Gupta Dave Editors: Anshu Gupta Kartik Dave

Gupta Dave Editors

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ISBN : 9781786354105

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Editors: Anshu Gupta Kartik Dave

Retail Marketing in India Trends and Future Insights

Retail Marketing in India Trends and Future Insights Editors Anshu Gupta Kartik Dave

Emerald Group Publishing (India) Private Limited New Delhi Emerald Offices Bingley, Cambridge, Sao Paulo, Johannesburg, Dubai, New Delhi, Beijing, Kuala Lumpur, Melbourne

Emerald Group Publishing (India) Private Limited 1001-1004, 10th Floor, Hemkunt Towers, 6, Rajendra Place, New Delhi - 110008 Title: Retail Marketing in India: Trends and Future Insights Copyright © 2016 Ambedkar University Contact: Anshu Gupta ([email protected])

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Editor’s Profile Anshu Gupta is presently associated with School of Business, Public Policy and Social Entrepreneurship, Ambedkar University Delhi (AUD), India as Assistant Professor in the Operations and Decision Sciences area. She obtained her Ph.D., M.Phil. and M.Sc. degrees in Operational Research (OR) from University of Delhi (DU), India. Her research in Ph.D. was in the domain of mathematical modeling and optimization in the field of Marketing and Software Reliability. She has served as Assistant Professor (Adhoc) and Research Fellow at Department of OR, DU before joining AUD. She was gold medalist in the master degree, 2003 and received Young Author of the Year award by the society for reliability engineering, quality and operations management (SREQOM), 2009. She has published several papers in refereed national and international journals of repute. He has co-authored a book Software Reliability Assessment with OR Applications, published by Springer, 2011 and a chapter in Wiley Encyclopedia of Operations Research and Management Science. Her teaching interests include quantitative methods, decision sciences, operations management, supply chain management and total quality management. She is currently pursuing research in the domain of innovation diffusion modeling, optimization for promotion planning, media planning models, supply chain optimization and quality management (six sigma methodologies). Kartik Dave presently is Dean (Officiating) and Associate Professor at School of Business, Public Policy and Social Entrepreneurship, Ambedkar University Delhi, India. He is Management Graduate and Doctorate in Marketing Management from M L S University, Udaipur. He brings in-rich experience of 5 years in industry and around 13 years in academics. He served as visiting faculty at Rouen Business School, France and ETEA, Spain. He has developed new courses like Challenges in Marketing in Emerging Markets, Services Excellence etc. His areas of teaching are Services Marketing, Marketing Management, Marketing Strategy, Retail Marketing, Branding, Social Media Marketing etc. He has been guiding Ph.D. Students in the area of Marketing, Services Marketing, Branding, Luxury Marketing, Retail, Human dimension in Marketing, Skills and Inclusive Marketing. Dr. Kartik Dave has published many research papers, cases and articles in academic journals and newspapers. He has been writing in International journals of repute published by Wiley, Inder Science Publications, Emerald, Springer Gabler, Science Direct etc.

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PREFACE Retail Industry in India has been experiencing unprecedented changes for the last couple of decades. It is projected to reach $1 trillion from the current $600 billion (BCG Report 2015). At the same time, modern retail is also expected to grow three fold to $180 billion from the present $60 billion. Indian consumers are evolving with greater brand recognition, rising income, urbanization, urge for exciting buying experience, and better environment. On the other hand, retailers are also striving for differentiated formats, greater supply chain efficiency and optimization, leveraging e-commerce and internet technology, mobile technology, and data analytics to gain sustainable advantage. E-commerce offers greater convenience, broad assortment, superior value proposition, and higher discounts to customers. However, there are plenty of challenges before retailers to match the expectations of their customer such as managing customer experience through omni channel strategies, supply chain, and infrastructure. Further, talent management, implementing technology, and costing are paramount for survival and growth of the industry. The Indian Retail Conference, 2016 was organized by School of Business, Public Policy and Social Entrepreneurship (SBPPSE), Ambedkar University Delhi, India during February 26– 27, 2016. The conference spanning over two days had the theme: “Indian retail: Will it Strive or Thrive?” It provided the participants a platform to deliberate challenges, opportunities, and future of retail in India. The conference proved to be a great success with about 200 distinguished participants from industry and academia including research scholars and graduate students. The conference provided ample opportunity to the participants to take part in fruitful discussions and intellectual exchange that contributed to the success of the conference. The conference received more than 80 abstracts for presentation on four broad themes: 1. Retail Consumer. 2. Retail Marketing. 3. E-Tailing and Retail Strategy, and 4. Retail Operations and Supply Chain. Forty-one papers selected through a blind review process were presented by research scholars in four parallel sessions. Apart from the research presentations, keynote addresses, invited talks, and panel discussions on current issues in retail management by eminent professors and leading industry practitioners were highly appreciated by the participants. Ample opportunity for networking was also provided to the participants. All papers presented at the conference were first reviewed by the conference editorial board and selected parers are then sent for blind review to the eminent experts in the area. Eleven papers selected from the two step review process have been published in this conference proceeding. Eminent professors who accepted our invitation to review the papers provided feedback to the researchers to improve their research findings as well as manuscript presentation. Our contributors showed positive response to the suggested revisions. The sincere efforts made by both reviewers and contributors have made it possible to bring quality research output into this publication. The proceedings from the conference provide an opportunity to the readers to read a good selection of refereed papers that were presented at the conference. The research papers presented in the conference were both theoretical as well as empirical. Mostly empirical papers passed the review process. The study by Saha et al. from the track retail strategy investigates the effect on customer preferences and brick-and-mortar market because of price discount offered by the e-commerce firms on Information and Technology (IT) products. The authors

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conducted survey of customer and sellers in the Nehru Place, New Delhi (Asia’s largest electronic and IT goods market). Results are drawn based on Chi-square Automatic Interaction Detection (CHAID) decision tree analysis. The study finds evidence of significant shift from brick-and-mortar seller to online platforms with high pricechoice sensitivity among consumers. The empirical study from the track retail customer by Sharma and Kesharwani presents a framework to explore store loyalty by segmenting green consumer. It tests the relationship between sociodemographic and psychographic variables and store loyalty of green consumers. Findings of the study identify the consumer’s dimension that may influence by greentailing to generate store loyalty. The study by Das elaborates the strategic price behavior of the large capital retail traders in fixing prices of agricultural food items. The study also throws light and identifies how small capital retail traders can compete in the market in the presence of large capital retail traders. Four papers examine the different factors that effect of the customer preferences for organized retail segment for different product categories or geographical locations. The study by Rathee and Rajain attempts to identify the factors that influence the consumer preference for organized retailing in NCR and Haryana region empirically. The findings suggest three factors namely service quality, variety, and supplementary services are the key determinant of the consumer preference for organized retailing. On the other hand, the research conducted by Sharma examines the influence of nature of product and frequency of visits on store loyalty in some cities of Uttarakhand. The study by Garg et al. conducted in Delhi–NCR region using survey method determines the effect of price and factors related to store and category characteristics on the purchase behavior of fruits and vegetables products in organized retail segment. Another empirical work by Mullick analyze the shopping experience at retail centres in NCR–Delhi using the survey method. The findings of the study bring out the important elements of better mall management. The study by Aggarwal et al. discusses the importance of online advertising in the retail advertising mix. The author identifies the requirement for developing a model that can help the web publisher in optimizing their revenue from advertisement taking care of the time preference and other requirement of the advertisers. A model is proposed in the study to determine the optimal placement schedule of advertisements for online portals considering advertisers’ time window, location, and frequency preferences and also illustrated with a case study One of the primary concerns of the retail industry is sustainable growth. It generates the requirement from the supply chain members to rethink on their goals and redesign their strategies. Two papers presented in the track Retail Operations and Supply Chain focus on the sustainability aspect of the retail supply chains. The study by Darbari and Agarwal delves into literature to identify key dominant areas for improving sustainability of Indian electronic retail supply chain. Eleven enablers of sustainability are identified and using Interpretive Structural Modeling (ISM) technique a structural hierarchy is extracted to understand the association between the suggested enablers and their level of impact on the environment and society. The study by Gandhi et al. presents an integrated supplier selection, procurement, inventory control and transportation model, and case study that helps in evaluating the suppliers, determining optimum quantity to procure, choosing transportation vehicle type along with managing environmental issues, obtaining optimal stock keeping units (SKU)

and safety stock for each product category to fulfil a specified service level for retailers with a cost minimization objective. The study by Gupta and Bhatt presents a method based on Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for performance assessment and ranking of 52 countries in retail trade using retail sales data for the year 2011 under different grocery and non-grocery categories. The success of conference is a reflection of the support and dedication of the various concerns. We are extremely thankful to Ambedkar University Delhi and our co-sponsor Konica Minolta Business Solutions India Pvt. Ltd. for the generous financial support. We are grateful to our distinguished guest speakers and panellists who came all the way from different parts of the country to deliver inspiring talks and discussions. We would like to thank all participants for their contributions to the conference program and the proceedings. We also acknowledge the unwavering support received from the faculty, staff members and students of the school in organizing the conference. Our special thanks to the reviewers of the proceedings for providing valuable feedbacks in time and help towards the improvement of quality of papers presented in the conference. We hope this volume will add value to the existing literature and will be helpful to the practicing managers and leaders. Anshu Gupta and Kartik Dave

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Contents Preface 1. Competing with Virtual Entities: IT Product Purchases in India

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Naushadul Haque Mullick

8. Optimal Advertisement Planning on Web considering Time Window Concept

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Srishty Garg, Shreshth Goyal, Chanakya Purohit, Shaily Wadhwa, Pankaj Priya

7. A Study of Shopping Experience in Selected Retail Centres in NCR

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Deependra Sharma

6. Purchasing Fruits and Vegetables: Role of Price and Store Characteristics

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Rupa Rathee, Pallavi Rajain

5. Influence of Nature of Product and Frequency of Visit on Store Loyalty: A Study of Uttarakhand

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Dipankar Das

4. Factors Influencing Consumer Preferences towards Organized Retailing in NCR and Haryana

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Nitika Sharma, Subodh K Kesharwani

3. Quality Attribute and Non-linear Pricing under Changing AgricultureFood Retailing: A Study in India

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Debdatta Saha, Ram Narayan Shrestha, T M Vasuprada

2. Greentailing and Green Consumer Profile: Retailers’ Strategies for Store Loyalty

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Sugandha Aggarwal, Arshia Kaul, Anshu Gupta, Prakash Chandra Jha

9. An Interpretive Structural Modelling Approach for Analysing the Enablers towards Adoption of Initiatives for a Sustainable Supply Chain 103

Jyoti Dhingra Darbari, Vernika Agarwal

10. A Fuzzy Multi-Criteria Optimization Model for Allocating SKU and Suppliers in SC System

Kanika Gandhi, Kirit Goyal, Abhinav Jha

11. Performance Assessment of 52 Countries Across the Globe in Retail Sector and Ranking based on TOPSIS

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Seema Gupta, Chandra Prakash Bhatt

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Competing with Virtual Entities: IT Product Purchases in India Debdatta Saha1*, Ram Narayan Shrestha2, T M Vasuprada3

Assistant Professor, Faculty of Economics, South Asian University, New Delhi Research Scholar, Faculty of Economics, South Asian University, New Delhi

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2,3

Abstract This paper investigates the impact of recent price discounts offered by e-commerce entities such as Flipkart on consumer preferences and on wholesale brick-and-mortar sellers for Information and Technology (IT) products, specifically computers, laptops and related accessories. Our results are based on a consumer survey in Delhi and a supply-side survey of sellers at Asia’s largest electronic and IT goods market for this product category (Nehru Place market, New Delhi). The Chi-square Automatic Interaction Detection (CHAID) decision tree analysis is used to understand the determinants of consumers’ preferred shopping destination. We find evidence of a significant shift away from brick-and-mortar shops in favor of online purchases with high price-choice sensitivity among consumers. Business strategies that traditional shops are following to counter the online price challenge are investigated. It is found that diversification of the product basket and price risk, rather than seller experience, is a robust strategy to counter Bertrand price competition in retail.

