Signals and willingness to purchase in services

Finally, brand investment plays it role in all three signal characteristics – clarity, consistency and credibility.. To bridge the aforementioned gaps, this study aims to apply signaling

Signals and Willingness to Purchase in Services

Nguyen Dinh Tho

University of Economics HCMC, Vietnam

Abstract

The purpose of this study is to employ a signaling framework to investigate the role of marketing signals

in customers’ willingness to purchase (WIP) a service brand Based on a sample of 297 online shopping

customers in Ho Chi Minh City, the findings produced by structural equation modeling show that brand

investment together with signal credibility positively affects WTP Signal clarity and consistency have a

positive impact on signal credibility; signal consistency underlies signal clarity Finally, brand investment

plays it role in all three signal characteristics – clarity, consistency and credibility The findings confirm the

net effects of brand investment and signal characteristics on WTP The results from necessary condition

analysis demonstrate that brand investment and signal credibility have varying levels of

necessity for the

occurrence of WTP Signal clarity, consistency and brand investment also exhibit different levels

of necessity

for the occurrence of signal credibility The study findings offer a number of implications for theory, research

and practice

Keywords Necessary condition analysis; Signaling theory; Services; Vietnam.

1 Introduction

Information asymmetry exists in a market when “different people know different things” (Stiglitz,

2002, p 469) A market is characterized by the asymmetry of information where providers know more about the quality of their offers than do their customers (Connelly, Certo, Ireland, & Reutzel, 2011; Kirmani & Rao, 2000) In such a market, signaling theory is useful and providers are able to use signals to inform their customers about the quality of offers (Mavlanova Benbunan-Fich, & Koufaris, 2012; Nguyen, Barrett, & Nguyen, 2014) Signaling theory, derived from information economics under the condition of asymmetric information (e.g., Spence, 2002; Stiglitz, 2002; Tirole, 1988), has been widely applied in business research over the past several years (Connelly, Certo, Ireland, & Reutzel, 2011; Kirmani & Rao, 2000) In marketing, signaling theory has been employed

by researchers to study various marketing issues such as brand equity (Erdem & Swait, 1998), warranty, product quality (e.g., Rao, Qu, & Ruekert, 1999), price and advertising (e.g., Caves & Greene, 1996), business relationships (e.g., Alsos & Ljunggren, 2017), online retail (e.g., Rao, Lee, Connelly, & Iyengar, 2018)

The service market is an information asymmetric market because service providers know the quality of their services better than do their consumers As such, signaling is valuable because signals help service providers to inform the quality of service brands to customers (Kirmani & Rao, 2000) However, little research has been devoted to explore the usefulness of signaling theory in services, especially in transitioning markets such as Vietnam In addition, quantitative marketing researchers have mainly employed conventional statistical tools such as multiple regression analysis and structural equation modeling (SEM) to test their theories Such conventional methods are

appropriate for researchers to test the net effects (i.e., beta weights) of independent variables on dependent variables, they are however unable to help researchers to discover the causal complexity

of social phenomena (Ragin, 2008) Fuzzy-set qualitative comparative analysis (fsQCA) assists researchers in examining sufficient and in-kind necessary conditions for an outcome but it does not

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support researchers in exploring the level of necessary conditions Necessary condition analysis (NCA; Dul, 2016a) helps researchers to investigate the degree of necessary conditions for the occurrence of an outcome

To bridge the aforementioned gaps, this study aims to apply signaling theory to investigate the role of brand investment and signal characteristics, including signal clarity, consistency and credibility, in customers’ willingness to purchase (WTP) a service brand Specifically, the study first uses SEM to investigate the net effect of brand investment and signal credibility on WTP and of brand investment, signal clarity and consistency on signal credibility The study then employs NCA

to discover the degrees of necessity of brand investment and signal credibility for the occurrence of WTP and the degree of necessity of brand investment, signal clarity and signal consistency for the occurrence of signal credibility The remainder of the paper is organized around the following key points: theoretical background and hypotheses, method, data analysis and results, discussion and implications, and conclusions, limitations and directions for future research

