Antecedents and consequences of a retailers’ price image: The moderating role of pricing strategy

Abstract

The high competition in retail sectors around the world has a decisive impact on the selection of the right retail price strategies for developing a strong retail price image. Therefore, this study examines the effect of price-related and non-price factors on developing a favourable retailer’s price image in tandem with the mediating role of price image and the moderating role of pricing strategy on shopping intention. This study employed the mall-intercept method and collected data from 522 retail customers of different retail stores in Indonesia. SEM-PLS is employed for examining the conceptualized hypotheses and the research model. The empirical findings exhibit that price-related factors and pricing strategy are important antecedents of retail price image, but non-price-related factors are insignificant antecedents of the retailer’s price image. The empirical findings also exhibit that the price image mediates the relationship between price-related factors and shopping intention. In addition, the price strategy has an impact on price-related factors, non-price factors, price image, and shopping intention. Price strategy also moderates the relationship between price image and shopping intention. This study suggests that retail managers should select one of the best retail price strategies, such as everyday low pricing (EDLP), promotional pricing, and high-low pricing (Hi-Lo) strategy, to develop a strong retailer price image and to enhance customer impression on price setting. In so doing, the purchase intention of retail customers could be increased significantly, and the selling of the retailers could be higher.

Keywords:

• price image
• price strategy
• everyday low pricing
• high-low pricing
• shopping intention

PUBLIC INTEREST STATEMENT

High global retail sector competition has a significant impact on the selection of the most effective retail pricing tactics for establishing a strong retail price image. This research investigates the impact of price-related and non-price aspects on a retailer’s price image, as well as the function of price image as a mediator and the moderating effect of pricing strategy on shopping intention. Price-related variables and pricing strategy were revealed to be significant antecedents of retail price image, but non-price-related elements were not. We also observed that the price image mediates the association between price-related factors and buying intention. Additionally, pricing strategy influences price-related variables, non-price-related factors, price image, and shopping intent. The association between price image and shopping intent is further moderated by the way prices are set.

1. Introduction

The relationship between consumers and retailers is getting more complicated because of price expectations and retail decisions (Koschmann & Isaac, 2018; Omar et al., 2018). Besides, with high price competition, emerging retail formats-such as discount stores, supermarkets, and weekly markets-are excellent options for grocery retailers as price is a key driver of the retail choice of consumers (Khare et al., 2014; Rondan-Cataluña et al., 2019; Willart, 2015; Zielke, 2018). Hence, in the competitive world, effective pricing is a must in the retailing business for success (Ailawadi & Farris, 2017; Dolbec & Chebat, 2013; Grewal et al., 2017; Rondan-Cataluña et al., 2019; Willart, 2015). Thus, offering a good price has always been a key element of the success of a retail business, and such an offering has also been facilitated to shape the price image.

In supporting the above-mentioned edict, Zielke (2010) and Lombart et al. (2016) have stated that consumers visit a retail shop based on their beliefs and feelings about the price image of the retailer. According to Graciola et al. (2018), price-sensitive consumers frequently choose where to buy a product based on the retailer’s price image. Koschmann and Isaac (2018) have also suggested that price expectations and consumer decisions are often determined by a retailer’s price image. Therefore, it is crucial for retail managers to identify key drivers that help in building a favourable price image within consumers’ minds. Thus, one of the aims of this study is to examine the antecedents and consequences of a retailer’s price image.

Based on the adaptation theory of Helson (1964), Sinha and Adhikari (2017) suggest that consumers use different information cues to develop an image of a brand or retailer as well as to make a decision. The information cues include focal and contextual cues. The focal cues are the information related to price factors, including pricing strategy. In contrast, contextual cues include other factors that provide a frame of reference within which consumers examine the focal cues, such as the store’s atmosphere, store service quality, etc. Furthermore, Hamilton and Chernev (2013) proposed a conceptual framework presenting the determinants of a retailer’s price image and their implications for consumer shopping intentions. Furthermore, Hamilton and Chernev (2013) conceptualized price-related factors¹ (e.g., price dispersion, pricing policies, price dynamics, and price-based communications) and non-price-related factors² (e.g., retailer physical attributes, level of services, and non-price store policies) as antecedents of retailers’ price image. Thus, this study considers price-related factors and non-price-related factors as potential factors of price image.

Empirically, several prior studies have tested this framework of Hamilton and Chernev (2013) in various retailer settings, such as antecedents of price image (Lourenço et al., 2015) and consequences of price image (Lombart et al., 2016; Zielke, 2010, 2018). Chang and Wang (2014) investigated the antecedents and consequences of the price image with the moderating effect of the retail format, considering various key determinants of a retailer’s price image (i.e., price value image, price fairness image, price reward image, and price pleasure image) without taking into account consumer-based drivers. Prior studies, as discussed in Hamilton and Chernev (2013), have only focused on one aspect of price image antecedents and consumer shopping intentions. Furthermore, prior studies paid little attention to price image antecedents (i.e., price-related and non-price factors), price image consequences (i.e., consumer shopping intention), consumer characteristics (i.e., income and education), and retail price strategy with a holistic perspective (see Chernev and Hamilton (2018) and Lombart et al. (2016) for more literature and conceptual framework). Besides, Lombart et al. (2016) have proposed several consequences of the price image. Furthermore, Zielke (2018) has suggested that the retailer’s price image has an association with consumers’ shopping intentions. Zielke (2018) has also reported that a retailer’s price image can be differently interpreted by consumers because of the change in the retail price strategy. Hence, this research study attempts to empirically test the Hamilton and Chernev (2013) conceptual framework while incorporating price strategy into the framework, as price strategy is likely to have direct effects on price-related factors, non-price factors, price image, and consumer shopping intentions.

Pricing strategy has become one of the most interesting topics in retailing research over time (Fassnacht & Husseini, 2013; Grewal & Levy, 2007; Son & Jin, 2019). Tang et al. (2001) have argued that “nothing is more important in business than getting the pricing strategy right” for retail managers. Additionally, it is known that price strategy (offered price) is an important factor in consumers’ decision making and selection. It also has an impact on all determinants of consumer choices. Therefore, it could have moderating effects on all existing relationships, which have been ignored in earlier studies. Thus, based on these premises, this study is motivated to investigate the effect of price-related and non-price factors on the retailer’s price image with the moderating role of pricing strategy, focusing on the retail consumer in Indonesia.