Keywords Bertrand Price Competition in Retail, Consumer Decision-making process, Chi-square Automatic Interaction Detection (CHAID), Seller’s Risk Perception, Durable Goods Purchase

1. Introduction and Objectives In recent times, the biggest Schumpetarian upheaval in the Indian retail space has been the explosion of e-commerce, with tempting choices and unbelievable prices across an unimaginably large spectrum of consumer products (groceries, baby diapers, computers, laptops, mobile phones, etc.). Although the present volume of e-commerce is miniscule relative to total retail sales in India, the recent growth surge of e-commerce is impressive with Credit Rating and Information Services of India Limited (CRISIL), estimating a Compound Annual Growth Rate (CAGR) of 50–55% for the next three years. In this paper, we study the effect on consumer preferences and offline business strategies due to competition by e-commerce in the sales of computers, laptops and accessories. The choice of the offline market place in this paper, namely Nehru Place, is driven by the fact that it is Asia’s largest electronic goods marketa. The product category of IT goods is durable in nature, which is the desired product attribute for which we intended our study. * Corresponding Author: Debdatta Saha ([email protected]) a This is mentioned in Sundaram (2004) as well as e-resources such as http://india24.xyz/ nehru-place-asias-biggest-computer-electronics-gadget-market/, http://www.nehruplace.net. in/articles/nehru-place-asia-biggest-it-market.htm

Retail Marketing in India: Trends and Future Insights pp. 1–15 © Ambedkar University

Retail Marketing in India: Trends and Future Insights

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In terms of novelty of our results, we find significant price sensitivity among consumers for durable goods (mostly in line with the existing literature). Our paper is unique as it integrates the demand side with the supply side, which is rare in the empirical literature. We highlight the fragility of the nature of Bertrand price competition in the market, as we find that experience matters less than price-risk diversification for offline retail. This, coupled with the dominance of price-choice for online preference,b raises questions about the limits of e-commerce. Entrepreneurship development through e-commerce is a policy thrust in India now. Our results underscore significant qualifiers for the potential of e-commerce as an engine of growth. Additionally, a methodological contribution of our paper is the application of non-parametric technique of decision tree analysis (CHAID).

2. Research Question This paper investigates the following research questions: i. What is the impact of the aggressive marketing strategies by e-tailers on consumers’ preferences in terms of shopping destination for consumer durables in the category of computers, laptops and accessories? ii. What is the impact of the same, if any, on the brick-and-mortar sellers of such durables at the Nehru Place market?

3. Conceptual Framework and Related Literature 3.1 Literature Review Table 1 provides a summary of relevant literature (mostly USA), whereas Table 2 compares our results with relevant papers from India.

3.2 Impact on Consumers Our results are based on a consumer survey conducted in May–June 2015 in Delhi, through direct interviews. The sample is drawn from the population randomly, consisting of University students, working professionals residing in the NCR region, working professionals at Nehru Place, self-employed consumers and some nonresidents of Delhi. There is heterogeneity in consumers’ familiarity with Delhi’s markets, as well as in their online purchasing behaviour in the selected sample. Out of an initial sample size of 132, our survey resulted in 130 valid observations (due to non-responses for some questions). We studied the choice of preferred location of purchasing laptops and computers as well as accessories by asking consumers to rank their preferences of shopping destination in four categories: Category 1: Nehru Place (excluding dedicated dealers within the market) Category 2: Dedicated dealersc (within and outside Nehru Place) b Our significant and surprising contribution is that neither shopping ease nor price discovery is important for online preferences in shopping behaviour. c We treat dedicated dealers as a separate category, as these shops do not have the pricesetting flexibility like traders at Nehru Place. These shops have the branding power of the brand they sell (HP, Asus, etc.) but are limited by the pricing policy of the brand they represent. After-sales services are a part of the sales strategy of these shops. Some of our respondents specifically mention that they prefer going to dedicated dealers at Nehru Place.

Category 3: Retailer near to consumer’s home other than Nehru Place Category 4: Online A comment about the way we have modelled the Nehru Place market is due. Located in South Delhi, this commercial centre has been functional since the early 1980s. We perceive it as the wholesale purchase destination in Delhi for the product category of our interest, with traders offering best prices and choices for a variety of IT products. However, this market houses many other establishments. Hence, our analysis provides a partial characterization of the entire Nehru Place market.

3.3 Methodology for Consumer Behaviour Characterization Using a non-parametric regression technique CHAID, we attempted to understand the possible reasons for the choice of preferred shopping destination for laptops and computers for the entire distribution of consumers. CHAID is a recursive partitioning method developed by Kass (1980), which uses Chi-square tables to branch the decision-making process revealed by the data into initial nodes and subsequent nodes (in decreasing levels of significance). In our case, CHAID analysis visually aids the understanding of the decision-making process of all consumers in terms of variables affecting the decision in decreasing order of importance, where the significance of the variables is measured by the successive Chi-square values at each node. Also, the independent variable with the smallest p-value is the variable which appears first after the dependent variable. The initial node lists the preference of shopping destination of all the consumers which is our dependent variable. The next level of the decision tree also known as the “parent node” begins with the most significant variables affecting choice of destination. Also, the clubbing of the categories of the independent variable happens according to the pair of least significantly different categories with respect to the dependent variable. That is, if a pair of categories for the independent variable is not statistically significant, the two categories will be merged together in the branching process. The following layer of the tree is the second most important to influence variables in shopping destination choice and is also the “terminal node” in our analysis. The tree is formed in this manner, with the least important variables forming its terminal branches. For calculations, we have stopped the branching process of the tree whenever the total number of observations on any branch falls short of 10 observations. As a non-parametric method, CHAID has some distinct advantages over other methods when it comes to modelling choice of consumers. In the first place, most marketing research surveys (as well as in this study) result in ordinal or categorical variables that rule out traditional regression methods. Second, among non-parametric techniques such as kernel regression, non-parametric multiplicative regression and neural networks, CHAID is most suitable for understanding the hierarchical nature of the decision process. Apart from CHAID, there are other decision tree algorithms such as Classification and Regression Trees (CART; Breiman et al., 1984), Automatic Interaction Detection (AID; Morgan and Sonquist, 1963) and Theta Automatic Interaction Detection (THAID). Perreault and Barksdale (1980) note the superiority of CHAID over AID and THAID as it cross-tabulates all variables of interest and is based on data theory (like alternating least squares optimal scaling). Additionally, the splits created by CHAID are not by peculiarities of the sampling procedure. Recent applications of CHAID in direct marketing include the works of Haughton and Oulabi (1993) and McCarty and Hastak (2007). The latter study finds that CHAID outperforms other techniques such as

Competing with Virtual Entities: IT Product Purchases in India

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Market

USA; Computers

USA; Books and CDs

No Specific Market

USA; Books

USA; Personal computers

USA; Books, Electronics and Music

Authors

Goolsbee (2001)

BiynjoliSs-on and Smith (2000)

Balasubramanian (1998)

Forman et al. (2009)

Prince (2007)

Nelson et al. (2007)

Theoretical: Framework based on Salop (1979)

Empirical: Hypothesis testing using t-tests

Empirical: Hedonic price regressions; logit estimation

Methodology

Empirical: Probit estimation Empirical: Multivariate regession

Retail computer prices (Online and offline) Price dispersion (online alone)

Relative price of Empirical: online vs. offline Differencein- Difference regression

Retail prices (offline alone)

Prices of selected books and CDs (online and offline)

Retail computer prices (online and offline "remote")

Variable(s)

Price dispersion is positively related to price of products and number of sellers

Supply-side factors ((i). expansion of PC firms in 1997 (ii).customization for high end buyers by online retail) alone explain switch from offline to online

Increased access to offline stores cause shifts from online to physical retailers

With enough information about sellers direct marketing is a viable channel; direct marketer retains competitive advantage by controlling information to consumers

Results depend on time period (199698); may not be replicable in India

Assumption of high online disutility costs unrealistic for India at present

Results heavily dependent on model assumptions

Broad based study with Use of single price many product categories point online for calculating price dispersion

Integration of both supply and demand side factors

Rare study relating location of offline stores with likelihood of online purchases

Simple application of Salop (1979) to study competition between direct versus conventional retail

Product base very narrow; results may not generalize to entire industry, particularly India

Simple approach to Online prices lower than offline with smaller price adjustments; online price analyse price dispersion across channels dispersion sensitive to price definition (posted versus weighted price); online channel increases retailer heterogeneity more than offline

Drawbacks Online purchase estimates mixed up with remote computer purchases

Strengths Captures price sensitivity of buyers across channels by using more than 20,000 observations

Probability of buying offline remotely vs. online significantly depend on relative prices

Central Result

Table 1: Summary of Papers Comparing Online vs. Offline Purchases Worldwide


Market

Subjective analysis, nonrepresentative of India

Life-style based segmentation of the consumers

Price does not determine online preference [Result in contrast with our paper]

Empirical: Cluster and Factor analysis; Chi-square tests; Cronbach's alpha

General online shopping

Gehrt et al. (2012)

Authors

Drawbacks

Strengths

Central Result and Comparison

Methodology

Market (India)

Authors Khare et al. (2012)


Empirical; Exploratory factor analysis; Regression; ANOVA; Cronbach's alpha

Young consumers prefer online (ease of use); men are more likely to shop online; ease of use determines online usage [Age result in line with our paper; not gender]

Demographic and social factors included in analysis

Severe sample selection bias (Mall intercept technique)

Variable(s)

Beldona et al. (2011)

Airlines

Empirical; Descriptive statistics; Multivariate regression

Older consumers prefer to purchase airline tickets through offline mode [Result in line with our paper]

Consumers' search as well as cognitive costs included

Data is limited to two cities with a small sample size

Methodology

Gupta et al. (2008)


Empirical; Descriptive analysis; Pearson correlation

Young Indian adults mostly surf rather than purchase online [Results partly similar to our paper]

Holistic analysis of online shopping (surfing and actual purchases)

Severe sample selection bias (75 % students without purchasing power)

Central Result

Pandey et al. (2015)


Empirical: Factor and Cluster analysis; Chi-square tests; Cronbach's alpha

Middle-aged Indians (offer enthusiasts) mostly shop online [Results partly similar to our paper]

Life-style based segmentation of the consumers

Results too general, may not hold for all market segments

Strengths

Table 2: Summary of relevant Indian studies

Khare et al. (2010)


Empirical: Multivariate regression analysis; Cronbach's alpha

Young Indians mostly purchase online using cash as a preferred mode of payment [Result in line with our paper]

Captures noveltyseeking behaviour of consumers effectively

Nonrepresentative of Indian youth (includes a narrow subset)

Drawbacks

Khare (2016)


Empirical: Factor and regression analysis; Cronbach's alpha

Brand and quality-conscious shoppers unlikely to purchase online [Result indirectly in line with our paper]

Consumer Survey Inventory (CSI) innovatively tested for India

Severe sample selection bias (Mall intercept technique)


Recency, Frequency and Monetary Value (RFM) when the response rate to a mailing is low. Nong (2003) mentions an application of CHAID for partitioning Internet users into homogenous sub-groups for targeted advertising. Ritschard (2010) discusses the CHAID decision tree analysis as an improvement upon the earlier tree methods.

