4.13 below, the Beta (B) values were the coefficients used in coming up with the regression model. Therefore, the regression model equation was as follows: Y4= -0.616 + 0.028EDLP + 0.222HLP + ε From Table 4.13, the R value is 98.8% which means that the model explained 98.8% (R) of the data. R squared explains the extent to which the variability of the dependent variable is explained by the independent variables. In this case 97.7% of the variability in customer purchasing time was explained by the independent variables namely EDLP and HLP. The adjusted R square value was 97.7%. This means that accurately, 97.7% of the total variability of the dependent variable was explained by the independent variables. In the second section of the same table we have the analysis of variance (ANOVA). In this case the p-value is 0.000 which is less than 0.05. This tells us that there are independent variables explaining purchase timing therefore the model is statistically significant. P-values tells us if we should reject or accept the null hypothesis. In the last section of table, we were able to see which specific independent variables were significant in explaining product choice by looking at the p-values. For a variable to be significant in the model its p-value should be less than 0.05. In this case all the variables were significant in explaining purchase timing. The final regression model was as shown below: Y4= -0.616 + 0.028EDLP + 0.222HLP + ε 47 Table 4.13: Pricing strategies and purchase timing regression results Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .988a 0.977 0.977 0.66 a. Predictors: (Constant), HLP, EDLP ANOVAa Model Sum of Squares Df Mean Square F Sig. 1 Regression 5461.125 2 2730.563 6260.37 .000b Residual 129.541 297 0.436 Total 5590.667 299 a. Dependent Variable: PURCHASE TIMING b. Predictors: (Constant), HLP, EDLP Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) -0.616 0.128 -4.817 0 EDLP 0.028 0.011 0.108 2.534 0.012 HLP 0.222 0.011 0.883 20.767 0 a. Dependent Variable: PURCHASE TIMING Source: Survey data (2017) 4.7.2.5 Influence of pricing strategies on consumer purchase decision overall regression model Overall regression analysis was done to further explain the relationship between pricing strategies and consumer purchase decision. From the results in Table 4.14 below, the Beta (B) values were the coefficients used in coming up with the regression model. Therefore, the regression model equation was as follows: Y5 = 1.875 + 0.143EDLP + 0.799HLP + ε Table 4.14 shows the results of the overall analysis. In the first section of the table; the model summary; provides the R values, The R value explains how well the whole model describes the data. In this case the model explained 99.4% (R) of the data. R squared explains the extent to which 48 the variability of the dependent variable is explained by the independent variables. In this case 98.8% of the variability in customer product choice was explained by the independent variables namely EDLP and HLP. Sometimes the R squared can be overestimated so the adjusted R squared corrects the values. So in our case, the adjusted R square value was 98.8%. This means that accurately, 98.8% of the total variability of the dependent variable was explained by the independent variables. In the second section of the same table we have the analysis of variance (ANOVA). This section provides statistics about the overall significance of the model being fit. By looking at the significant value also known as the p-value, one is able to know if there are any independent variables explaining the dependent variable, in this case the p-value is 0.000 which is less than 0.05. This tells us that there are independent variables explaining product choice therefore the model is statistically significant. P-values tells us if we should reject or accept the null hypothesis. In ANOVA, the null hypothesis always states that the model has no explanatory power so by getting a significant p-value (p˂0.05) one rejects the null hypothesis. In the last section of table, we were able to see which specific independent variables were significant in explaining the overall consumer purchase decision by looking at the p-values. For a variable to be significant in the model its p-value should be less than 0.05. In this case all the variables were significant in explaining overall consumer purchase decision. The final regression model was as shown below: Y5 = 1.875 + 0.143EDLP + 0.799HLP + ε 49 Table 4.14: Pricing strategies and consumer purchase decision overall regression Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .994a 0.988 0.988 1.78971 a. Predictors: (Constant), HLP, EDLP ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 77139.291 2 38569.645 12041.535 .000b Residual 951.306 297 3.203 Total 78090.597 299 a. Dependent Variable: CONSUMER PURCHASE DECISION b. Predictors: (Constant), HLP, EDLP Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 