The remaining paper is organized as follows. The following section discusses the theoretical foundation and hypothesis development for the antecedents of a retailer’s price image, price image dimensions, price strategy, and consumer shopping intentions. The third section describes the research methods used to test the hypotheses and presents the empirical results. The next section discusses the research findings. The final section discusses research contributions and implications, the limitations of the study, and possible suggestions for future research.

2. Theoretical and hypotheses development

2.1. Retailer’s price image

Price image is a consumer’s impression of the overall level of price at the retailer (Babin et al., 2016). Hamilton and Chernev (2013) defined that price image as “the general belief about the overall level of prices that consumers associate with a particular retailer”. This indicates that the retailer’s price image is not referred to specific item prices or unit prices but the overall prices. Hence, the price image is an overall impression of the aggregate price level of a retailer, described by ordinary scales (e.g., expensive vs. inexpensive prices), and the price image is not only informed by more than observed prices, but by integrated non-price cues. This definition refers to a price image that is a multi-dimensional construct that reflects an overall impression of the retailers’ price. Given the growing trend of research studies on price images, several extant studies have focused on unidimensional price images (Koschmann & Isaac, 2018). However, Zielke (2010) has defined the retailer’s price image from a multidimensional perspective which consists of five dimensions, such as price-level perception, value for money, price perceptibility, price processability, and evaluation certainty. Henceforth, the retailer’s price image in study is regarded as multidimensional.

2.2. The antecedents of retailer’s price image

Hamilton and Chernev (2013) and Chernev and Hamilton (2018) identified some key elements of price-related factors in retailers’ price image, i.e., “the dispersion of prices, pricing policies, price dynamics, and price-based communications”. First, the dispersion of prices reflects how high and low prices are dispersed within retail (Hamilton & Chernev, 2013). Thus, customers not only evaluate the overall price level, but they are price-sensitive. Thus, they check the dispersion of prices within the retail store and compare unit prices with other retail stores (Alba et al., 1994; Tang et al., 2010). Roth et al. (2017) empirically evidence that consumers assess the unit price before deciding to buy a product from retailer.

Second, price dynamics reflect retail price changes over time by offering coupons, discounts, and price adjustments (Hamilton & Chernev, 2013). Empirically, Abrate et al. (2019) have shown that a higher dynamic price leads to higher revenue. Third, price-related policies reflect price-match guarantees using promotion, where consumers attempt to take price-match benefits from these policies because consumers believe that price-match guarantees are a direct signal of low prices (Mamadehussene, 2019). Finally, price-based communication reflects communication resulting from sales tags and price-based advertising (Hamilton & Chernev, 2013).In practice, consumers not only gather information about unit prices by seeing label prices, but by searching for price information through social media, advertising, and public relations activities. Graciola et al. (2018) demonstrated that customers’ price-related sensitivity is highly associated with shaping a retailer’s price image. Hence, the following hypotheses are proposed in this study:

H₁:

Price-related factors promote the retailer’s price image.

Besides price-related information, non-price information can also inform the price image impressions. These are “retailer physical attributes, level of services, and nonprice-store policies” (Chernev & Hamilton, 2018; Hamilton & Chernev, 2013). The physical characteristics reflect store location, ambience, and decor; the level of service reflects the service delivery of retail staff to customers; non-price-policies reflect policies in retail, such as return policies. For example, Wee et al. (1995) state that physical characteristics as an important aspect are considered by consumers in South-East Asia. According to Obeng et al. (2016), the level of services, as a non-price policy, can shape the price image. Thus, it can be called “competitive service overlap” due to the retailer offering similar services. As a result, retailers will be successful when they introduce unique service portfolios that create price image impressions for consumers. Hence, based on these arguments, non-price-related factors are important antecedents of a retailer’s price image; thus, this study hypothesizes as follows:

H₂:

Non-price-related factors promotes a retailer’s price image.

Hamilton and Chernev (2013), in their framework, have not studied the relationship between antecedents of price image (i.e., price-related and non-price factors) and consumer shopping intentions, although it has been widely discussed in the retailing management literature. Traditional price theory suggests that price will determine consumer choice behaviour. Similarly, consumers interpret retailers’ policies based on their psychological perspective, and response to policies is interpretation wise (see, Lambert, 1972). This suggests that managers determine prices that will have an impact on consumer shopping intentions. Additionally, a new development in behavioural pricing research has highlighted that the current empirical findings contradict existing knowledge of price-related concepts (see, Koschate-Fischer & Wullner, 2017). As a result, it has become the primary reason for integrating new findings with prior empirical research. Therefore, this study proposes hypotheses as follows:

H₃:

Price-related factors have a positive effect on consumer shopping intention.

H₄:

Non-price-related factors have a positive effect on consumer shopping intention.

2.3. Roles of a retailer’s price image

Several studies have examined the price image on future purchase intentions in the retailing sector. Zielke (2010), for example, investigated the impact of a retailer’s price image on future purchase intentions of grocery retailers.In line with this, the study of Babin et al. (2016) found that price image has a positive direct effect on value and patronage. Baker et al. (2016) tested actual image and purchase intentions in retail settings in a recent study.Therefore, the inclusion of store image is an important factor in determining purchase intention and finding expected effects of store image on purchase intention. A similar test was also conducted by Calvo-Porral and Levy-Mangin (2016), and they also concluded that price/store image acts as a determinant of customers’ purchase intentions towards retail shopping. Then, the following hypothesis is proposed:

H₅:

A retailer’s price image has a positive effect on consumer shopping intention.