3.4 Results on Consumer Purchase Behaviour

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Around 42% of the 130 consumers ranked Nehru Place as their first preference and this is statistically significant. The reason for market preference was measured in the survey through: I. Preference for features of various aspect of the purchase (variable feature_pref, see Figure 1), which was coded in seven categories: Category 1: Better price Category 2: Better choice Category 3: Ease of travel to the shop Category 4: After sales service Category 5: Possibility of discount on repeat purchase Category 6: Possibility of resale of the product Category 7: Any other reason Categories 1 and 5 are price-related reasons for purchase. Whereas category 1 deals with price of immediate purchase, category 5 concerns price of repeat purchases. Most responses for category 7 were concerned with quality and other choice-related variables. II. Demographic variables such as the age of the respondent, gender, own transport ownership and number of years of stay in Delhi. III. Behavioural variables such as familiarity with Delhi markets (categorized in three categories of low familiarity, moderate familiarity and extreme familiarity) and online shopping frequency (categorized as infrequent (once a year), moderate (once a quarter) and very frequent (monthly)). The non-parametric regression (see Figure 1) shows that the most important variable affecting the distribution of market preference (variable market_pref) is the first category of decision variables feature_pref, with a statistically significant Chisquare value of 39.00 and adjusted p-value of 0.00. The feature_pref variable in data is trifurcated as follows: categories 1 and 5 (which together reflect the price sensitivity of the consumers) with a total of 46 observations, categories 2 and 7 (which together reflect the choice-related variables of purchase) with 45 observations and categories 3 and 4 (which are related to logistical issues with purchase) with the remaining 39 of the total sample of 130. Category 6, which is resale possibilities, does not influence market preference in our sample. Thus, all pairs of categories which are least significantly different with respect to variable market_pref have been clubbed together. We find that this clubbing of categories also has a legitimate economic interpretation. Both the price sensitivity branch and the choice-related branch of the feature_pref variable show Nehru Place as the significant first choice of consumers (58.7% and 46.7%, respectively). The logistical issues branch, on the other hand, shows dedicated dealers as the significant first preference for consumers, with 46.2% observations of a total of 39 along this branch). Consumers who consider after-sales service and ease of travel as important decision variables rank dedicated dealers higher than the

other three categories. This is presumably due to the trust placed on dedicated dealers for after-sales servicing and logistical difficulties of parking private vehicles at the congested Nehru Place. Recently, a multi-level parking has been built in the market. However, the parking charges are very steep.


7 Feature_pref

Age

Figure 1: CHAID Output for the Entire Sample of 130 Consumers Key: Category 1. Nehru Place; Category 2. Dedicated dealers; Category 3. Nearest local retailers; Category 4. Online

The decision tree has two more branches, one beginning at Node 2 (choice related categories) and the other at Node 5 (logistical issues categories). After controlling for choices (45 observations), the only demographic variable which is statistically significant is age, with an adjusted p-value of 0.032. Nodes 4, 5 and 6 divide the age variable into three classes (less than 21 years: young and inexperienced; 7 observations), between 21 and 22 (relatively older and more experienced; 6 observations) and greater than 22 (older and experienced; remaining 32 observations of the total of 45). Here, the distribution of market preference shows an interesting trifurcation. For the young and inexperienced (with 71.4% of the total number of consumers at the node), online is the first preference, the relatively more experienced age group between 21 and 22 years rank dedicated dealers as their first preference, whereas the older and most experienced (greater than 22) rank Nehru Place as their first preference. Young and inexperienced first purchasers in our data experiment significantly more with online purchases. This observation highlights the demographic that is fuelling the recent online purchasing frenzy. Older consumers are more wary of


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online purchases. Many consumers in the higher age cohorts mentioned that even if they purchase IT products online, they visit Nehru Place once for verifying the product description as advertised online. The clustering of age within a gap of one year at 21–22 is potentially explained by the fact that this is the age for beginning university education and consumers are risk-averse about experimenting with laptops/computers and accessories (as incomes are very low: either parental endowment or scholarships). For an age higher than 22, consumers have presumably gathered more experience than the younger cohorts and are more confident about the offline wholesale market place. After controlling for logistical issues (Node 5), the only demographic variable which statistically significantly influences market preference is own transport, with an adjusted p-value of 0.046. This variable is categorized into two (19 observations with own transport and remaining 20 of the 39 observations without own transport). In either of the cases, dedicated dealers are the first preference (52.6 and 40.0% of the total number of observations, respectively). As discussed earlier with logistical issues of purchase, our data is indicative of poor civic facilities at the Nehru Place market. Accessibility of the market has improved in the last two years, with a metro station on the opposite side of the market. However, this aids access of consumers who use public transport rather than private vehicle owners. Hence, there is no effect of own transport in the preference ranking of shopping destination. The data is naturally bifurcated into two partsd: I. Historical consumers: Consumers (a total of 75) who actually purchased a laptop/computer in the last two years. II. Potential consumers: Consumers (a total of 53) who did not purchase in the last two years, but might purchase in the future. Figure 2 shows the non-parametric output for historical consumers. The dominance of Nehru Place is higher for this category of consumers (with 52% of total 75 consumers ranking it first at Node 0). The reason for this preference is explained by the next branch of the decision tree with only the feature_pref variable being statistically significant. The demographic and behavioural variables are insignificant for historical consumer behaviour. The branching of the feature-pref variable is different from the overall result for all consumers. Here, at Node 1, categories 1, 2 and 5 are merged together (price, choice and discount on repeat purchase). Node 2 represents only category 4 (after-sales service) and Node 3 (ease of travel and other reasons). The risk estimate here is 0.413 with a standard error of 0.057. For consumers who made actual purchases during the last two years, Node 1 ranks Nehru Place as the significantly first preference (with 68.1% of total consumers at the node). These consumers treated price, choice and discount on repeat purchase as a composite category in making this ranking decision. Node 2 ranks dedicated dealers as the first preference (60% of total observations at this node concentrated here). Consumers for whom after-sales service is the most important category in making a purchase have made this preference ranking. Node 3 (ease of travel and other reasons) have an even split between Nehru Place and nearest retail shop (each with 33% of the total observations). d We lost two observations in the process of bifurcating the entire consumer sample of 130. The risk estimate is 0.438 with a standard error of 0.044.


9

Figure 2: CHAID Output for 75 Historical Consumers Key: Category 1. Nehru Place; Category 2. Dedicated dealers; Category 3. Nearest local retailers; Category 4. Online

The results indicate that for experienced historical consumers, Nehru Place is the best shopping destination for computers from a price-choice point of view. On the other hand, if after-sales servicing is dominant category for making a purchase decision, dedicated dealers are the best bet. This preference ranking in favor of Nehru Place, which is much more marked for historical consumers than in the overall scenario with all 130 consumers, undergoes a significant transition for the potential customer’s category. Figure 3 shows the CHAID output for the 53 potential consumers, 39.6% of whom rank Nehru Place as their first preference. Feature-pref alone is statistically significant in explaining this ranking, with categories 3 and 4 (ease of travel and after-sales service) clubbed in one node (Node 1) and categories 1, 2, 5 and 7 bunched in Node 2 (price and choice combined variable) clubbed in another category. Node 1 expectedly ranks dedicated dealerships as the first preference, as after-sales services is a dominant category here. On the other hand, Node 2 ranks Nehru Place at the top. The risk estimate is 0.528 with a standard error of 0.069. Summarizing the results, potential consumers consider Nehru Place as the best destination for computer purchases along the price-choice dimension. Dedicated dealers are the best choice if after-sales services matter in decision-making. Notably, there is a shift away from Nehru Place in favor of online purchasing in our data. Though potential consumers rank Nehru Place as their first preference (39.6% observations), it is much reduced from the 52% majority ranking given by historical consumers. Only 18.7% of the historical consumers had an online preference, whereas 37.7% of the potential consumers foresee themselves as online consumers. That online pricing and choice basket has dented consumer preferences is corroborated by this shift factor. On the price front, there has been recent news about raging price wars between online giants like Flipkart and Amazon. We calculated some indirect


measures of price sensitivity to examine whether or not it is online pricing that has been a primary reason for this shift. We observe that price and choice together has been the most important purchase determinant in our sample, rather than price alone. For all the consumers in the data, the bundled variable of price–choice was ranked as the most significant variable for purchase by 58.8% of historical consumers and 57.4% of the potential consumers. Price alone is ranked as the most important decision variable by 33.6% of the overall consumers and 27.8% of the new consumers.

10

,

1 2

18.8 43.8

,

3 7

Figure 3: CHAID Output for 53 Potential Consumers Key: Category 1. Nehru Place; Category 2. Dedicated dealerships; Category 3. Nearest local retailers; Category 4. Online

For the combined price-choice variable, most consumers cluster their preferences either at Nehru Place or online (54.5% at Nehru Place and 37.7% online). While not a perfect measure of price elasticity, we can claim from our data that consumers are sensitive about prices and about the choice of brands available in making an IT product purchase decision. These price-choice sensitivity results are in line with existing academic research such as Maxwell (2001) and Gupta (2011).

4. Supply-side Analysis: Survey Results from Nehru Place Traditional businesses, over the last two years, have been adversely impacted due to Bertrand price competition with large online sellers, as reported by most of the retailers at Nehru Place. We interviewed and collected data from 44 establishments at Nehru Place. Non-response from sellers has limited our sample size. Our focus was primarily on the organized retail trade in computers.e This is representative of the entire market as the overall presence of unorganized retail is 5% of the total market. There is a significant amount of variation in terms of monthly sales, monthly rent and margins. Monthly sales e Only two of our respondents are from unorganized retail.

figures vary from 0.75 lakhs to 600 lakhs for these shops. Eight of the 17 shops revealed that their net monthly income shows a variation of 0.11 lakhs to 113.5 lakhs.


4.1 Discussion of Results for the Supply Side Hypothesis 1: Retail traders (non-dedicated dealers) in Nehru Place prefer to sell computer-related accessories rather than branded laptops and computers to survive the online price challenge. Table 3: Classification of 41 Shops According to Products Sold Category Computers only Mobiles only Repairs and Data Recovery Accessories and Spare Parts Both Computers and Mobiles Total

Frequency 12 5 4 13 7 41

11

% 29.27 12.20 9.76 31.71 17.07 100.00

Source: Authors’ own Calculations

Table 4: Cross-tabulation of Experience with Perceived Competition Experience

Perceived Competition Online Other Sellers in the Market Other Brands and Products No Perceived Competition Total

Very Moderate Mature New Experience Sellers 4 4 3 2 1 0 2 3 2 5 0 1 13 8 6

Very Experienced and Mature 5 1 1 2 9

Total 16 4 8 8 36

Pearson Chi2 (9) = 7.1394 Probability = 0.623 Fisher’s Exact p-value = 0.659 Source: Authors’ own calculations

Table 3 above shows that the majority (almost 32 per cent of the sample) are engaged in the business of selling accessories and spare parts, followed by about 30 per cent selling computers alone. Given that our survey is biased towards functional firms, the large concentration of accessory sellers lends partial credence to our hypothesis of business strategies to survive intense online competition. Many computer sellers in their interviews mention the strategy of diversification into accessories and spare parts business, as this faces less competition from online selling. In Table 4 seller’s experience is categorized as: very new (0 to 4 years), moderate experience (5 to 9 years), mature sellers (10 to 14 years) and very experienced sellers (more than 15 years of trading) and their sellers’ perceived threat of competition is


12

ranked in four categories, namely online competition, sellers in the market, other brands and products and no competition perceived. From Table 4 it can be inferred that 16 out of total 36 shops categorized by experience complain that e-competition has been the largest source of competition for them over the last two years. Some cellvalues in Table 4 contain less than 5 observations making a normal t-test invalid for inference. Hence, we used the Fisher’s Exact Test, under the null hypothesis that there is no statistically significant relationship between the source of competition and seller experience. The test values indicates that we cannot reject the null hypothesis. Note that we have lost some data points due to non-responses in all question categories in tables 3 and 4. Hypothesis 2: Years of trading experience does not matter for perception of competition. The degree of price setting freedom is categorized as: no freedom, moderate freedom and full freedom. Table 5 shows that there is a statistically significant relationship between sellers’ perception of competition and the degree of price setting freedom. Hypothesis 3: The degree of price setting freedom has an impact on the perception of likely competition among sellers at Nehru Place. Due to non-responses on price-setting freedom, our sample here is of only 32 business establishments, 14 of whom perceive e-commerce as their biggest competition. 12 of these 14 shops reported that they have full freedom to set their prices. Price risk is diversified more if the establishment has lower price setting freedom (dedicated dealerships). Hence, majority of the shops with an online threat perception have full price setting freedom Table 5: Cross-tabulation of Price Setting Freedom of Shops with Perceived Competition Perceived Competition Online Other Sellers in the Market Other Brands and Products No Perceived Competition Total

Full Freedom 12

Price Setting Freedom Moderate No Freedom Freedom 1 1

Total 14

1 0 3

0 2 0

3 5 4

4 7 7

16

3

13

32

Pearson Chi2 (6) = 18.6319 Probability = 0.005 Fisher’s Exact p-value= 0.000 Source: Authors’ own Calculations

Due to non-responses on price-setting freedom, our sample here is of only 32 business establishments, 14 of whom perceive e-commerce as their biggest competition. 12 of these 14 shops reported that they have full freedom to set their prices. Price risk is diversified more if the establishment has lower price setting freedom (dedicated dealers). Hence, majority of the shops with an online threat perception have full price setting freedom.