2 Theoretical background and hypotheses

Signals and signaling theory

Information asymmetry exists in a market when buyers and sellers are uncertain about the information concerning their exchange, or when one party owns more information than the other (Alsos & Ljunggren, 2017; Connelly et al., 2011; Kirmani & Rao, 2000; Wells, Valacich, & Hess, 2011) When faced with an information asymmetry problem, sellers and buyers will encounter a number of difficulties in making decisions For example, sellers of high quality products will be confronted with the positioning of their products (goods or services) against the products offered by low-quality sellers, and buyers may have trouble distinguishing a high-quality product from a low-high-quality one (Mishra, Heide, & Cort, 1998; Izquierdo & Izquierdo, 2007) Under such a market condition, several signals can serve as a signal of product quality, resulting in the application of signaling theory in several disciplines (Connelly et al., 2011; Kirmani & Rao, 2000) Rao et al (1999, p 259) define a signal as “an action that the seller can take to convey information credibly about unobservable product quality to the buyer.” It is a piece of information on the product that is able to assist sellers in showing the ability of their product to meet the requirements of their buyers and to differentiate the product from other offerings, and that helps customers to make inferences about the quality and value of the product (Heil & Robertson, 1991; Nguyen et al., 2014) The information to be conveyed can take a variety of forms such as the product’s attributes, prices, advertising, and warranties (e.g., Li, Fang, Wang, Lim, & Liang, 2015; Ozsomer & Altaras, 2008; Wells et al., 2011)

The quality of a product can be classified into three categories: search, experience and credence qualities (Darby & Karni, 1973) Search-quality products “are those that can be ascertained in the search process prior to purchase and experience-quality products are those that can be discovered only after purchase as the product is used Credence-quality products are those which, although worthwhile, cannot be evaluated in normal use Instead, the assessment of their value requires additional costly information” (Darby & Karni, 1973, pp 68-69) The literature on signaling theory suggests that signaling theory is highly applicable to experience-quality products because those products are characterized by a combination of high pre-purchase information scarcity and high post-purchase information clarity (Kirmani & Rao, 2000) In such a market condition, through a signaling process, sellers are able to reduce such

an information gap, confirming their buyers that they are selecting a high-quality product (Wells et al., 2011)

A signaling process involves three key elements: the signaler, the receiver and the signal itself Signalers are holders of or insiders concerning information about the unobservable (of a good or service) which is not available to outsiders (the receiver) Such information can be positive or negative; however, signalers primarily focus on the positive information with an attempt to convey positive attributes of the product or service (Connelly et al., 2011) Signaling is a learning process (Heil & Robertson, 1991) in which outsiders (the receiver)

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receive the signal, read and interpret it in the light of their experience, and react accordingly (Heil & Robertson, 1991) The detection and interpretation of a signal by the receiver depend on several characteristics of the signal; that is, signals have different levels of effectiveness (Li et al., 2015) Some signals may be easier to be detected and interpreted while others may not be A number of signal characteristics have been proposed in the literature such as signal clarity, intensity, observability, strength, visibility, consistency, credibility, and reliability Note that some of the above characteristics are closely related with others For example, clarity is similar to intensity, observability, strength and visibility; reliability has the same content with credibility (Connelly et al., 2011; Well et al., 2011) Accordingly, three key characteristics of a signal that help the receiver to detect and interpret the signal are clarity, consistency and credibility (Erdem & Swait, 1998; Tho, 2017) Signal clarity refers to the absence of ambiguity in the information conveyed by the signaler Signal consistency is reflected in the agreement among signals sent by the signaler Finally, signal credibility denotes the honesty of the signaler as well as the correspondence between the signal and the signaler’s quality, making the receiver to be confident in the signaler’s product claims (Connelly

et al., 2011; Erdem & Swait, 1998; Tho, 2017)

Signaling and WTP: Conceptual model

The service market is characterized by asymmetric information, which may create higher risks for customers because prior to actual purchase of a service brand, customers lack knowledge about the quality of the service; that is, pre-purchase information is scarce The quality of the service can only be evaluated when customers complete their consumption, meaning that the quality of the service is based on customers’ experience; that is, high post-purchase information clarity In such an experience-quality service (i.e., a combination of high pre-purchase information scarcity and high post-purchase information clarity), signaling theory

is appropriate, and service providers can use signals to inform their customers about the quality of their service brand (Kirmani & Rao, 2000; Wells et al., 2011) Signals can serve as a source of knowledge about service brands, reducing the information gap in the market (Nguyen

et al., 2014; Rao et al., 2018; Wells et al., 2011) Several marketing-mix elements, such as warranties, advertising, prices, brand names, can serve as signals These marketing signals convey information about the service brand to customers (e.g., Erdem & Swait, 1998) In such

a setting, customers would also like information that can help them to distinguish high quality service brands from low quality ones Therefore, marketing signals play an important role when consumers are not ready to evaluate the quality of a service brand before consumption