1.875 0.347 5.41 0 EDLP 0.143 0.03 0.147 4.766 0 HLP 0.799 0.029 0.85 27.571 0 a. Dependent Variable: CONSUMER PURCHASE DECISION Source: Survey data (2017) 50 CHAPTER FIVE DISCUSSIONS, CONCLUSION, AND RECOMMENDATION 5.1 Introduction The purpose of this study was to examine the influence of pricing strategies on consumer purchase decision in supermarket in Nairobi County. To make the determination, the study design focused on the attitudes towards the purchasing decisions held by adults living in Nairobi County. Three hundred and fifteen individuals 18 years and older, and those who shop in Ukwala, Nakumatt, Tuskys or Uchumi supermarkets were the focus of the study. This chapter summarizes the key findings from the study based on the research objectives, draws conclusions arising from the findings and then makes recommendations. The research objectives of this research were: (1) to determine the extent to which pricing strategies are adopted by supermarkets in Nairobi County; (2) to establish the extent to which Everyday low pricing strategy influences consumer purchase decisions in Nairobi County; (3) to determine the extent to which High-Low pricing strategy influences consumer purchase decisions in Nairobi Based on data obtained and analyzed, the following key findings were found. A questionnaire was administered to 315 individuals, where only 300 were fully responded to, showing that it is 95% response rate. Out of the three hundred respondents, 55% were female, 40% were male while 5% did not respond to this question, with 60% of them being employed and 40% of them unemployed. A majority of the respondents had a family of above four members and were between the ages of 31– 35 years of age while the least being in the age bracket of 18-24 years old. 5.2 Discussions This section summarizes the findings based on the research objectives. 5.2.1 The extent of adoption of pricing strategies by the supermarkets One of the objectives of the study was to find out the extent of adoption of pricing strategies by supermarkets in Nairobi County. A question was posed to the supermarket market managers on the type of pricing strategy their respective supermarkets have adopted and the results analyzed. 51 Based on previous research promotional pricing strategy is by far the most common form of sales promotion employed in both the service and goods industry (Hartley and Cross, 1988). However, it was not clearly cut the type of pricing strategy the supermarket adopted with Nakumatt and Tuskys inclining more towards High-Low strategy and Ukwala and Uchumi inclining towards Everyday Low pricing strategies. Based on the information provided by the supermarket managers, the supermarkets have not focused keenly on informing the consumers about their pricing strategies but rather advertise and carry out promotions. From the study, the managers need to engage their customers more in order to influence their purchase decisions. According to the Signaling theory (Spence, 1974) the signaler sends signals and the receiver interprets the signal. In this case, the signaler who is the marketer needs to send clear signals in form of pricing strategies to the receiver who is the consumer. The consumer will therefore react to the signal depending on how they interpret it. If marketers do not send this signal, the consumer may use other cues to tell those supermarkets apart, which may not favour some of the marketers. 5.2.2 Everyday Low pricing strategy and consumer purchase decision The second objective of the study was to determine the influence of Everyday pricing strategy on consumer purchase decision and an analysis of the pricing strategy on consumer purchase decision was conducted. Product choice, store choice, purchase timing and purchase amount were the elements that were used to explain consumer purchase decision. Understanding these elements allows managers to develop strategies to meet consumer needs and increase the financial performance of the organization (Cram, 2006). The descriptive analysis showed that Every Day Low pricing strategy does influence consumer purchase decision specifically the purchase amount and product choice. The correlation and multiple regression analysis conducted to determine the relationship between pricing strategies and consumer purchase decision showed that there is a relationship between Everyday Low pricing strategy and consumer purchase decision. This suggests that supermarkets should adopt this pricing strategies that appeal to the consumers in order to influence their purchasing decisions and make more sales. 52 Time constrained consumers find the EDLP supermarkets more attractive due to their lower basket prices (Lal & Rao, 1997). However, this was not clear from the study since consumers seemed to have a favorite supermarket regardless of the time available. Thus the EDLP strategy does not lead to a clear segmentation in which its clientele consists of mainly time constrained consumers. However, the study seemed to be consistent with Helson’s (1964) adaptation-level theory, the consumers are likely to assume that prices are always low in the supermarkets using EDLP strategy. 