5. Conclusion In the recent times, e-commerce sites by offering massive discounts, free home delivery and other attractive offers such as buybacks, 30-day replacement, etc. have wrenched away a certain market share away from offline markets. This is clearly established through our consumer survey, showing a shift away from the wholesale brick-andmortar market at Nehru Place in favor of online buying, though the former continues to the most preferred destination for IT products among a large subset of our sample. The primary advantage for online purchasing over the offline market place for a consumer is that it minimizes travel and search costs. Consumers can buy a bundle of commodities (FMCG, durables, etc.) through a single search online. However, the trust that people place on experiencing a product can only be provided by offline market places. Interestingly, in our survey consumers’ choice of location of purchase is dictated by a combined price–choice variable and not convenience of shopping. Price discounts and product variety online is the driving force for this shift in consumer preferences. On the supply side, if a very experienced and mature trader has not diversified from selling standardized IT products, intense price competition is likely to hurt him/ her just as much as a new seller at Nehru Place. What provides further support to our claim is that the degree of price setting freedom that a trader has influences his perception of price competition from online traders. This clearly indicates that traders with diversified price risk will survive this competition and there will be exit of a large number of undiversified small traders from the traditional market.

6. Limitations Our sample on the supply side suffers from a survival bias. It does not pick up data on sellers who have exited the market already. The static nature of our survey cannot address this issue. Further, as a survival strategy, diversification of the product basket is very important for the brick-and-mortar shops. Due many non-responses from sellers, we could not empirically establish this result. Our offline market sample is from one wholesale market alone, though consumers reveal this market to be the most relevant for making IT product purchases in New Delhi.

7. Further Research To understand the total impact of e-commerce in the product category we are investigating, we have started further research on pricing and non-price promotional behaviour of e-tailers. We also extend our research to other offline market places to study the dynamic pattern of entry and exit to enrich the existing results.

8. Managerial Implications The advantage of traders in a offline marketplace like Nehru Place seems to lie in trading in “non-standardized” products with a high informational content. Nonstandardized products are brand-specific components and parts and for which the consumer does not have adequate know-how to order online. This helps traders retain market share, as standardized products with less informational requirement are sold online at discounted prices. A simple strategy of diversifying into the repairs business by existing computer dealers is going to be self-defeating, as our research indicates that consumers interested in repairs of computers prefer to go to dedicated dealers of the computer brand rather


13


than general repair shops at Nehru Place. Such a strategy will only attract the residual demand of consumers whose product warranty has either expired or who have made foreign purchases and faces invalid warranty in India.

References

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Balasubramanian, S. (1998). “Mail versus mall: A strategic analysis of competition between direct marketers and conventional retailers”, Marketing Science, 17(3): 181–95. Beldona, S., Racherla, P. and Mundhra, G.D. (2011). “To buy or not to buy: Indian consumers’ choice of online versus offline channels for air travel purchase”, Journal of Hospitality Marketing and Management, 20(8): 831–54. Breiman, L., Friedman, J., Stone, C.J. and Olshen, R.A. (1984). Classification and Regression Trees. Belmont, CA: Wadsworth International. Brynjolfsson, E. and Smith, M.D. (2000). “Frictionless commerce? A comparison of internet and conventional retailers”, Management Science, 46(4): 563–85. Forman, C., Ghose, A. and Goldfarb, A. (2009). “Competition between local and electronic markets: How the benefit of buying online depends on where you live”, Management Science, 55(1): 47–57. Gehrt, K.C., Rajan, M.N., Shainesh, G., Czerwinski, D. and O’Brien, M. (2012). “Emergence of online shopping in India: Shopping orientation segments”, International Journal of Retail and Distribution Management, 40(10): 742–58. Goolsbee, A. (2001). “Competition in the computer industry: Online versus retail”, Journal of Industrial Economics, 49(4): 487–99. Gupta, N. (2011). “Extent of susceptibility to interpersonal influence and price sensitivity among Indian youth: Is there a relationship between these two constructs?”, Young Consumers, Emerald Group Publishing Limited, 12(4): 361–69. Gupta, N., Handa, M. and Gupta, B. (2008). “Young adults of India-online surfers or online shoppers”, Journal of Internet Commerce, 7(4): 425–44. Haughton, D. and Oulabi, S. (1993). “Direct marketing modeling with CART and CHAID”, Journal of Direct Marketing, 7(3): 16–26. Kass, G.V. (1980). “An exploratory technique for investigating large quantities of categorical data”, Applied Statistics, 29(2): 119–27. Khare, A. (2016). “Consumer shopping styles and online shopping: An empirical study of Indian consumers”, Journal of Global Marketing, 29(1): 40–53. Khare, A., Khare, A. and Singh, S. (2012). “Attracting shoppers to shop online: Challenges and opportunities for the Indian retail sector”, Journal of Internet Commerce, 11(2): 161–85. Khare, A., Singh, S. and Khare, A. (2010). “Innovativeness/novelty-seeking behaviour as determinants of online shopping behaviour among Indian youth”, Journal of Internet Commerce, 9(3–4): 164–85. Maxwell, S. (2001). “An expanded price/brand effect model: A demonstration of heterogeneity in global consumption”, International Marketing Review, 18(3): 325–43. McCarty, J.A. and Hastak, M. (2007). “Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression”, Journal of Business Research, 60(6): 656–62. Morgan, J.N. and Sonquist, J.A. (1963). “Problems in the analysis of survey data, and a proposal”, Journal of the American Statistical Association, 58(302): 415–34.

Nelson, R.A., Cohen, R. and Rasmussen, F.R. (2007), “An analysis of pricing strategy and price dispersion on the internet”, Eastern Economic Journal, 33(1): 95–110. Nong, Y. (Ed.) (2003). The Handbook of Data Mining. Mahwah, NJ: Lawrence Erlbaum Associates. Pandey, S., Chawla, D. and Venkatesh, U. (2015). “Online shopper segmentation based on lifestyles: An exploratory study in India”, Journal of Internet Commerce, 14(1): 21–41. Perreault Jr, W.D. and Barksdale Jr, H.C. (1980). “A model-free approach for analysis of complex contingency data in survey research”, Journal of Marketing Research, 17 (4): 503–515. Prince, J.T. (2007). “The beginning of online/retail competition and its origins: An application to personal computers”, International Journal of Industrial Organization, 25(1): 139–56. Sundaram, R. (2004). “Uncanny Networks: Private, Urban and New Globalization”, Economic and Political Weekly, 39(1): 64–71. Ritschard, G. (2014). “CHAID and earlier supervized tree methods”, in McArdle, J.J. and Ritschard, G. (Eds.), Contemporary Issues in Exploratory Data Mining in the Behavioural Sciences. Routledge, New York and London (Taylor and Francis Group): 48–74.

Declaration The authors would like to acknowledge the contributions of Kumarjit Saha (Indian Statistical Institute, New Delhi) and the following M.A. Development Economics students (2014), Faculty of Economics, South Asian University, who helped in the surveys—From India: Shelly Gulati, Akanksha Batra, Anubrata Deka; From Pakistan: Prem Kumar and Ajmal; From Afghanistan: Md Mahdi Frough Abdurrahman Rahmani and Md Salim Samimy and Mr Swarn Singh at Nehru Place. The corresponding author wishes to thank the South Asian University for giving a grant under the Professional Development Allowance ( ` 30,000) for conducting the survey and the subsequent research.


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Greentailing and Green Consumer Profile: Retailers’ Strategies for Store Loyalty Nitika Sharma1*, Subodh K Kesharwani2

Research Scholar, Department of Commerce, University of Delhi, Delhi, 2 Reader, School of Management Studies, IGNOU, New Delhi,

1

Abstract Purpose: This study aims to combine the literature on green marketing and retailing into a new managerial framework, to explore store loyalty by segmenting green consumer characterization. In addition, this study explores the concept of “greentailing” in consumer behaviour. Design/methodology/approach: This study summarizes the literature related to the sociodemographic variables, psychographic variables, and the store loyalty to analytically explore the concept of greentailing. A questionnaire was administered to empirically verify the hypothesis. Data collected from Indian consumers was analysed using correlation and multiple regression analysis. Findings: Sociodemographic variables identified as weak measures in explaining store loyalty while psychographic measures showed significant positive effect on store loyalty through greentailing. The relevance of perceived consumer effectiveness (PCE) and environmental concern (EC) found more apparent than sociodemographic variables. The conclusions drawn in the study are useful to the retailers to understand the measures that affect the store loyalty among consumers and will help them in improving the greentailing strategies. Originality: Although researchers have been examining the role of greentailing in the context of green consumerism, the outcomes and processes like green strategies, environmental performances and the sustainable behaviour of consumers, it may be safely affirmed that the broader greentailing dimensions with respect to consumer’s segmentation and store loyalty remains explored limitedly. This study provides the comprehensive understanding of various determinants of greentailing, affecting the store loyalty of the retailers.

Keywords Greentailing, Store Loyalty, Perceived Consumer Effectiveness, Environmental Concern, Psychographic Factors, Sociodemographic Factors

1. Introduction Retail Marketing in India: Trends and Future Insights pp. 16–26 © Ambedkar University

Consumers at present, are conscious regarding environment (Sharma and Kesharwani, 2015) and this generated the demand of environment-friendly products (Gam, 2011). Owning to growing environmental awareness, Indian retail sector intend to take * Corresponding Author: Nitika Sharma ([email protected])

viable green initiatives. In order to gain prominence in competitive market, it is important for retailers to showcase the environmental friendly products they are selling or the environmental practices they are indulging. This concept is known as “Greentailing”. Also, these environmental practices develop positive image in the mind of environmental conscious consumers (Kumar, 2014). Sharma and Narula (2015) examined that environmental consciousness positively influences the store loyalty. Authors also stated that consumer who has a belief that his/her actions may positively influence the environmental problems will show higher repeat purchase, willingness to recommend, and enduring desire to maintain the relationship with commitment over a period of time with one store. Lee et al. (2012) also demonstrated the significant influence of green retailing activities on environmental consciousness. In the past studies, the profiling of green consumers has been examined using different lenses. These studies broadly discussed the green behaviour, environmental consciousness of consumers and the relationship of sociodemographic and psychographic variables with environmental behaviour (Sharma and Sharma, 2013; Akehurst et al., 2012; Laroche, 2001; Straughan and Roberts, 1999). It is argued that the examination of these variables in greentailing is limitedly explored to establish their relationship with store loyalty. Despite the pioneer studies which discussed the segmentations of consumers in retail market with many orientations and contexts (Birtwistle et al., 1998; Lockshin et al., 1997; Darden et al., 1976), few attempts have been made to empirically explore the extent to which, or how, green consumer segmentation may endow store loyalty or how these demographic and psychographic variables can imbue commitment for a store among consumers. This paper aims to profile the sociodemographic and psychographic variables of green consumers in greentailing. Conceptually, retail image can be patronage by performing social responsibility towards the society (Gupta and Pirsch, 2008). Within the retailing context, activities with some good social cause or beyond the interest of the firm exemplify constructive store image. Since, greentailing includes two aspects of environmental conscientious retailing; one involvement of sale of products which have smallest amount of adverse impact on the environment and second to aware the consumers to adopt one or the other methods like providing paper, jute, recyclable, or biodegradable bags; by charging a little extra money for carry bags, by environment-friendly packaging or by motivating “Bring your own bag”, retailers may create positive image among customers (Sharma and Narula, 2015).

2. Literature Review and Framework The review is presented as follows.

2.1 Greentailing Retail sector is prevailing in acquainted, demanding, and competitive environment (Woodruff, 1997). Ferraro and Sands (2009) explored that green retailing can gain competitive advantage as green projects may have the potential to yield high return on investment and cost savings. Sinha (2011) found that sustainable practices adopted by retailers facilitate green consumptions. Mcmillan Dictionary defined greentailing as “the business of selling environmentally-friendly products to the public and/or the practice of using environmentally-friendly methods to run a business which sells products to the public”.