Although few prior studies have focused on the mediating role of price image, the literature is still limited regarding the mediating role of a retailer’s price image between the relationship of price-related and non-price factors with consumer shopping intentions. Kim et al. (2015) tested the image’s mediating role on behavioral intention empirically. Their study, on the other hand, focused on destination images and revisiting intentions in the context of an international sporting event like the Formula 1 Chinese Grand Prix (F-1). Ali et al. (2015) found that price perceptions significantly mediate the relationship between the physical environment and customer satisfaction in the context of Chinese resort hotels. In line with this, Roy et al. (2016) investigated the role of internal reference price (IRP) in mediating consumers’ future intentions in the context of food and beverage outlets. In this regard, the scarcity of empirical studies on the mediating role of a retailer’s price image has led us to consider the price image as a mediator between the relationship of price and non-price factors to consumer shopping intentions. With the support of Hamilton and Chernev (2013) and the above premise, this study proposes the following hypothesis:

H₆:

A retailer’s price image mediates the relationship between price-related factor and consumer shopping intention.

H₇:

A retailer’s price image mediates the relationship between non-price-related factor and consumer shopping intention.

2.4. The role of price strategies

Research on pricing strategy in the context of retailing has been popular, as it is one of the most powerful and effective strategic tools in the retailing business (see, Gauri et al., 2008). However, there is a lack of consistency in defining the term. Fassnacht and Husseini (2013) have discussed similarities and differences in definitions and interpretations of pricing strategy in the retailing setting. Most of the studies have followed the definition of Hoch et al. (1994), a pioneer of price strategy research, that a price strategy is a continuum between everyday low price (EDLP) and high-low (Hi-Lo) price strategies. An EDLP price strategy offers a low price without providing discounts and promotions. The Hi-Lo price strategy starts with higher prices and gradually lowers them by offering frequent discounts to stimulate consumer purchasing intentions (Gauri et al., 2008). Furthermore, according to Hoch et al. (1994), the interesting fact about EDLP in practice is that EDLP store prices were lower than Hi-Lo. Specifically, EDLP offers a price that is less than 11% lower than everyday basis prices and 6% lower than Hi-Lo store promotion prices. This indicates that the percentage of price reductions in EDLP stores is higher than in Hi-Lo stores because a price-sensitive customer primarily visits stores or purchases a product from the store due to a large percentage of price reductions. Based on these arguments, it can be said that the price strategy relates to consumer preferences between the EDLP and the Hi-Lo price strategy.

Lambert (1970 and 2016) has suggested price strategy as a key driver of consumer choices, and it has an influence on all aspects/determinants of consumer choices. Thus, previous research on pricing strategy and consumer store choice has looked at pricing strategy as a predictor of consumer store choice (e.g., Binkley & Chen, 2016; Gauri et al., 2008; Geyskens et al., 2018; Olbrich et al., 2017; Shankar & Krishnamurthi, 1996; Tang et al., 2001). Furthermore, in this study, important aspects, such as price-related factors, non-price-related factors, and a retailer’s price image, are influenced by a retailer’s price strategy. Therefore, this study conjectures that price strategy can influence price-related factors, non-price-related factors, and a retailer’s price image.

Empirically, Gauri et al. (2008) have tested the effect of a retailer’s price strategy on consumer store choice. However, they have not explored how pricing strategy may influence price-related factors, non-price related factors and retailer’s price image. A recent study on price strategy has been done by Olbrich et al. (2017), where they only focused on price strategy outcomes on private label and national brand performance. Yan (2009) also investigated price strategy on optimal pricing and brand management strategies. Binkley and Chen (2016) tested price strategies in various types of food retail formats. They discovered that consumers search for low prices and more choice for buying in the EDLP store strategy, while others choose based on preference and convenience (non-price-related factors), with little apprehension about prices. However, this study did not make explicit the fact that price-related factors and non-price related factors are influenced by price strategy. Therefore, it can be argued that price strategy is important for determining price-related factors, non-price-related factors and building a strong price image. Therefore, this study proposes the following hypotheses:

H₈:

Price strategy has a positive effect on price-related factors.

H₉:

Price strategy has a positive effect on non-price-related factors.

H₁₀:

Price strategy has a positive effect on a retailer’s price image.

H₁₁:

Price strategy has a positive effect on consumer shopping intention.

Graciola et al. (2018) argued and demonstrated that price sensitivity has a moderating effect on repurchase intent. Thus, it has become an empirical foundation for considering price strategy further as a moderator, since, in their study, the price-sensitive was tamed to evaluate the effects of low and high price level perception on purchase intention. Thus, this study intends to introduce price strategy as the moderating variable in the framework of Hamilton and Chernev (2013), as price strategies are related to EDLP versus Hi-Lo. Furthermore, Lambert (1972 & 2016) advocates that price strategy can influence all aspects of consumer choices, leading to the conclusion that price strategy may interact with all of those aspects. As a result, price strategy may have an interactive effect on a retailer’s price image and consumer purchasing intentions. Additionally, price strategy can both strengthen and weaken the relationship between price image antecedents and consumer shopping intentions, because failing to identify all price and non-price factors can result in a missed opportunity to shape a strong price image and increase consumer shopping intention. Hence, based on the above-mentioned arguments, this study proposes the following hypotheses:

H₁₂:

Price strategy moderates the relationship between price-related factor and retailer’s price image.

H₁₃:

Price strategy moderates the relationship between non-price-related factor and retailer’s price image.

H₁₄:

Price strategy moderates the relationship between retailer’s price image and consumer shopping intention.

2.5. Research model

This framework is built based on a framework of Hamilton and Chernev (2013) that proposed a new research model for future study regarding a retailer’s price image in three perspectives (e.g., retailer-based drivers, consumer-based outcomes and consumer-based drivers). However, to offer a more comprehensive new understanding, this study combines price strategy as a moderator within this model. This study is underpinned by the adaptation theory (Helson, 1965) and consumer buying behaviour theory. The theories suggest that consumers consider the benefits and sacrifices of a particular decision (Thaichon et al., 2014). More precisely, if consumers have a negative perception of the price of the retail store, that creates a negative store price image in consumers’ minds and reduces purchase intentions for the retail store. On the other hand, when store performance outweighs, past expectations can enhance price image and shopping purchase intention.