5.2.3 High-Low pricing strategy and consumer purchase decision The theory of reasoned action focuses on predicting and understanding an individual’s action) Ajzen & Fishbein, 1980). According to Ajzen & Fishbein (1980), the identification and measurement of an individual’s interest in a behavior leads to the prediction of an action. By understanding what influences consumer purchase decision, the marketers can be in a position to predict how they react to a particular strategy. This theory was used to predict how consumers would react to High-Low pricing strategy. The third objective of the study was to determine the influence of High-Low pricing strategy on consumer purchase decision. Similarly, product choice, store choice, purchase amount and purchase timing were the elements that were used to explain consumer purchase decision. Descriptive analysis showed that High-Low pricing strategy does influence consumer purchase decision to a great extent. The correlation and multiple regression analysis conducted to determine the relationship between this pricing strategy and consumer purchase decision showed that there is a relationship between the pricing strategy and consumer purchase decision. High-low pricing strategy seems to be more popular that Everyday low pricing strategy. This could be as a result of product substitution, forward buying or purchase acceleration, brand switching and product testing or repeat purchase (Jung, 2014). Product testing occurs when people try the product for the first time because it is on promotion. This reflects one of the most common aims of sales promotions to encourage consumers to try new products (Hawkes, 2009). 5.3 Conclusion The study contains research yielding empirical data regarding the influence of the pricing strategies on consumer purchase decision. The study looked at the effect demographic factors such as; age, 53 level of income and size of family have on their purchase decisions. It was established that customers who frequently shop at the four supermarkets where the research was conducted are those within the age bracket of 31 – 35 years, have a families of above four members and are employed. These are the middle aged people who have regular income. Customer purchase decision is a complex issue that is dependent on a number of factors. In that regard, several factors were used to measure this, that is, product choice, store choice, purchase timing and purchase amount. The relationship between the pricing strategies and consumer purchase decision was analyzed using correlation and multiple regression analysis. The positive relationship revealed in the study suggests that customers have become more price sensitive and are more demanding on the value for their money. It was also observed that customers are hold on their shopping program until prices are favorable. Therefore, supermarkets which fail to understand the consumer purchase decisions will lose their customers to those that understand this decision. 5.4 Recommendations The study presents managerial recommendations presented as below. To succeed in the retail industry and consumer understanding consumer purchase decision, managers of retail outlets must evaluate ways to influence consumer decisions (Choi & Weiss, 2005). The findings of the study indicate that pricing strategies influence consumer purchase decision. The findings of the current study provide important pointers to the managers who need to have a competitive advantage. To succeed in the retail industry, managers must evaluate ways to influence consumer purchase decision (Choi & Weiss, 2005). Managers developing customized pricing strategies provide the organization the ability to adapt to changing consumer purchase decisions. Improvement in pricing strategies especially those including consumer perspective, create sustainable competitive advantages for an organization. According to Capro et al., (2003) providing as much information as possible to a consumer enables more informed decision making and the perception of adding a valuable service. Adding value to consumers and improving their level of knowledge provides a barrier to losing customers. 54 Managers should therefore inform their customers about their pricing strategies in order to influence their decisions. When a customer leaves one supermarket for another or purchases for the first time, price is foremost on the criteria list. Managers who understand the consumer’s price position will succeed in providing their organization a competitive advantage (Cram, 2006; Dutta et al., 2002). The insight is applicable to managers of all types of retail outlets and is not limited to supermarkets. The recommendation to managers is that price is at least as important to consumers when purchasing products as customer service. Managers must spend equal amount of organizational resources on developing appropriate pricing strategies as is spent on customer acquisition and service efforts. Pricing must become a strategic capability within all supermarkets as well as other industries (Dutta et al., 2002). 