Greentailing and Green Consumer Profile: Retailers’ Strategies for Store Loyalty

17

2.2 Green Consumer Profiling Retail Marketing in India: Trends and Future Insights

18

From the managerial perspective, it is important to identify the consumers who may be influenced by greentailing to generate store loyalty. Several studies have been done in the literature to profile the population of customers conscious towards greentailing, segmenting the profile of green oriented conscious consumers of the population (Sharma and Sharma, 2013; Akehurst et al., 2012; Schlegelmilch et al., 1996). Mainly, the two variables were categorized; sociodemographic factors such as gender, age, education, income, and social class and psychographic factors, such as PCE, altruism, ecological consciousness, lifestyle, personality, motivation, and values (Sharma and Sharma, 2013; Akehurst et al., 2012; Thompson et al., 2009; Paco and Raposo, 2008; Straughan and Roberts, 1999). 2.2.1 Sociodemographic Characterization During the last three decades, pioneered studies of Roberts (1996), Schlegelmilch et al. (1996), and Kilbourne and Beckmann (1998) determined the sociodemographic segmentation of environmental conscious consumers. These studies showed that sociodemographic profiling has less impact on green behaviour. However, it is argued by Diamantopoulos et al. (2003) that it is important to study the sociodemographic variables to investigate its impact on environmental-friendly consciousness of consumers and studied its relationships with environmental measures. In this study, we have empirically explored the linkage between sociodemographics and store loyalty in green marketing context. 2.2.1.1 Age In an attempt to conceptualize the environmental consciousness constructs, many studies considered age as a parameter to segmentalize green consumers (Sharma and Sharma, 2013; Straughan and Roberts, 1999; Widegren, 1998; Altenburg et al., 1996). Many studies indicated that younger age group exhibits higher environmental concern (Sharma and Sharma, 2013; Straughan and Roberts, 1999; Grunert and Kristensen, 1992; Arcury et al., 1987). Many authors have found different result in context of relationship between age and green variables. Diamantopoulos et al. (2003) investigated 33 studies and reported significant relationship between age and environmental consciousness in two studies only. However, since majority of the studies indicated insignificant relationship, therefore to support the view an alternative hypothesis of difference is postulated in greentailing context. H1: Age is related to store ttloyalty in case of greentailing. 2.2.1.2 Gender Several studies examined the linkage between gender and green consumer behaviour. Diamantopoulos et al. (2003) reported significant relationship between gender and greentailing while few other studies contradicted these findings (Sharma and Sharma, 2013; Akehurst et al., 2012; Straughan and Roberts, 1999). While some studies reported that females are more environmentally conscious than males. Few others reported that men relatively have more environmental knowledge than women (Diamantopoulos et al., 2003; Grunert and Kristensen, 1992; Arcury et al.,

1987). It may therefore be conjectured that gender has no association with store loyalty in green marketing context. H2: Gender is related to store loyalty in case of greentailing.

2.2.1.3 Education Many studies were relatively homogenous in their findings that higher educated consumers are more sensible and concerned towards environment as they understand the environmental issues better and act accordingly (Straughan and Roberts, 1999; Zimmer et al., 1994). Conversely, few studies observed that education has no significant relationship with green consumer behaviour (Sharma and Sharma, 2013; Akehurst et al., 2012). Due to the inconsistency in the past results, to support the view following alternative hypothesis of no difference is postulated in greentailing context. H3: Education is related to store loyalty in case of greentailing.

2.2.1.4 Income According to the studies Sharma and Sharma, 2013; Awad, 2011, income has a positive correlation with green consumer behaviour. Few authors have found it otherwise, i.e., no relationship between income and green activities. Hence, it can be asserted that income may affect store loyalty in green marketing context. H4: Income is related to store loyalty in case of greentailing.

2.2.2 Psychographic Characterization Since the 1990s, many researchers have been considering psychographic variables in profiling the green consumers and mainly considering the variables, namely Altruism, Collectivism, Ecological concern, Liberalism, and PCE (Rowlands et al., 2003; Straughan and Roberts, 1999; Schlegelmilch et al., 1996; Shrum et al., 1995). However, no attempts have been made to empirically explore the extent to which, or how these variables affect the store loyalty in greentailing context. In this paper, we have taken two psychographic variables explicitly; PCE and EC and it has been found that higher level of environmental consciousness among consumers apparently show more green purchasing behaviour (Sharma and Kesherwani, 2015; Schlegelmilch et al., 1996). The concept of characterization of green consumers is at mature phase and sociodemographic variables have limited utility in characterization of green consumers. It may be asserted that relevance of sociodemographic variables is relatively less in explaining store loyalty in greentailing context. H5: Sociodemographic variables are less relevant than psychographic variables in explaining Store Loyalty in case of greentailing. 2.2.2.1 Perceived Consumer Effectiveness (PCE) According to the ecological marketing perspective, it has been observed that environmental consciousness manifests through attitudes and beliefs of consumer’s actions contribute towards the solution of environmental problems (Sharma and


19


20

Sharma, 2013; Straughan and Roberts, 1999; Kinnear et al., 1974). This phenomenon is known as PCE. Kinnear et al. (1974) first defined the term PCE as “the measurement of one’s belief in the result of his/her own actions”. Previous studies were relatively homogenous in reporting significant relationship between PCE and green consumer behaviour (Sharma and Sharma, 2013; Akehurst et al, 2012; Kim and Choi, 2005; Straughan and Roberts, 1999). This study proposes the construct, “PCE” to address the premise that it may influence store loyalty if retailers include environmental-friendly activities like usage of renewable energy, encouraging the notion of bring your own bag, biodegradable bags/packages, selling of environmentally products, self-efficient in store operationalization and charging extra for store bags, etc. H6: PCE is related to store loyalty in case of greentailing. 2.2.2.2 Environmental Concern (EC) Conceptually, environmental concern is linked with individual’s general orientation toward environment (Kim and Choi, 2005; Straughan and Roberts, 1999; Chan, 1996). EC is the willingness of the consumers to solve the ecological problem and readiness to change their behaviour for better environment (Dunlap and Jones, 2002; Chan and Lau, 2000). The proposed explanation is that as environmental concern triggers the individual’s awareness (Dunlap and Jones, 2002), beliefs, and value orientation (Schultz, 2000) to willingly show an environmental behaviour will lead to store loyalty in case of greentailing. H7: EC is related to store loyalty in case of greentailing.

3. Methodology Research methodology is presented as follows.

3.1 Data Collection and Survey Instrument The quantitative study was developed to test the relationship among sociodemographic variables, psychographic variables, and store loyalty of green consumers when retailers perform greener activities. To study the effects of two major profiling variables, i.e., sociodemographic variables and psychographic variables on store loyalty when retailers attempt to adopt greentailing approach a questionnaire is prepared and data is collected from 134 participants. Table 1 represents the summary of demographic classifications. Table 1: Demographic Classifications Gender (n = 134) Male Female Total

(%) 65.20 34.80 100.00

Education (n = 134) Senior Secondary

(%) 4.30

Under graduate

39.40

Post graduate Professional Education

36.00 17.40

Other

2.90

Total

100.00

Age (in years) (n = 134) 18–23

(%) 35.40

Monthly Income (n = 134) 50,001

42 and Above Total

100.00

Total

(%)


8.00 100.00

3.2 Measures Following measures are used for the study. 3.2.1 Sociodemographic Variables Gender, age, education, and income were considered as independent variables in this study. 3.2.2 Psychographic Measures PCE and EC were examined as independent measures in psychographic measures (adapted from Kim and Choi (2005)). PCE’s Cronbach’s alpha coefficient was 0.739 and EC was 0.788. 3.2.3 Store Loyalty in Greentailing To examine the store loyalty of the respondents when retailers adopt greentailing strategies, we gave them a hypothetical situation. Five questions were administered to investigate the store loyalty of respondents in case of greentailing. The calculated Cronbach’s alpha of the instrument was 0.934.

4. Results For the preliminary analysis, the correlation analysis was run. The results indicated that the demographic variables, i.e., age and income were positive, and gender and education were negative and insignificantly correlated with store loyalty (p-value > 0.001). However, the correlation coefficients of psychographic classifications were significantly and highly correlated with store loyalty (p < 0.001). Further regression analysis was carried to test the hypotheses. General assumptions of the multiple regressions were taken into consideration. Store loyalty was taken as the dependent variable and demographic variables viz age, gender, income, and education were taken as dependent variables (Regression model M1). According to the regression model fitted on the data, R2 = 0.006, F(4,130) = 0.206 and p - value = 0.934, leading to the rejection of the hypotheses: H1, H2, H3, and H4. Hence, this study affirms that demographic variables (gender, age, education, and income) are not significantly related in explaining store loyalty. The regression coefficients, standard error and R2 values corresponding to all independent variables in the regression model M1 are shown in table 2.

21


22

Table 2: Regression Analysis (Model M1) of Demographic Variables with Store Loyalty Variables Constant Gender Age Education Income

Coefficients 2.282 -0.007 0.098 -0.081 -0.030

p-value 0.000 0.968 0.401 0.602 0.789

Standard Error 0.316 0.165 0.117 0.154 0.113

To examine the hypotheses H6 and H7, stepwise regression models were fitted on the psychographic variables considering PCE and EC as independent variables and store loyalty as dependent variable. In the step one regression equation is fitted with PCE as independent variable (model M2). As shown in Table 3, model M2 explains significant relationship between PCE and store loyalty when retailers adopt green strategies with R2=0.318 and F(1,133) = 62.005, p - value= 0.000. In the second step variable EC is added as dependent variable removing the variable PCE. The R2 value for this model (model M3) is 0.449 and F (1,132) = 108.577, p- value = 0.000 (refer Table 3). In the third step both PCE and EC were taken as independent variables and the model (model M4) explained R2 =0.552, F(2, 132) = 81.47 and p- value = 0.000 (see Table 4). The results of models M2-M4 support the hypothesis H6 and H7. It implies that PCE and EC have a positive association with store loyalty for greentailers. Table 3: Stepwise Regression Analysis of Psychographic Variables with Store Loyalty Model M2 M3

Variable

Coefficients

Standard Error

p -value

Constant

0.918

0.179

0.000

PCE

0.647

0.082

0.000

Constant

0.448

0.181

0.015

EC

0.675

0.065

0.000

R2 0.318 0.449

Table 4: Regression Analysis of Psychographic Variables (Model M4) with Store Loyalty Variables Constant PCE EC

Coefficients 0.001 0.402

Standard Error 0.183 0.073

p-value 0.996 0.000

0.533

0.064

0.000

Again stepwise linear regression is run to test H5 assuming demographic and psychographic variables as independent variables and store loyalty as a dependent variable (Model 5). The result shows R2 = 0.56 , F(6,128) = 27.16 and p-value = 0.000. It is asserted that psychographic variables are appropriate in profiling green consumers which lead to store loyalty in greentailing. The regression coefficients, standard error and R2 values corresponding to all independent variables for the regression model M5

are shown in table. The results of Table 5 also show that demographic variables have no significant role in influencing store loyalty through greentailing. Table 5: Regression Analysis of Sociodemographic and Psychographic Variables with Store Loyalty (Model M5) Variables Constant Gender

Coefficients -0.188 0.031

Standard Error 0.289 0.112

p-value 0.518 0.779

Age Education Income PCE

0.073 -0.014 0.025 0.401

0.079 0.104 0.076 0.075

0.353 0.895 0.742 0.000

EC

0.539

0.066

0.000

The study shows that H1, H2, H3, and H4 are not supported and implies that demographic variables are insignificant in profiling store loyalty of green consumers in case of greentailing. However, H5, H6, and H7 are accepted which infer that psychographics variables are more relevant and significant in impacting store loyalty in case greentailing.