According to Helson’s (1964) adaptation-level theory, the past and present context of experience defines an adaptation level relative to which stimuli are perceived and compared. The consumer tends to seek information before they make their buying decision. This information gathering is based on contextual cues as well as a comparison of store images and pricing strategies. Based on their strategies, consumers tend to make their buying decisions. Therefore, the background and contextual information has motivated us to integrate the pricing strategy within the framework of Hamilton and Chernev (2013). The detailed model is presented in Figure 1. In this model, we added, the income factor to improve the causal interpretability, and the hypothesized effects are estimated at constant levels of the control variables (Klarmann & Feurer, 2018). Thus, the impact of personal characteristics may be minimized.

Figure 1. Conceptual research model.

3. Research design and analysis

3.1. Questionnaire and measures

A structured survey questionnaire was developed following previous studies. Following mall intercept methods, data for this study were collected from outside of prominent retailers chosen randomly in the city area in Indonesia, especially two groups of retailer formats, i.e., lowest price stores and promotional stores. A few motivating factors have promoted this study to select retail consumers in Indonesia. First, based on a report by the Indonesian Retailers Association in 2017, Indonesia was the third-largest Asian country in terms of recent retail sales growth after India and China (approx. USD 15 billion) (APRINDO, 2017). Second, following the Global Retail Development Index (GRDI) report in 2017, Indonesia is included in the top-ten retail index in the world because of “a more favourable foreign investment environment, strong economic growth, and a consumption boom” (Kearney, 2017). Third, the retail sales growth projection for Indonesia is around 5 per cent per annum between 2008 and 2018 by value (Statista, 2018). Finally, Salanto (2018) suggests an optimistic retail outlook and modest growth is expected in Indonesia. Moreover, he reported that although global retail growth has declined, the Indonesian retail segment has experienced consistent growth in 2018.

In the present study, we have measured seven constructs with their respective measurement items. These items were modified to fit the retailer in the Indonesian context. The measurement items for price-related factors consist of four-dimensions (i.e., the dispersion of prices, pricing policies, price dynamics, and price-based communications) that were adopted from prior studies (Alba et al., 1999; Amara & Bouslama, 2011; Estelami et al., 2007; Zielke, 2006). Measurement items for non-price-related factors consist of three-dimensions (i.e., retailer physical attributes, level of services, and non-price store policies) that were adopted from previous studies (Amara & Bouslama, 2011; Baker et al., 1994; Chang & Wang, 2014; Estelami et al., 2007). Measurement items related to retail price image and consumer shopping intentions were adopted from the study of Zielke (2010). The Price strategy is adopted from the study of Hoch et al. (1994). All constructs’ items in this study were measured using a five-point Likert scale, anchored from 1 = strongly disagree to 5 = strongly agree, excluding price strategy which was measured using a nominal scale, anchored from 0 = EDLP and 1 = Hi-Lo. We also added control variables such as education and income. The details on construct measurement items can be seen in Appendix A1.

The instruments were validated using a rigorous, two-step pre-test approach. In the first stage, the item of the questionnaire was originally developed in the English version. The final survey of the study was conducted in the Indonesian language, as the respondents in the study were Indonesian retail consumers. As suggested by Chapman and Carter (2016), to ensure the quality of the instrument, back-to-back translation was conducted from English to Bahasa Indonesia, and Bahasa Indonesia to English. The final version was validated by two experts and one professional translator. The face validity of the instrument was assessed by several academic experts on the clarity of understanding, wording, structure, sentence, and content. The second stage of pre-testing of the questionnaire was conducted on 54 retailer consumers. The final version of the survey questionnaire is modified based on the obtained feedback from both step pre-tests.

3.2. Data collection

For data collection, we have used the mall intercept method, where consumers are approached outside of selected grocery retailers in Indonesia and by group retailers (i.e., lower price stores and promotional stores) over a period of two months. Following the study of Shah et al. (2018), we have approached every third individual to participate outside of grocery stores to control the randomness of the data. Furthermore, to increase reliability, we have collected data at different times and on different days. Hence, with the support of Aczel and Sounderpandian (2009) and Malhotra (2010), we can say that collecting data in such a manner will reinforce the quality of the data and allow for generalization of the study results.

Comrey and Lee (2013) suggested that a sample size of 500 questionnaires is a good sample size for most research problems, and they also recommended that a sample size of 200–250 is still good for empirical investigations and generalizing results. Grounded on their suggestions, we approached 776 individuals to participate in this study, but 611 retail consumers agreed to participate in filling out this questionnaire. We also provided a token of appreciation to the respondents for participating in the study. We assured them that data collection is purely for academic purposes, and, thus, anonymity is assured. Following Hair et al. (2014), p. 89 questionnaires were discarded due to multivariate requirements such as missing data, outliers, careless response patterns, and other reasons (Johnson & Wichern, 2004). Finally, 522 questionnaires out of 611 were deemed as useable, after scrutinizing and data cleaning (i.e., Mahalanobis distance, Cooks distance, centred leverage value, and outliers) (Susanto et al., 2020; Wardi et al., 2018; Wesarat et al., 2018). These were employed in data analysis.

The questionnaire shows the demographic aspects of the respondents. The response rate for our study is above 70%. Table 1 shows the participant profiles and sample descriptive data. The sample consisted of 300 males (57.47%), 222 females (42.53%), 186 aged 18–25 (25.63%), 153 aged 26–35 (29.31%), 177 aged 36–55 (33.91%), and 6 aged 56+ (1.15%). The majority of the respondents had completed a bachelor’s degree (53.39%), followed by senior high school (36.88%). Following Indonesia’s income categories, the majority of respondents (41.95%) have a middle-low income, while only 12.45% have a middle-upper income. Based on occupation, most of the respondents were employees, i.e., 82 government employees (15.71%), 119 private employees (22.80%), and 144 entrepreneurs/traders (26,63%). We assessed common method variance using Harman’s single-factor test and the Common Latent Factor (CLF) as suggested by Podsakoff et al. (2003). The result shows no significant common method bias in this dataset.