5.5 Limitations of the study and suggestions for future research This study provides useful insight into the types of pricing strategies adopted by supermarkets in Nairobi, and the extent to which these pricing strategies influence consumer purchase decision. However, it should be noted that the study population was limited to geographic area of Nairobi County. While the location provides an interesting population considering the concentration of insurance companies in the area, a further research opportunity would be to examine a countrywide population. These supermarkets within which the study was carried out have established branches in major towns within the country, thus enlarging the sample size would provide a wider perspective on the issues mentioned. This research also focused on only four major supermarkets. Future research should focus on other super markets like Naivas, Chandarana, Carre four, Tumaini and Power star as these have incorporated several pricing strategies within their operations and have a major uptake from the consumers. A study on these supermarkets will also provide a broader perspective on other pricing strategies within the retail industry as well as the influence these have on consumer purchase decision. 55 Further research opportunities would be a longitudinal or experimental study. Being able to observe actual decisions relative to pricing strategies provides the full cycle involved in purchasing decisions. A casual design is another opportunity for future research. I identifying whether pricing strategies actually cause specific purchase decisions would add valuable information to organizations. 56 REFERENCES Abernethy, M. A, Chua, W. F, Luckett, P. P & Selto, F. 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Data from this research will be kept under lock and key and reported only as a collective combined total. No one other than the researcher will know your individual answers to this questionnaire. If you agree to participate in this project, please answer the questions on the questionnaire as best you can. It should take approximately twenty minutes (20) to complete. If you have any questions about this project, feel free to inquire from me. Thank you for your assistance in this important endeavor. Sincerely yours, Researcher 63 APPENDIX II: QUESTIONNAIRE Section A: General Information 1. Which of the following age groups do you belong to? 2. Gender 3. Which of the following size of families do you belong to? 4. Employed: 5. Level of income per month: 6. Which of the following supermarkets do you frequent? 18 – 24 years 36 – 40 years 25 – 30 years Above 40 years 31 – 35 years Male Female Ksh 11,000 – Ksh 50,000 Below Ksh 10,000 More than Ksh 50,000 Nakumatt Ukwala Single member No Four members Two members Yes Above four Uchumi Three members Tuskys 64 7. On average, how often do you do your shopping? Daily Monthly Weekly Every fortnight 65 Section B. Pricing strategies adopted by supermarkets 8. Please indicate with a tick the pricing strategy that your supermarket uses. Select one (1). 9. What is your reaction to Everyday Low Pricing strategy? Please indicate with a tick the extent to which you agree with the following statements concerning the extent of adoption of Everyday Low pricing strategy in your supermarket. (1 = strongly disagree; 2 = disagree; 3 = agree; 4 = strongly agree). STATEMENT SCALE 1 2 3 4 1 Our prices are always low 2 We do not offer frequent discounts 3 We do not review prices frequently 4 We offer lower prices than our competitors 5 We reach out to our customers for feed back 6 Our existing customers are always buying new products 7 Our supermarket branding attracts new customers 8 Our prices are always in line with customer preference 9 We always compare our prices with the competitors’ prices 10 Others Others specify below None High-Low pricing strategy Everyday Low Pricing Strategy 66 10. What is your reaction to High-Low pricing strategy? Please indicate with a tick the extent to which you agree with the following statements concerning the extent of adoption of High-Low pricing in your supermarket. (1 = strongly disagree; 2 = disagree; 3 = agree; 4 = strongly agree). STATEMENT SCALE 1 2 3 4 1 We frequently run promotional campaigns for all our products 2 We always run promotions to increase store awareness 3 We normally run promotions on slow moving products 4 We regularly run promotions for new products 5 We frequently offer price discounts 6 We are quick to revise our prices when need be 7 We regularly give quantity