6. Discussions and Implications The emergence of green market provoked many researchers to segmentalize and characterized the green consumers. Therefore, by converging the multidisciplinary view of retail management and green marketing, this paper presents an approach to examine the profiles of environmental-friendly conscious consumers through two constructs, namely demographic and psychographic variables and subsequently examining their impact on store loyalty. The literature has not been conclusive in determination of store loyalty through demographic and psychographic variables and how these variables effect on action orientation of consumers. This study allows us to affirm that demographic characterizations are not pertinent in explaining store loyalty among consumers. Nevertheless, psychographic variables are verified to be more effectual in profiling green consumers and establishing its association with store loyalty. It is worth mentioning results are similar to the previous studies of Sharma and Sharma (2013), Akehurst et al. (2012), Diamantopoulos et al. (2003), and Straughan and Roberts (1999) where they have found that psychographic measures are more appropriate in characterizing green consumer behaviour as compared to sociodemographic variables. This study shows the five perspectives of store loyalty in greentailing. First, the study attempts to understand the role of demographic characteristics and psychographic measures on store loyalty in case of retailers indulge in green activities. Second, when participants were asked if they will be regular and consistent buyer of the store which is dedicated towards greentailing, most of the respondents agreed with it. Moreover, most of the respondents stated that if a store performs a green activity they choose that store over other non-green stores. Third, no noteworthy relationship between sociodemographic classifications and store loyalty were reported. Fourth, there is a positive and significant relationship between psychographic variables and store loyalty in context of greentailing. It means that beliefs and behaviour of consumers generate store


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loyalty if retailers perform environmental-friendly practices. Fifth, this paper extends the research of green consumers buying behaviour by profiling the consumers and investigating the influences of these variables on store loyalty in context of greentailing. The practical contributions in this study verify that retailers can achieve store loyalty if they adopt green actions for preserving the environment. It implies that “judgment” and /or “self-evaluation” are ever present phenomena in human psyche and PCE and EC present in overt or subcutaneous form is forming loyalty among consumers. Hence, marketers need to trigger psyche of consumers for store loyalty in greentailing context. Environmentally sound retailing can help retailers in achieving store loyalty and psychographic variables are more effective in leading store loyalty. Green retailers should articulate the greener actions they are performing to magnetize new markets, customers, and retain the existing green consumers.

7. Limitations and Future Scope One of the limitations of this study is the small sample size and sampling technique which might have affected the generalizability of the results. Moreover, sample is collected from Delhi and NCR only. To revalidate the results, researcher may extend the sample to other cities of the country. Further, in this study store loyalty concentrates only on the hypothetical situation of greentailing and not focused on any particular retailers. For further research, a particular retailer or group of retailers can be considered to evaluate the results. Only a few variables of demographic and psychographic characteristics have been examined. For further research, researchers can use niche demographic segment like young, mid- to high-income, educated, urban women, or enhanced classification of consumers. Furthermore, other psychographic variables can be considered like Altruism, Liberalism, or Environmental concern for better psychographic characterization of consumers. Factors and dynamics of PCE, pro-environmental behaviour, and perspective of store loyalty change over period of time. Hence, future researchers should carry the longitudinal study.

References Akehurst, G., Afonso, C. and Martins Gonçalves, H. (2012). “Re-examining green purchase behaviour and the green consumer profile: New evidences”, Management Decision, 50(5): 972–88. Arcury, T.A., Scollay, S.J. and Johnson, T.P. (1987). “Sex differences in environmental concern and knowledge: The case of acid rain”, Sex Roles, 16(9–10): 463–72. Awad, T.A. (2011). “Environmental segmentation alternatives: Buyers” profiles and implications”, Journal of Islamic Marketing, 2(1): 55–73. Birtwistle, G., Clarke, I. and Freathy, P. (1998). “Customer decision making in fashion retailing: A segmentation analysis”, International Journal of Retail and Distribution Management, 26(4): 147–54. Chan, R.Y. and Lau, L.B. (2000). “Antecedents of green purchases: A survey in China”, Journal of Consumer Marketing, 17(4): 338–57. Chan, T.S. (1996). “Concerns for environmental issues and consumer purchase preferences: A two-country study”, Journal of International Consumer Marketing, 9(1): 43–55.

Darden, W.R. and Perreault Jr, W.D. (1976). “Identifying interurban shoppers: Multiproduct purchase patterns and segmentation profiles”, Journal of Marketing Research, 13(1): 51–60. Diamantopoulos, A., Schlegelmilch, B.B., Sinkovics, R.R. and Bohlen, G.M. (2003). “Can socio-demographics still play a role in profiling green consumers? A review of the evidence and an empirical investigation”, Journal of Business Research, 56(6): 465–80. doPaço, A. and Raposo, M. (2009). “‘Green’ segmentation: An application to the Portuguese consumer market”, Marketing Intelligence and Planning, 27(3): 364–79. D’Souza, C., Taghian, M. and Khosla, R. (2007). “Examination of environmental beliefs and its impact on the influence of price, quality and demographic characteristics with respect to green purchase intention”, Journal of Targeting, Measurement and Analysis for Marketing, 15(2): 69–78. Dunlap, R.E. and Michelson, W. (Eds.) (2002). Handbook of Environmental Sociology. Westport, CT : Greenwood Publishing Group. Ferraro, C. and Sands, S. (2009). “‘Greentailing’: A key to thriving in the recession?”, in Ewing, M. and Mavondo, F. (Eds.), Australian and New Zealand Marketing Academy (ANZMAC) Conference 2009 (pp. 1–9). Melbourne : Australian and New Zealand Marketing Academy (ANZMAC). Grunert, S.C. and Kristensen, K. (1992). “The green consumer: Some Danish evidence”, Marketing for Europe—Marketing for the Future, 1:525–39. Gupta, S. and Pirsch, J. (2008). “The influence of a retailer’s corporate social responsibility program on re-conceptualizing store image”, Journal of Retailing and Consumer Services, 15(6): 516–26. Jensen, B.B. (2002). “Knowledge, action and pro-environmental behaviour”, Environmental Education Research, 8(3): 325–34. Jin Gam, H. (2011). “Are fashion-conscious consumers more likely to adopt eco-friendly clothing?”, Journal of Fashion Marketing and Management: An International Journal, 15(2): 178–93. Kilbourne, W.E. and Beckmann, S.C. (1998). “Review and critical assessment of research on marketing and the environment”, Journal of Marketing Management, 14(6): 513–32. Kollmuss, A. and Agyeman, J. (2002). “Mind the gap: Why do people act environmentally and what are the barriers to pro-environmental behaviour?”, Environmental Education Research, 8(3): 239–60. Kumar, P. (2014). “Greening retail: An Indian experience”, International Journal of Retail and Distribution Management, 42(7): 613–25. Laroche, M., Bergeron, J. and Barbaro-Forleo, G. (2001). “Targeting consumers who are willing to pay more for environmentally friendly products”, Journal of Consumer Marketing, 18(6): 503–20. Lee, N., Choi, Y.J., Youn, C. and Lee, Y. (2012). “Does green fashion retailing make consumers more eco-friendly? The influence of green fashion products and campaigns on green consciousness and behaviour”, Clothing and Textiles Research Journal, 30(1): 67–82. Lockshin, L.S., Spawton, A.L. and Macintosh, G. (1997). “Using product, brand and purchasing involvement for retail segmentation”, Journal of Retailing and Consumer Services, 4(3): 171–83.


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Myers, J.H. (1996). Segmentation and Positioning for Strategic Marketing Decisions. Chicago: American Marketing Association. Rowlands, I.H., Scott, D. and Parker, P. (2003). “Consumers and green electricity: Profiling potential purchasers”, Business Strategy and the Environment, 12(1): 36–48. Schlegelmilch, B.B., Bohlen, G.M. and Diamantopoulos, A. (1996). “The link between green purchasing decisions and measures of environmental consciousness”, European Journal of Marketing, 30(5): 35–55. Schultz, P. (2000). “New environmental theories: Empathizing with nature: The effects of perspective taking on concern for environmental issues”, Journal of Social Issues, 56(3): 391–406. Sharma, C.S. and Sharma, N. (2015). “Impact of self efficacy on green consumerism through consumer’s motivation, PCE and attitude”, Journal of Management Review, 1(2): 40–52. Sharma, N. and Kesherwani, S. (2015). “Encouraging green purchasing behaviour by increasing environmental consciousness”, in Das, J.K., Zameer, A., Narula, A., Tripati, R. (Eds.), Reinventing Marketing for Emerging Markets (pp. 288–301). India: Bloombury Publishing. Sharma, N. and Sharma, C.S. (2013). “Encouraging green purchasing behaviour through green branding”, Business Analyst, 34(2): 65–76. Shrum, L.J., McCarty, J.A. and Lowrey, T.M. (1995). “Buyer characteristics of the green consumer and their implications for advertising strategy”, Journal of Advertising, 24(2): 71–82. Sinha, R. (2011, March). “Green retailing: An exploratory study examining the effects of sustainability on global retail landscape”, in Proceedings of the Conference on Inclusive and Sustainable Growth Role of Industry, Government and Society. Straughan, R.D. and Roberts, J.A. (1999). “Environmental segmentation alternatives: A look at green consumer behaviour in the new millennium”, Journal of Consumer Marketing, 16(6): 558–75. Thompson, D.W., Anderson, R.C., Hansen, E.N. and Kahle, L.R. (2010). “Green segmentation and environmental certification: Insights from forest products”, Business Strategy and the Environment, 19(5): 319–34. Yusof, J.M., Musa, R. and Rahman, S.A. (2011, June). “Self-congruity effect on store loyalty: The role of green environment image”, in 2011 3rd International Symposium and Exhibition in Sustainable Energy and Environment (ISESEE) (pp. 157–64). IEEE.

Quality Attribute and Non-linear Pricing under Changing Agriculture Food Retailing: A Study in India Dipankar Das*

Post-Doctoral Research Fellow, RTCHDS, Institute of Development Studies Kolkata, University of Calcutta, Kolkata

Abstract Large capital or corporate investment in agriculture is becoming an important issue in Indian economy. Large capital investment may be domestic, foreign or both. While domestic (Indian) large firms are allowed for retail as well as cash and carry trade, 100% Foreign Direct Investment (FDI) in cash and carry has become operational only since 2006 with automatic route and in the single-brand retail market since 2012. Most of the Asian countries have liberalized their retail markets. In this study, we have discussed the strategic pricing behaviour of the large capital retail traders (LCT) (including single brand, multi-brand retailers and both domestic and foreign) in the retail agriculture market in India. A model has been established to show strategic price behaviour of the food items in the changing retail markets in India.

Keywords Agricultural commodities, Agriculture marketing and agribusiness, FDI, Non-linear pricing, Retail markets,Wholesale markets

1. Introduction and Objectives Large capital investment in agriculture is an important issue in the Indian economy. Large capital investment can be domestic, foreign or collaborative. The Government of India is continuously reforming agriculture through reforms in food processing, single brand and multi-brand retail trading, cash and carry (wholesale) trade, export–import policies and commodities exchange markets, etc. The Indian market is reformed for domestic large capital traders, including, retailers, wholesalers, exporters, importers and food processing firms. 100% FDI in cash and carry has become operational since 2006 with automatic route and in the single-brand retail market since 2012. In India large capital retail traders as well as cash and carry (wholesaler) traders are either involved in single or multi brand trade. These traders exist in organized as well as unorganized sectors. The organized retail traders include privately owned large retail businesses, publicly traded supermarkets, corporate-backed hypermarkets and retail chains. On the other hand, unorganized retail traders are generally the small capital retail traders (SCT) involved in conventional format of low cost retailing. These include owner operated general stores, the local corner shops, convenience stores, handcart and pavement vendors, etc. Single-brand retailing implies a retail store selling goods under a single brand name, whereas a, multi-brand retail refers to selling multi-brands under one roof. * Corresponding Author: Dipankar Das ([email protected])

Retail Marketing in India: Trends and Future Insights pp. 27–41 © Ambedkar University


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FDI in retail has always been a debating issue in India. The main question is the competition between small and large retailers. It is debated that in the presence of large retailers the predominant small scale retailers will exit from the market and the market will be oligopolistic. We have been trying to answer this question for the last few years. In a country like India wherein a large number of small scale retailers are involved in agriculture food products, inflation is an important issue. This study focuses on the strategic pricing issues related to agriculture food retailing in India. This paper is an extension of an earlier research work by the author (Das, 2015a, 2015b). Here, we have tried to analyse the strategic priceing behaviours of large capital retail traders for agriculture-food commodities. We have explored the possible reasons through which small capital retailers can continue existing in the market with their limited competitive strength in the presence of large capital retailers. It is found that large scale retailers are mostly involved in selling high quality products whereas small scale retailers are mostly trading average quality products. Hence their pricing is also different. The large scale retailers use non-linear pricing strategies and fix higher prices for their products compared to the small scale traders. Since India is a cost sensitive economy, therefore only, a small section of the total consumer will purchase from the large retailers. The rest will buy from the small retailers. This is supporting the existence of the small scale retailers in the retail market in the presence of the large retailers. Objectives of the Study (1) To identify the strategies for setting prices of agriculture-food products, by the large capital retail traders in urban retail market in India. (2) To analyse, whether the large capital retail traders can offer agriculture-food items at lower prices.