Table 1. Demographic profile and sample descriptive statistics

Demographic aspects	N	Percentage (%)
Gender
Male	300	57.47
Female	222	42.53
Ages (years)
18 to 25	186	35.63
26 to 35	153	29.31
36 to 55	177	33.91
Above 56	6	1.15
Education completed
below senior high school	37	7.09
Senior High school	196	37.55
Bachelors degree	275	52.68
Masters degree	14	2.68
Incomes (IDR)
below Rp 2 Juta	207	39.66
above Rp 2 juta to Rp 3.5 juta	219	41.95
above 3.5 juta	65	12.45
not response	31	5.94
Proffesions
Civil servants (government employees)	82	15.71
Private employees	119	22.80
Entrepreneurs/traders	139	26.63
House’s wife	42	8.05
Students	140	26.82
Total	522	1.00

3.3 Data analysis and results

In this study, we have employed Structural Equation Modeling (SEM) to verify the conceptual model, where we have considered variance-based techniques through Partial Least Square (PLS) with Smart-PLS version 3 as recommended by Hair et al. (2017). Considering that, this study concurrently estimates direct and indirect effects, in which SEM-PLS is a most popular statistical tool in estimating direct and indirect relationships (Chin, 1998; Hair et al., 2017; Henseler et al., 2009), and it has ability to analyses both single-item constructs and complex models (Hair et al., 2019). We performed our data analysis in the first step in assessing the measurement model for the reliability and validity instrument, and then we ran the structural model for hypotheses testing, following the guidelines of Anderson and Gerbing (1988), Hair et al. (2013), and Ringle et al. (2012). The following paragraphs discuss these analytical procedures further.

3.3.1. Measurement model

The Measurement model, as the first stage, with PLS-SEM, is employed for examining and confirming internal consistency and construct reliability, including convergent validity (indicator reliability and average variance extracted/AVE), and discriminant validity (cross-loading and Fornell-Larcker criterion) (Hair et al., 2017). The results of the measurement model estimation are shown in Table 2.

Table 2. Results summary for construct reliability and validity

Latent variables and dimensions	Cronbach’s Alpha	rho_A	Composite Reliability	AVE*
Retailer’s price image	.776	0.784	.849	0.532
Price-level image	.761	0.760	.863	0.677
Value for money	.618	0.635	.787	0.553
Price perceptibility	1.000	1.000	1.000	1.000
Price processibility	.904	0.963	.953	0.910
Evaluation certainty	.755	0.755	.891	0.803
Price-related factors	.789	0.796	.855	0.541
Dispersion of price	1.000	1.000	1.000	1.000
Price dinamics	.712	0.859	.867	0.766
Price-based communication	.788	0.789	.904	0.825
Price-related policy	.518	0.634	.794	0.662
Non-price-related factors	.791	0.796	.857	0.546
Service level	.736	0.752	.883	0.790
Physical attributes	.803	0.832	.868	0.623
Non-price policies	.774	0.774	.869	0.689
Consumer shopping intention	.662	0.699	.812	0.591

Notes: *AVE=Average variance extracted.

Following the psychometric properties for all constructs, we have presented the factor loadings in Appendix A1. The factor loadings show that selected items exhibit more than 0.70, suggesting that selected items are appropriate for inclusion in the next stage of analysis. Cronbach’s alpha, composite reliability, and AVE values are presented in Table 2, and the values of all three indicators indicate that all factors in the measurement model carry more than the cut-off value.Thus, we can believe that the measurement model has achieved the desired level of reliability and internal consistency. Furthermore, discriminant validity is measured utilizing Fornell-Larcker criterion analysis, which is presented in Table 3. All Fornell-Larcker criterion analysis values are consistent with the hypotheses proposed by Hair et al. (2017) and Henseler et al. (2015). Finally, values relating to the VIF value (will be provided upon request) were lower than 2.00, which indicates low multicollinearity.

Table 3. Results summary for discriminant validity based on Fornell-Larcker criterion

Variables/dimensions	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16
Consumer shopping intention	0.769
Dispersion of price	0.116	1.000
Evaluation certainty	0.275	0.010	0.896
Non-price policies	0.068	0.002	−0.046	0.830
Non-price-related factors	0.077	−0.017	−0.065	0.904	0.739
Physical attributes	0.114	0.149	0.067	0.276	0.327	0.789
Price dinamics	0.115	0.071	0.000	0.145	0.169	0.277	0.875
Price perceptibility	0.245	0.119	0.408	−0.006	−0.024	0.022	0.007	1.000
Price processibility	0.041	0.106	−0.129	0.218	0.199	0.325	0.359	−0.010	0.954
Price-based communication	0.173	0.153	0.003	0.225	0.206	0.199	0.398	0.084	0.257	0.908
Price-level image	0.378	0.078	0.258	0.118	0.126	0.189	0.201	0.131	0.117	0.220	0.823
Price-related factors	0.158	0.143	−0.040	0.252	0.238	0.287	0.716	0.046	0.453	0.846	0.231	0.736
Price-related policy	0.138	0.115	−0.009	0.145	0.131	0.218	0.547	0.051	0.290	0.423	0.181	0.717	0.814
Retailer’s price image	0.413	0.081	0.273	0.117	0.124	0.198	0.217	0.126	0.132	0.257	0.939	0.263	0.198	0.730
Service level	0.064	−0.038	−0.071	0.486	0.812	0.288	0.149	−0.040	0.108	0.115	0.095	0.143	0.071	0.094	0.889
Value for money	0.371	0.066	0.210	0.076	0.076	0.153	0.195	0.064	0.114	0.239	0.571	0.252	0.213	0.803	0.051	0.744

3.3.2. Structural model

A structural model in the second stage was suggested by Anderson and Gerbing (1988). Using SEM-PLS software, the latent variable scores were created into a single-item measurement using single items of their latent variable from stage 1 results (Hair et al., 2017). Hence, the structural model of the conceptual model was estimated with two-stage estimations, and the findings of the structural models are presented in Tables 4, 5, and 6, which comprise path coefficients for direct effects and indirect effects including moderating effects and mediating effects. The t-statistic of the structural model is shown in Figure 2.

Figure 2. Estimated structural model with t statistic.

Notes: Price-related factors, non-price-related, and price image are second order variable.