discounts 8 We offer different price ranges for selected products to attract variety of customers 9 We frequently give promotions during holiday seasons 10 Others 67 Section C: Pricing strategies and their influence on consumer purchase decision 1. The influence of Everyday Low pricing strategy on consumer purchase decision Please indicate with a tick the extent to which you agree with the following statements concerning the influence of Everyday Low pricing strategy on consumer purchase decision. (1 = strongly disagree; 2 = disagree; 3 = agree; 4 = strongly agree). STATEMENT SCALE 1 2 3 4 1 My brand choice is not influenced by promotional offers 2 I will buy a brand I don’t recognize simply because it is cheap 3 All the brands in my shopping bucket are always have low prices 4 I prefer brands which have a steady low prices 5 I choose my supermarket based on their low prices 6 My choice of supermarket is not influenced by promotional campaigns 7 I choose my supermarket based on stability of their prices 8 I am not likely to switch my supermarket because I like its low prices 9 I always have a large shopping bucket when the prices are low 10 I always have a small shopping basket whenever the prices are high 11 I only buy goods which are cheap from any particular supermarket 12 My shopping basket in not influenced by the price 13 I am willing to spend more time whenever I visit a supermarket with generally low prices 68 STATEMENT SCALE 1 2 3 4 14 I will shop at any time of the month because the prices are stable 15 My shopping pattern is not influenced by the prices at the supermarket 16 The supermarket is always full whenever am shopping 17 Any Other (please specify) 2. The influence of High-Low pricing strategy on consumer purchase decision Please indicate with a tick the extent to which you agree with the following statements concerning the influence of High-Low pricing strategy on consumer purchase decision (1 = strongly disagree; 2 = disagree; 3 = somewhat agree; 4 = agree; 5 = strongly agree). STATEMENT SCALE 1 2 3 4 5 1 I am likely to buy something I do not need when it is on offer 2 I will not buy a superior brand if it is not on offer 3 I will buy a new product if it is on offer 4 If a product is consistently on offer I will perceive it as of low quality and therefore will not buy it 5 I choose a supermarket based on the discounts it offers 6 I am likely to shop in a supermarket that offers frequent promotional campaigns 7 I will not shop in a supermarket that does not offer discounts 69 STATEMENT SCALE 1 2 3 4 5 8 I am likely to shop in a supermarket that frequently reviews its prices 9 If prices rise I am likely to buy in small quantities 10 I buy in large quantities whenever there are discounts 11 My shopping basket is influenced by the prices at the supermarket 12 I will only buy products that have a price discount 13 I am always willing to spend more time whenever I visit a supermarket with frequent offers 14 I am likely to suspend my shopping until there are promotional campaigns in the supermarket 15 Whenever there are promotions the supermarket is always full 16 My shopping pattern is influenced by the prices the supermarket offers 17 Any Other (please specify) 70 APPENDIX III: LIST OF SUPERMARKETS Table I: Target population Tuskys supermarket branches in Nairobi County Name Name 1 Tuskys EMBAKASI Branch 13 Tuskys IMARA Branch 2 Tuskys UTHIRU Branch 14 Tuskys ADAMS Branch 3 Tuskys GREENSPAN Branch 15 Tuskys ONGATA Branch 4 Tuskys KARASHA Branch 16 Tuskys HAKATI Branch 5 Tuskys CITY STADIUM Branch 17 Tuskys TOM MBOYA Branch 6 Tuskys ONGATA Branch 18 Tuskys CHAP CBD Branch 7 Tuskys TMALL Branch 19 Tuskys TOM MBOYA Branch 8 Tuskys NORTH VIEW Branch 20 Tuskys PIONEER Branch 9 Tuskys OLTALET Branch 21 Tuskys OTC Branch 10 Tuskys EASTLANDS Branch 22 Tuskys BEBA BEBA Branch 11 Tuskys CITY STADIUM Branch 23 Tuskys PIONEER Branch 12 Tuskys ONGATA Branch Source: Tuskys branches accessed on 20/01/2017 Nakumatt supermarket branches in Nairobi County Name Name 1 Nakumatt Express Wendani 10 Nakumatt Thika Road Mall (TRM) 2 Nakumatt Ronald Ngala 11 Nakumatt Junction 3 Nakumatt Moi Avenue 12 Nakumatt Karen 4 Nakumatt Mega 13 Nakumatt Prestige/Ngong Road 5 Nakumatt Lifestyle 14 Nakumatt Haile Sellasie 6 Nakumatt Ukay 15 Nakumatt City Hall 7 Nakumatt Westgate 16 Nakumatt Village Market 8 Nakumatt Galleria Mall 17 Nakumatt Embakasi 9 Nakumatt Ridgeways 18 Tuskys OTC Branch Source: Nakumatt branches accessed on 20/01/2017 Uchumi supermarket branches in Nairobi County Name Name 1 Uchumi Adams Arcade 5 Uchumi Jogoo Road 2 Uchumi Buruburu 6 Uchumi Nairobi West 3 Uchumi Jipange 7 Uchumi oinange branch 4 Uchumi Ongata Rongai 8 Uchumi Westlands Source: Uchumi branches accessed on 20/01/2017 71 Ukwala supermarket branches in Nairobi County Name 1 Ukwala Tom Mboya 2 Ukwal JJ Haile Selassie 3 Ukwala Mega Source: https://en.wikipedia.org/wiki/Ukwala_Supermarkets accessed 20/01/2017