2. Literature Review The research question is whether with the presence of large retailers in the food retail market, small retailers will be crowded out from the markets. To answer this question, we need to search for the strategy adopted by the large retailers to enter into the market. The Michael Porter’s “Three Generic Strategies” identifies the strategies which are used to enter into the market. These are “Cost Leadership Strategy”, “Differentiation Strategy”, “Focus Strategies” (Porter, 1980). Treacy and Wiersema (1995) in their book “The Discipline of Market Leaders’’ have modified Porter’s three strategies to describe three basic “value disciplines” that can create customer value and provide a competitive advantage. They are-operational excellence, product leadership and customer intimacy. A popular post-Porter model was presented by Mauborgne and Kim in the article “Creating New Market Space” (1999). They discussed a “value innovation” model in which companies must look outside their present paradigms to find new value propositions. Their approach complements most of Porter’s thinking, especially the concept of differentiation. They later introduced their ideas in the book “Blue Ocean Strategy’’. Thus, it is difficult, but not impossible, to topple a firm that has established a dominant strategy. “Blue Ocean Strategy’’ suggests that an organizations should create new demand in an uncontested market space, or a “Blue Ocean”, rather than compete head-to-head with other suppliers in an existing industry (Maubargne and Kim, 2015). From the market observations and analyzing data, we have found that, the large retailers have adopted Blue Ocean Strategy. They are actually trying to differentiate products from the existing small retailers. They usually do not want to compete with the small retailers and set different

price-quality combinations. Maintenance cost for quality products is high and also require technical skills, which is difficult to maintain for small retailers. In support of this argument few recent studies existing in the literature are discussed here. The study “High Value Agricultural Commodities’’ conducted in Indonesia (Toiba et al., 2013) has shown how the traditional food retailers are still operational among the majority of consumers. It showed how a particular socio economic niche tends to use large modern food retailers. The paper by Sahara et al. (2013) examined the relationship between chili farmers and their buyers.The paper by Feenstra and Romalis (2014) showed the influences of quality on the unit values of internationally traded goods. Increased competition affects nonlinear pricing. The key fact revealed by the heterogeneity amongst firms in any market is that, relative to the incumbent, individual entrants tend to select two-part tariffs with higher tariff weightings, i.e. “high fixed fees and lower marginal prices” (Davies et al., 2009;Yang and Ye, 2008). A study in Indonesia and other countries in Asia by the International Food Policy Research Institute (IFPRI) found that, firms are allowed to collect from the farmers maintaining a quality standard (Toiba et al., 2013). Now, we need to understand the relationship between quality and non-linear pricing strategies. The theory of industrial organization discusses two non-linear pricing strategies bundling and tying. Bundling can lead to the over supply or under supply of particular goods. Bundling is a plausible tool to protect a multi-product monopolist in restricting entry of new players in the market. It also help the monopolist in raising its profit (Nalebuff, 2004). In India, at present newly arrived large capital firms are using bundling strategy in selling agriculture-food items, combining the attribute of quality with it. This is leading to inflation of food prices (Das 2015a, 2015b, 2015c). In case of mixed bundling strategies, it is important to consider the ability to monitor purchases by the monopolist (McAfeeet al., 1989). This is an alarming situation in relation to the agriculture-food market. The study Bhattarai and Schoenle, 2011, established that in the U.S. producer price index relating multi-product price setting frequencies of price changes are more with the increase in the number of goods produced by the firms. (Das 2015a, 2015b, 2015c) found that the large capital retail traders in the agricultural food markets, agricultural food items maintaining some quality norms. Selling food items of higher quality standards serve as a differentiation strategy for large capital traders. These firms enjoy extra market power by selling differentiated products, which translates into higher marketing margins. The degree of product differentiation is determination of the size of the marketing margin (Azzam, 1999; Dixit and Stiglitz, 1977; Keller, 1976; Tomek and Robinson, 2003; Wohlgenant, 1999, 2001). The concept of marketing margin is central to the analysis of a food supply chain. A farm-to-retail marketing margin is the difference between the implicit value of an agricultural commodity when sold at the retail level in processed form versus the explicit value of the unprocessed commodity at the farm level. In an earlier studya, it was observed that large capital retail traders were not able to sell agricultural commodities or food items through their outlets at lower prices. Further findings suggests that, if large capital single and multibrand retailers operate in the market, then small capital retail chain operate with reduced market share. (Das, 2015a, 2015b). The competitive behaviour among Das, D. (2015a), “Impact of corporate investment on trading of agricultural commodities: A study of select districts of West Bengal”, The University of Burdwan (Unpublished Ph.D. thesis).

a

Quality Attribute and Non-linear Pricing Under Changing Agriculture-Food Retailing

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the corporate traders in the world market is found to be imperfectly competitive. The competition among the local small and large capital traders are generally not of oligopolistic, but monopolistic in nature (Das, 2012). Therefore, to maintain the standard and high market share with high profit margin the corporate traders always buy “standard products” specified by themselves from the farmers. As the corporate traders mostly buy quality products at a higher price from the wholesale markets and the farmers, so they are not generally able to offer lower prices in the retail market. The presence of large capital traders in the rural wholesale markets have changed the price behaviour of agriculture commodities (Das, 2014). In a study related to the bargaining power of the farmers, it has been shown how the preferences of SCT are depending upon the preferences of LCTb (Das, 2014). So far, after studying the identified literature we have found that for high quality products the price will be higher. But no existing study in the literature discusses, how the expenditure on other commodities is related to the purchase of particular high quality agriculture food products. In this study we have explored how tying expenditure among food and non-food items is related to the quantity discounts on agriculture food products for large capital retail traders. In this article, we have extended the work and built new models based on the new database, related to strategic price behaviour of large capital retailers under the changing or liberal retail market in India and have shown how the price behaviour of agricultural or food items is changing based on the strategies applied by the large capital retailers.

3. Methodology As explained in the literature section the “Blue Ocean Strategy” suggests that an organization should create new demand in an uncontested market space rather than competing head-to-head with other suppliers in an existing industry (Maubargne and Kim, 2005). From the market observations and analyzing data, this study attempts to establish that large capital retail traders have adopted “Blue Ocean Strategy”. They differentiate their products from the existing small retailers. They usually do not compete with the small retailers and set different price-quality combinations. Maintaining high quality products require managerial skills and large capital investments. That is difficult to maintain by the small retailers. Large retailers are collecting high quality products and setting non-linear pricing strategies. Therefore,we have used non-linear pricing methodology “Block Tariff” (Stahl and Siegel, 2005) in our study and the non-linear price curve theoretically to analyze and explain the pricing strategies of large capital retailers and scope of interaction with the small capital retail traders. The study identifies whether the large retailers are using “Tying”strategy or “Bundling” strategy. Bundling strategy is further explored for “Mixed Homogeneous Bundling”and “Mixed Heterogeneous Bundling” Further, it explores how these strategies are changing the per unit price of agriculture-food products at the large retail stores. Non-linear pricing methodology has been used here to identify the presence of quality attribute of agriculture-food items sold by the large retailers. From the literature, it is clear that if a firm uses non-linear pricing strategy it has some degree of monopoly power. Formulation of the theoretical model is based on the primary data collected on Das, D. (2015a), Impact of corporate investment on trading of agricultural commodities: A study of select districts of West Bengal, The University of Burdwan (Unpublished Ph.D. thesis).

b

strategies applied by the large retailers for agriculture commodities, especially food items. Regarding the field survey, farm level data is collected from rural areas of West Bengal and data for retail markets is collected from urban markets in Kolkata. The steps of the analysis are as follows: Step 1: C ollect data to know how the large retailers (or LCT) collect agriculture-food products from the rural wholesale markets. Step 2: Analyse the collected data to identify the preferences of the LCT in their collection process. The theory of Industrial Organization is used to know the disparities of strategic behaviour between the LCT and the existing predominant SCT present in the rural wholesale markets. Step 3: C ollect data and identify the strategies adopted by the LCT in setting prices in the retail markets. Step 4: Analyse of the data and identify the non-linear pricing strategies. Step 5: C onstruct the formal theoretical model based on data analysis. Research question Here, we are interested to know how tying expenditure among food and non-food items is related to the pricing strategies adopted by LCT

4. Empirical analysis The study is based on the survey conducted in the district of North 24 Parganas, West Bengal, India, with four corporate firms, including one firm with 100% FDI in cash and carry trade. These were Reliance Fresh, Keventer Agro, Aditya Birla Group and Metro Cash and Carry. Metro Cash and Carry is the only 100% FDI in West Bengal in the wholesale market and others are domestic and working in partnership with foreign firms through indirect control. To identify the preferences of these LCT in their collection process and make sure that they are collecting only high quality products, that divides the rural wholesale market into two parts - average quality market and high quality market, we required field evidences. From Tables 1–3, we can understand the quality preferences of LCT when they buy from the producers (i.e., the farmers). Table 1: The Average Portion of Quality Products Collected by the Large Capital Traders in West Bengal in the Year 2014 (Example 1) Year Crops (fruits and vegetables)

The average proportion of quality products The range of average land used by a farmer

2014 Brinjal Lady’s Ridge Water Potato Pointed Ceylon Fingers gourd/ spinach gourd spinach Chinese okra 70%

60%

65%

80%

77%

80%

80%

10 katha 8 katha 8 katha 10 katha 10 5 katha 5 katha to 2 to 1.5 to 1 katha to to 10 to 10 bighac bigha bigha 3 bigha katha katha


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(Here proportion of quality is based on size, color, freshness, etc. For example, in the case of potato and lady’s finger the special size is 3–4 inches, etc.) Table 2: The Average Portion of Quality Products Collected by the Large Capital Traders in West Bengal in the Year 2014 (Example 2) Year

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Crops (vegetables) The average proportion of quality products The range of average land used by a farmer

2014 Bottle Cauli- Cabbage String gourd/ flower beans Calabash 60%

80%

80%

20%

Mango Turnip

60%

87.5%

Broad beans

87.5%

10 katha 1.5 10 katha 10 katha 50 to 55 5 katha 3 katha to to bigha to to 7 bigha to 12 trees to 10 4 katha katha 1 bigha 5 bigha katha

Table 3: The Average Portion of Quality Products Collected by the Large Capital Traders in West Bengal in the Year 2014 (Example 3) Year

Crops (Vegetables)

The average proportion of quality products

Range of low quality product

The range of average land used by a farmer

2014

Bitter gourd

85% to 90%

10% to 15%

3 katha to 4 katha

Tables 1 - 3 shows, some quality norms are maintained while collecting fruits and vegetables by large capital traders. Information from the officials of these firms, local traders or small capital traders and farmers explain that, farmers are not preferring to sell only quality portion to the LCT. Therefore, LCT depends on local level consolidator or small capital traders. The LCT participates in the local mandis also. When the LCT collects from the farmers directly, then they try to maintain a rejection of 20–30% as non-standard and accept the rest. Moreover, when they collect from the local markets or mandis then, they reject 10–15% as non-standard and accept the rest. Therefore, it is observed that the farmers always try to avoid sell directly to the LCT and participate in the local markets or mandis. The following sections explains the pricing strategies adopted by the urban retail traders in West Bengal based on the field survey. A “katha” (also spelled “kattha” or “cottah”) is a unit of area in Bangladesh and India, approximately equal to 1/20 of a bigha (also formerly beegah) = 720 square feet and 1 acre = 3 bigha.

c

4.1 Tying, Bundling and Per Unit Price Behaviours Tying refers to a situation where a consumer buys a good only by purchasing another commodity as well. Corporate traders used to follow the strategy in setting prices for food items, for example, if any consumer buys a fixed value of certain commodity say non food-grocery item then from that date of purchase until to some stipulated date that the consumer shall have an advantage of getting food items with some specified discount. Tables 4–10 help to understand tying strategy. Table 4: Weekly Retail Price of Onion in Kolkata Market (Example - 1) Market name (1)

Kolkata

Food items or Vegetable name (2) Onion

Weekly retail price on 23.05.2014 (3) `25 per kg

Weekly retail Average of two price on retail prices in 30.05.2014 column (3) and (4) (4) (5) 23.50 per kg `22 per kg

(Data source: Website of Directorate of Economics and Statistics, Department of Agriculture and Cooperation, Ministry of Agriculture, Government of India.)