Table 4. Path coefficients summary for direct effect

Hypothesis	The relationships	Coefficient	STDEV	T Statistics	Decision for Hypothesis
	Panel A: Control variables
	Education → Shopping Intention	0.025	0.045	0.553	n.a.
	Education → Price strategy	−0.201	0.046	4.351***	n.a.
	Income → Shopping intention	0.018	0.040	0.443	n.a.
	Income → Price strategy	−0.023	0.047	0.487	n.a.
	Panel B: Exogenous variables
H1	Price-related factors → Price image	0.209	0.055	3.778***	Supported
H2	Non-price-related factors → Price image	0.044	0.048	0.904	Not supported
H3	Price-related factors → Shopping intention	0.007	0.051	0.141	Not supported
H4	Non-price-related Factors → Shopping intention	0.007	0.041	0.166	Not supported
H5	Price image → Shopping intention	0.41	0.054	7.559***	Supported
H8	Price strategy → Price-related factors	0.475	0.031	15.461***	Supported
H9	Price strategy → Non-price-related factors	0.219	0.040	5.419***	Supported
H10	Price strategy → Price image	0.117	0.046	2.540**	Supported
H11	Price strategy → Shopping intention	0.116	0.049	2.377**	Supported

Notes: *significant at P-value <0.10, **significant at P-value <0.05, and ***significant at P-value <0.01.

Table 5. Path coefficients summary for mediating effects

Hypothesis	The relationships	Coefficient	STDEV	T Statistics	Decision for Hypothesis
H6	Price-related factors → price image → Shopping intention	0.077	0.024	3.600***	Supported
H7	Non-price-related factors → price image → Shopping intention	0.015	0.017	0.888	Not supported

Notes: *significant at P-value <0.10, **significant at P-value <0.05, and ***significant at P-value <0.01.

Table 6. Path coefficients summary for moderating effects

Hypothesis	The relationships	Coefficient	STDEV	T Statistics	Decision for Hypothesis
H12	Price-related factors*Price strategy → Price image	0.090	0.058	1.539	Not supported
H13	Non-price-related factors*Price strategy → Price image	0.004	0.051	0.080	Not supported
H14	Price image*Price strategy → Shopping intention	0.109	0,044	2.470**	Supported

Notes: *significant at P-value <0.10, **significant at P-value <0.05, and ***significant at P-value <0.01.

4. Empirical results and discussion

4.1. Antecedents and consequences

Table 4 (see Panel A) illustrates the coefficients for control variables, where education and income were evaluated as control variables.³ The findings show that the income factor does not have significant impacts on a price strategy and consumer shopping intentions. Education has a significant impact on a price strategy but insignificant on consumer shopping intentions. Thus, empirical findings suggest that consumers’ education and knowledge are important in understanding what kind of price or promotion is offered by retailers.

Panel B in Table 4 represents the relationship between exogenous variables and endogenous variables. The finding supports H₁, stating that price-related factors promote the retailer’s price image. In the same way, H₁₀ is also supported by stating that a price strategy has a positive effect on a retailer’s price image. The results of price-related factors and price strategy as antecedents of the price image indicate that these variables have a significant effect on a retailer’s price image, which confirms the conceptual framework for managing the retailer’s price image based on price-related factors as suggested by Hamilton and Chernev (2013). These results are also supported by the study of Graciola et al. (2018), where they have shown customers’ sensitivity towards price shapes a retailer’s price image, as it is a key driver of their decisions about where to buy. Similar intuition is also provided by Mamadehussene (2019). These results also give an exciting insight, suggesting that price-related factors are more relevant as compared to non-price-pertinent factors in the context of developing economies where people are more price-conscious. Furthermore, price is strangely an important factor in retailers’ price image, suggesting that retailers’ price strategy has to be effective and right to shape retailers’ price image in customers’ minds. The findings are indirectly supported by Olbrich et al. (2017), as they have shown that price strategy has an influence on private label and national brand performance.

Another antecedent of a retailer’s price image, particularly a non-price-related factor, is found to be statistically insignificant in explicating a retailer’s price image, thus not supporting hypothesis H₂ which asserted that non-price-related factors promote a retailer’s price image. The results, however, support the assertion made in H₉, suggesting that price strategy has a positive effect on non-price-related factors. Hence, the insignificant influence of non-price related factors on a retailer’s price image suggests that Indonesian retail buyers are not influenced by non-price related factors. Thus, non-price-related factors have a limited influence on the perception of a retailer’s price image. One of the possible reasons for this finding could be that Indonesia is a developing economy where most residents belong to middle-income groups. Thus, the behavioural attachments and attitudes towards any products are shaped by their budget constraints, and they buy a product based on low-price categories. They are sensitive only to price-related factors. Thus, non-price related factors do not shape the attitude towards a retailer’s price image.

Furthermore, the empirical results support H₃, stating that price-related factors have a positive effect on consumer shopping intentions. Similarly, H₅ is also supported, asserting that a retailer’s price image has a positive effect on consumer shopping intentions. Moreover, H11 is also accepted, stating that the price strategy has a positive effect on consumer shopping intentions. On the contrary, H₄ is rejected, which contends that non-price-related factors have a positive effect on consumer shopping intentions. These empirical results suggest that consumer shopping intentions towards retailers are determined by a retailer’s price image and price strategy rather than price-related and non-price-related factors. Such findings are supported by Hamilton and Chernev (2013) as their conceptual model only shows the price image is a determinant of consumer shopping intentions. Additionally, these findings are consistent with prior studies that price image and price strategy are important aspects that determine consumer behavior, especially in consumer shopping intentions (e.g., Binkley & Chen, 2016; Graciola et al., 2018; Lombart et al., 2016; Olbrich et al., 2017; Shankar & Krishnamurthi, 1996; Tang et al., 2001; Zielke, 2010).

4.2. Mediating effects

The coefficients for mediating effects are reported in Table 5. The findings suggest that the price image mediates the relationship between price-related factors and consumer shopping intentions, as hypothesized in H₆. The price image, on the other hand, does not mediate the relationship between non-price related factors and consumer purchasing intentions, so the H7 is rejected. The mediation effect, which is related to price image, price-related factors and consumer shopping intention, shows that the price image partially mediates the effect of price-related factors on consumer shopping intention. Hence, this finding infers that the relationship between price-related factors and consumer shopping intentions also involves a sequence of relationships with the price image, as it is one important intervening construct. Importantly, when price-related factors (e.g., dispersion of price, price dynamics, price-based communication, and price-related policy) are involved in building a price image in consumers’ minds, that further increases consumer intentions. According to Iglesias and Guillen (2004), the purchase process is determined by price-related factors. Similarly, Van Heerde et al. (2008) argued that consumers search for the best deals and spend most of their budget based on the retail price image. Thus, the role of the price image is important as a mediating role. Hence, our findings provide strong support to a conceptual framework of managing the price image of Hamilton and Chernev (2013) and extend their research framework. Additionally, our findings are supported by Roy et al. (2016), where they found the price factor as a mediator in the context of customers’ intentions.

Furthermore, the price image does not mediate the effect of non-price-related factors on consumer shopping intentions as there is an insignificant relationship between non-price-related factors (e.g., physical attributes, service level, and non-price policies) and the price image. This finding is not surprising in the case of the Indonesian market, as residents of developing economies have low confidence in retailing products. In support of this, Hunneman et al. (2015) suggested that retailers should not overstress having a favourable retailer’s price image when consumers’ confidence towards retailers is low.

4.3. Moderating effects

The moderating effect of price strategy on retailer’s price image and consumer shopping intentions is shown in Table 6. The results show that when price strategy interacts with price-related factors and non-price-related factors, the coefficients of interaction effects are found to be insignificant on the retailer’s price image. These findings suggest that price strategy does not moderate the relationship of retailer price images with price and non-price factors, as hypothesized in H₁₂ and H₁₃. Hence, both H₁₂ and H₁₃ are rejected. These findings are in line with Zielke and Komor (2014) that the price strategy is not a significant moderator. Even though the findings are not in line with the study hypothesis, one could presume that price strategy will not interact with price-related and non-price related factors when it has a significant influence on price-related and non-price related factors. These findings show the possibility of mediating roles of price-related and non-price related factors in the relationship between price image and price strategy.

Furthermore, when price strategy interacts with a retailer’s price image, it exposes that the interaction of price strategy and price image has significant effects on consumer shopping intention, inferring that price strategy moderates the relationship between the retailer’s price image and consumer shopping intention. Hence, this finding supports hypothesis H₁₄. This finding confirms and shows the new phenomenon that a good price strategy will increase the degree of the relationship between a retailer’s price image and consumer shopping intention. Thus, if retailers believe that they have built a strong price image inside their customers’ minds, then they could offer their best price strategy to increase consumer shopping intentions towards retailing products.

5. Research contributions and implications

This study has explored the antecedents and consequences of the price image, the mediation effect of the price image, the moderation effect of price strategy on the relationship of a retailer’s price image with both prices related and the non-price related factors, and the moderation effect of price strategy on the relationship of retailer’s price image with consumer purchase intention. This study, therefore, contributes in the following ways. Firstly, the study contributes to a conceptual framework for managing the price image of Hamilton and Chernev (2013). We have attempted to provide a thorough description of managing a price image to explain consumer shopping intentions. The findings for the direct effect show that most of the explained variance (R-square value) was 35% of the variation in the retailer’s price image, and 28% of the variation in consumer shopping intention. Following the value of the acceptable R-square in assessing endogenous constructs in model prediction accuracy, which should be around 25% (see Hair et al., 2017; Henseler et al., 2015). In short, the inclusion of managing a price image framework in explaining consumer shopping intentions enhances statistical confidence in a model’s prediction accuracy with satisfactory R-square values (see, Sarstedt et al., 2014).

Secondly, this study has used an existing research framework to build a novel research model in the context of consumer retailing. This study specifically tested Hamilton and Chernev’s (2013) conceptual framework of managing price images and the price strategy concept of Hoch et al. (1994), to provide a more holistic explanation of how consumers react and behave towards a retailer’s price image. The research provides a greater view of a retailer’s price image and customer purchasing intentions by incorporating a price strategy as a moderating factor in the model. This study also provides a platform for future research to build extended models for consumer behavior, especially those related to consumer patronage, store image, and pricing strategies.

Thirdly, our empirical findings have confirmed that consumers have a high sensitivity towards retailer pricing strategies and price-related factors. We also confirm that non-price-related factors, such as retailer physical attributes, level of services, and non-price store policies are not considered by retail consumers in Indonesia. Thus, our findings provide new insights for consumers from one of the largest developing economies researchers and marketers, that all price-related factors are more important than non-price-related factors (such as retailer physical attributes, level of services, and non-price store policies) in shaping retailer price image in customers’ minds, ultimately driving their purchase intent. Thus, the marketers and owners of retail businesses in developing economies, which comprise the most significant portion of the world’s population, need to focus on an effective pricing strategy to have leverage over their competitors. Moreover, these retail stores in developing countries should focus on developing their private label brands, which are an alternative to expensive products specifically designed to target the price-conscious consumer. This ultimately improves store patronage by price-conscious consumers (Shah et al., 2020).

Fourthly, to the best knowledge, the price strategy factor is studied as a moderator in retail focusing research for the first time, where this study has explored the moderating role of price strategy on a relationship among antecedents of a retailer’s price image (price-related factors and non-price-related factors) and on the relationship between a retailer’s price image and consumer shopping intention. The finding on the moderating effects adds new insights and extends the study of Gauri et al. (2008). In their research, they have discussed price strategy as an antecedent of consumer choice of retailer, whereas other roles of price strategy, such as moderating role, were not explored earlier. In this aspect, this study further enhances the roles of price strategy as antecedent and contingency aspects in explaining consumer shopping intentions, thus extending the retailing literature. Hence, the findings contribute to the literature by validating price strategy as an important moderator within the conceptual framework for managing price images as introduced by (Hamilton & Chernev, 2013). In support of this finding, we recall Lambert’s (1972) classic piece of evidence on price and consumer choice, in which he discussed how consumers choose between two price strategies, namely a high-priced and a low-priced brand.