Table 5: Daily Wholesale Price of Onion in Kolkata Market (Example 1) Market name Food items (1) or Vegetable name (2) Bara Bazar Onion (Posta bazar)

Minimum price on 28.05.2014 (3) `13 per kg

Maximum price on 28.05.2014 (4) `13.75 per kg

Modal price on 28.05.2014 (5) `13.75 per kg

(Data source: website of agricultural marketing information network-AGMARKNET)

Table 6: Tying Strategy with Repeated Tying Expenditure (Example 1) Shop or bill amount

Food items or vegetable name

Strategic price

Tying constraints

`150

Onion

`10.90 per kg

Maximum 2 kg per bill of `150

(Data source: Spencer’s Kolkata Wednesday, 28.05.2014, The Times of India [Mid-Week blockbuster offers]).

Table 7: Daily Retail Price of Onion in Kolkata Market (Example 2) Market name (1)

Food items or vegetable name (2)

Retail price on 18.05.2016 (3)

Kolkata

Onion

`14 per kg

Kolkata

Potato

`20 per kg

(Data source: Retail price reported as of 18/05/2014, Unit-Rs./` (Data Source-Market Intelligence Units, DES)).


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Table 8: Daily Wholesale Price of Onion in Baro Bazar (Posta Bazar) Market (Example 2) Market name Food items or Minimum price Maximum price Modal price vegetable name on 18.05.2016 on 18.05.2016 on 18.05.2016 (1) (2) (3) (4) (5) Baro Bazar Onion `10 per kg `10.50 per kg `10.50 per kg (Posta Bazar) Baro Bazar (Posta Bazar)

Potato

`16.40 per kg

`16.50 per kg

`16.40 per kg

(Data source: Website of Agricultural Marketing Information Network-AGMARKNET).

Table 9: Daily Wholesale Price of Onion in Kolkata Market (Example 2) Market name (1)

Price on 18.05.2016 (3)

Kolkata

Food items or vegetable name (2) Onion

Kolkata

Potato

`16 per kg

`8.50 per kg

(Data source: Wholesaleprices reported as of 18/05/2014, Unit-Rs./` (Data Source-Market Intelligence Units, DES)).

Table 10: Tying Strategy with Repeated Tying Expenditure Shop or bill amount `59 for fruits andvegetables `59 for fruits andvegetables

Food items or vegetable name

Strategic price

Tying constraints

Onion

` 9.90 per kg

Potato

`13.90 per kg

Maximum 2 kg per bill of `59 Maximum 2 kg per bill of `59

(Data source: Spencer’s Kolkata Wednesday, 18.05.2014, The Times of India Kolkata).

From the field survey (Example-1) as presented in Table 6, we observed that if any consumer wants to buy onion, more than 2 kg at `10.90 per kg, then that consumer needs to shop again for `150. Therefore, for first 2 kg onion, tying expenditure is `150, for next 2 kg buy or 4 kg buy total, tying expenditure is `300, for 6 kg buys total tying expenditure is `450 and so on. Tables 4 and 5 shows the average retail price on other small capital retail markets and wholesale price of onion on the same date, offered by the LCT. The important outcome of this data is when the wholesale price of onion in Kolkata is `13.75 (both maximum and modal) and average retail price mainly in the small capital retail markets is `23.50 then the LCT offers the strategic price of per 1kg onion at `10.90. It appears that the strategic price offered by LCT is lower than the average retail price in the SCT stores of Kolkata, but it is not true. This is because to get per 1kg onion at `10.90 at the LCT store, extra amount has to be spent by the consumer on other commodities.

From Tables 7–10, explains another identified “Tying dependent quantity discount”, whereas Table 10 explains the quantity discount in the vegetables itself.

4.2 Bundling Bundling is a special case of tying in which two or more commodities are sold only in fixed proportions. Bundling may be either pure or mixed. Pure bundling occurs when a firm sells two or more products in a bundle and not individually. Mixed bundling occurs when the commodities are made available both in bundles and individually. Mixed homogeneous bundling can be a situation where for example the price of two units of a good is lower than twice the price of one unit. Mixed heterogeneous bundling is a situation where commodity bundling can also affect several commodities. For instance, a restaurant ties the consumption of several dishes into a menu. 4.2.1 Mixed Homogeneous Bundling Table 11: Mixed Homogeneous Bundling Strategy for Onion taken by the LCT Product name (1)

Weight per packet (in kg) (2)

Maximum retail price per packet (MRP in `) (3)

Strategic price or actual price with different selling strategies (in `) (4)

Onion

1 kg per packet

`70

`70 per kg packet for 1–3 packets buy `55 per kg packet for 4 kg and above buy

(Source: advertisement by Spencer’s, Kolkata retail shop, The Telegraph, date– 31/05/2014)

Table 12: Mixed Homogeneous Bundling Strategy for Potato taken by the LCT Product name (1)

Potato

Weight per packet (in kg) (2)

Maximum retail price per packet (MRP in `) (3)

Strategic price or actual price with different selling strategy (in `) (4)

1 kg per packet

`20

`20 per kg for below 1 kg buy `16 per kg for 1 kg and below 3 kg buy `15 per kg for 3 kg and above a buy

(Source: advertisement by Spencer’s, Kolkata retail shop, survey data, date-23/03/2014)


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Table 13: Mixed Homogeneous Bundling Strategy for Rice, Atta, Pulses taken by the LCT Product name (1)

Weight per packet (kg) (2)

Elina Long Grain Rice Basmati Supreme Loose Rice

5 kg per packet (pk) 1 kg per packet

Ganesh Whole Wheat Atta Premium Masoor Dal Premium Loose

5 kg per packet 1 kg per packet

Maximum retail Strategic price or actual price Per packet price with different (MRP in `) selling strategy (in `) (3) (4) Buy 1 @ 215/pk `800 Buy 2 @ 199/pk Buy 1kg @ 38/kg Buy 5 kg @ 37/kg `171

`121/pk

`106

Buy 1 kg @ 107/kg Buy 3 kg @ 105/kg

(Source: BIG BAZAAR offer under all the outlets in West Bengal, date April, 2014).

From Tables 11–13 and column 4 of each table, we find that large capital firms are selling homogeneous commodities of different food items at a high price for the lower quantity buys. Moreover, relatively lower price is offered for bulk purchase, for example, Table 11 shows the fact that the price of 1kg packet of onion is `70, if any consumer buys up to three packets and the price will reduce to `55 for each packet for 4 or more kg buys. 4.2.2 Mixed Heterogeneous Bundling Table 14: Mixed Heterogeneous Bundling Strategy for the Processed Food Items taken by the LCT Product name Weight per Maximum retail price Strategic price or actual packet per bundle if buy price with the different (in kg) individually and selling strategy if buy (2) bundling discount is not specified products with (1) available (MRP in `) specified bundle (in `) (3) (4) Wheat Atta+ 5 kg `1080 for all three `789 for the said products Mustard Oil+ 5 litre products in non-bundle in the bundle Sugar Crystal 5 kg Oil+ 5 litre `635+ `635 for the said products Sugar 3 kg Sugar price (not given) in the bundle Wheat Atta+ 5 kg `165+ `165 for said products in Salt 1 kg `16 (i.e., `181 for non the bundle bundle) Basmati Rice+ 5 kg `1590 for all three `888 for said products in Mustard oil+ 5 kg products in non-bundle bundle Sugar crystal 5 kg (Source: Food bazaar seasonal discount offer under all the future group shops, date 12/08/2013).

From Table 14, we have seen that a consumer will have advantage in buying a specified bundle of different commodities of heterogeneous nature from the large capital retailers rather than the consumer’s choice bundle. For example, from Table 14 of item (1), if a consumer buys wheat atta, mustard oil and sugar crystal of 5 kg each respectively not in a single purchase then the total price would be `1080. However, if that consumer buys the same items with the same weight on a specified day in bundle, then the total price will reduce to `789. This means that to gain in terms of lower price from the large capital retail shop, a consumer has to buy only the specified bundle. This explains that in order to get discount or to reduce the price a consumer need to buy in advance and the choice of a bundle is dependent.

5. Model for Deriving per Unit Price of Food Items at the Large Capital Retail Market

Price per unit

We have dealt here with the service sector. In our current study, we are not dealing with the case of monopoly market. Assuming that the nature of the market is monopolistic competition as countably few LCT and large numbers of the SCT are selling homogeneous but differentiated products. The product differentiation is mainly qualitative (through grading). The LCT prefers to sell quality and standard products with a high degree of non-linear pricing strategies (Das, 2015a, 2015b). Another important notion of the demand curve for perfectly substitutable goods explained in the book “The Theory of industrial Organization” (Tirole, 2007) is used here to derive price curve. We have used “Block Tariff” (Stahl and Siege, 2005) in our study and the non-linear price curve to analyse theoretically based on the empirical evidences that this non-linear price in agriculture-food market is because of quality preference and this must reduce consumer surplus and increase producers’ surplus (here LCT) and must have an impact on the general price level of food products. An example of a block tariff is illustrated in the Figure 1.

0

q1

q2

q3

q4

q5

Number of units Figure 1: Block tariff


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5.1 The model Retail Marketing in India: Trends and Future Insights

Based on the survey data new models have been developed and explained in Figure 2 (per unit price measured on vertical axis and quantity in horizontal axis). Explanation of dependence of quantity discount on tying expenditure

38

0 R1 is the total amount paid on buying commodities other than X. As tying is there so the expenditure 0 R1 includes a price paid in advance to get r % discount on food item. Let this extra payment be µ, where 0 ≤ µ ≤1. So the per unit payment in advance for maximum 0q1quantity of commodity buys is 0 R1 0q1 .

Price per unit of one particular food product X

A

B 0 R1

Total value of expenditure on R2 non-food products or “Tying”expenditure

a

q1 b

C D q2

I

K

q3

Quantity of one preticular food product X

Figure 2: Price and expenditure analysis of processed food products, based on the strategies set by the large capital retail traders due to quantity discount (0R1 µ + (0R1 (1–µ ) = 0R1 (1) Here, µ be the payment made in advance to avail the discount on food items purchased and (1-µ) be the real amount spent for buying other commodities other than food item X. Now, look for Oq2units’ food item buy. There is no limit of buying food item X for getting a discount. This means the discount is not limited with the food item buy in terms of quantity or value of purchase. From the Figure 2, 0 R1 0q1 = tan a, 0R2 0 q 2= tan b, and tan a = tan b. So, if any consumer wants to buy the 0qi amount of agriculture-food products X at a possibly lower price with spending an extra amount on bundling (both mixed homogeneous and mixed heterogeneous) and tying; then the new price equations Pq1 , Pq2 , Pq3 can now be written for 0 q1 , 0 q2 and 0 q3 purchase quantity respectively as, Pq1 = Bq1 + µ tan a (2) Pq2 = Dq2 + µ tan b

(3)

(4) Pq3 = Kq3 Here, Pq1 , Pq2 , Pq3 denote the price per unit the food product, for quantities 0 q1 , 0 q2 , 0 q3 respectively.

Expenditure equations for quantity discount with tying expenditure

{ } {{ }} { } {{ { } }} {{ }}

E ( q X ) = pq X + µ 0 R1 for 0 < q X ≤ q1 EE( q( qX X) = ) = pqpqX ++µµ0 R01R1 forfor 0 0