Fifthly, the findings of this research have important implications for retail managers and marketing practitioners. This study suggests that price factors are more important than other non-price factors in developing a favorable price image. Hence, retailers should focus more on the price-related factors that inevitably contribute to customer buying intentions. This research study contradicts previous researchers’ claims that non-price-related factors are equally important in shaping a store’s price image (Hamilton & Chernev, 2013). This, however, may be more viable for developing countries where consumers have a limited shopping budget and are more price conscious. Therefore, these consumers are more focused on price-related factors as compared to other non-price related factors, to develop their price image and ultimately make a shopping decision. Therefore, managers in developing countries, especially in Indonesia, tend to focus more on offering the best price to attract more customers and develop a competitive edge. Additionally, these price-related factors impact the price image of a particular store but also impact the overall purchase intentions of consumers. Non-price related factors, on the other hand, were found to have no significant impact on a store’s price image and overall shopping intentions. This may imply that store managers may make concessions on other factors in order to focus their resources on offering competitive prices.In doing so, for the price-conscious consumer, retail stores should focus on developing their own private label brand that is an alternative to expensive products, which will improve their store patronage and purchase intentions (Shah et al., 2020). The product offering should be at a budget price and provide a value addition. Furthermore, this study demonstrated the price image’s mediating role, emphasizing the importance of a favorable price image in increasing a consumer’s shopping intentions at a specific retail store.Hence, these stores should develop a strategy to develop a competitive price image, which is a significant mediator in developing consumer shopping intentions for a particular store. Furthermore, this study found the moderating effect of pricing strategy on the relationship between price image and shopping intention. Therefore, selecting the right pricing strategy is critical, as it affects the consumer’s mind in a variety of ways and develops a favorable response from the consumer towards the retailer (Binkley & Chen, 2016). Hence, retailer should tactic and manage their pricing strategies effectively. The customer’s perception and value-based pricing could be effective for EDLP stores (Binkley & Chen, 2016). Psychology-based pricing could be effective in the supermarket.

5.1. Limitations and future research

This study also has a few limitations, like other studies, thus offering opportunities for possible future research. Firstly, this study is based on a convenient sample. However, we tried to randomize the respondents by selecting different stores in different geographical locations. Moreover, the respondents were also contacted at a different age, gender, income, and household size. Nonetheless, it is not a quota sample.

This study was conducted in Indonesia. Thus, the same model may be replicated across other developing and developed countries to increase its generalizability. Furthermore, household income is an important issue in pricing literature (Olbrich et al., 2017), and thus, net income is a promising topic for future research studies. Moreover, future research studies may include the impact of personal characteristics of respondents, to develop a more comprehensive model. Since the study variables are related to consumer perception, the future may consider perception variables within Hamilton and Chernev (2013).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Universitas Negeri Padang [1762/UN35.13/LT/2022].

Notes

1. Price-related factors are those that have an impact on the cost of purchasing products.

2. Non-price related factors refer to factors that reflect things other than cost, and they are related to stores.

3. Previous empirical studies have suggested that demographic and characteristic variables play an important role in describing the phenomenon of study and error variance statistically (e.g., Becker, 2005); Gauri et al. (2008). Furthermore, statistically, the control variables are considered extraneous variables that minimize error terms and have the potential to increase statistical power (e.g., Schwab, 2005; Spector & Brannick, 2011).

Appendix

Table

Construct/Dimensions/Code	Items	Loadings	Source
Retailer’s price image
Price-level image
PLI1	The prices in this store are generally very low.	0.840	Zielke (2010)
PLI2	I can buy many products here with cheap prices.	0.841	Zielke (2010)
PLI3	The prices in this store are cheaper than in other stores.	0.787	Zielke (2010)
Value for money
VFM1	Price in this store is less than what I imagine.	0.781	Zielke (2010)
VFM2	At this price, I would save a lot of money.	0.776	Zielke (2010)
VFM3	At this store, I get good value for money here.	0.670	Zielke (2010)
Price perceptibility
PPP3	The prices in this store are harder to discern here compared with other stores.	1.000	Zielke (2010)
Price processability
PPS1	Comparing the prices of different products requires a lot of effort in this store.	0.940	Zielke (2010)
PPS2	It takes a long time to find the most reasonably-priced product within a product group here.	0.968	Zielke (2010)
Evaluation certainty
EVC2	I cannot assess this store at all, regarding the prices.	0.894	Zielke (2010)
EVC3	I find it difficult to evaluate the prices in this shopping establishment.	0.899	Zielke (2010)
Price-related factors
Dispersion of price
DOP3	This store sometimes tries to offer prices as being cheaper than they really are.	1.000	Zielke (2006)
Price dynamics
PDM1	Products with a few very deep discounts was perceived to have a lower average price than a product with many small discounts.	0.806	Alba et al. (1999)
PDM2	Depth effect reversed by correcting biased estimates of promotion frequency (i.e., by increasing attention to the frequency brand’s promotions).	0.939	Alba et al. (1999)
Price-related policy
PRP1	I think the store offer a large refund depth.	0.916	Estelami et al. (2007)
PRP2	The retailer accepted my request using card (debit/credit or others).	0.696	Estelami et al. (2007)
Price-based communication
PBC2	I found the prices of reference products in all flyers before visiting the store.	0.911	Amara and Bouslama (2011)
PBC3	It takes time to find reasonably-priced products.	0.905	Amara and Bouslama (2011)
Non-price-related factors
Physical attributes
SAF1	The background music (in the video) would make shopping in this store pleasant.	0.867	Baker et al. (1994)
SDF2	The colours used in the store appeared to be currently fashionable.	0.787	Baker et al. (1994)
SDF4	The merchandise in the store appeared organized.	0.757	Baker et al. (1994)
Service level
SVL1	The employees at this store are fair to customers.	0.867	Estelami et al. (2007)
SVL2	Customers are well treated in this store.	0.910	Estelami et al. (2007)
Non-price policies
NPP1	The store often offers regular customers something extra (e.g., free gifts)	0.834	Chang and Wang (2014)
NPP2	The store often offers discounts (e.g., personalized coupon) to regular customers	0.851	Chang and Wang (2014)
NPP3	This store frequently offers products on sale	0.806	Chang and Wang (2014)
Consumer shopping intention
SIN1	I should shop at this store as often as possible	0.837	Zielke (2010)
SIN2	I should shop at this store as seldom as possible	0.709	Zielke (2010)
SIN3	I should consider this store for my shopping	0755	Zielke (2010)