Figure 1 depicts a conceptual model explaining the role of signal characteristics and brand investment in WTP The model proposes that signal credibility and brand investment have a positive effect on WTP Signal clarity and consistency influence signal credibility and signal consistency affects signal credibility Finally, brand investment underlies all three signal characteristics (clarity, consistency and credibility)

Figure 1 Conceptual model

H3

H4

Signal clarity

H6

H5

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Signal credibility is the key characteristic of a signal because it determines the effectiveness of information conveyed (Tirole, 1988) When a market is characterized by imperfect and asymmetric information, it is important that a service provider should communicate credible information on its service brand to customers A credible signal can serve as a source of knowledge about the service brand, lessening customers’ perceived risk as well as costs of gathering and processing information (Erdem & Swait, 1998) Accordingly, if customers believe that the service brand’s signal is credible, i.e., the service provider is able and willing to deliver what is promised, they form a positive attitude toward the brand, leading to the willingness to purchase the brand In other words, when pre-purchase information is scarce, brand credibility is an essential factor that determines the choice of customers (Erdem & Swait, 1998; Kirmani & Rao, 2000; Tho, 2017)

H1 Signal credibility has a positive effect on WTP

Signal clarity assists customers in easily identifying what a service provider would like to inform its target customers, such as the brand’s attributes and position To make a signal clear, every marketing-mix signal should be consistent, i.e., reflecting the same attributes, objectives, position, and stable over time (Erdem & Swait, 1998) Therefore, signal consistency is essential

to signal clarity In addition, signaling theory suggests that most rational firms are unlikely to send false signals if the signals increase costs in terms of immediate profits, future profits, and reputation (brand image) (Tirole, 1988) As a result, signal clarity and consistency are vital to signal credibility because customers may believe that only quality service providers would send clear and credible signals to their consumers

H2 Signal consistency has a positive effect on signal credibility

H3 Signal consistency has a positive effect on signal clarity

H4 Signal clarity has a positive effect on signal credibility

A service provider should invest in its service brand to demonstrate that the firm commits to the brand, and to assure that its service claims are to be delivered (Erdem & Swait, 1998) Brand investment motivates customers to believe that the firm is willing and able to provide them the expected service leading to a higher level of WTP Further, high investment in the brand enables the service provider to send clear and consistent signals to its customers with an expectation of a return

in higher credibility perceived by customers Consequently, investment in a brand enhances the clarity, consistency, and credibility of the brand signal

H5 Brand investment has a positive effect on signal clarity

H6 Brand investment has a positive effect on signal consistency

H7 Brand investment has a positive effect on signal credibility

H8 Brand investment has a positive effect on WTP

3 Method

3.1 Sample and measure

A sample of 297 consumers in Ho Chi Minh City, the major business center in Vietnam, was surveyed to test the measurement and theoretical models Two key online-shopping service providers in Vietnam – Lazada and Home Shopping Vietnam – were selected for the study and face-to-face interviews, the most effective means of collecting survey data in Vietnam, was employed Note that this study aimed to investigate non-current customers of these two service providers, therefore, a screening question was used to select respondents who were not their current customers The sample comprised 102 (34.3%) female customers and 195 (65.7%) male customers

In terms of brand, 50 (50.5%) customers were interviewed with the Lazada questionnaire and 147 (49.5%) were interviewed with the Home Shopping Vietnam questionnaire

Constructs examined were brand investment, signal clarity, consistency, credibility and WTP The measures of these constructs were borrowed from the scales developed by Erdem and Swait (1998) Brand investments, signal clarity, and WTP were measured by three items each Signal consistency and brand

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credibility were measured by four items each All items were measured by a 7-point Likert scale, anchored by

1:strongly disagree and 7: strongly agree Note that the questionnaire was initially prepared in English and then translated into Vietnamese by an academic who is fluent in both languages

To confirm the equivalence of meanings between the two versions of the questionnaire (English and Vietnamese), a back translation was also conducted by another bilingual academic This procedure was undertaken because English is not well understood by most Vietnamese consumers Note also that the order of the questionnaire items was randomly distributed to reduce the agreement tendency bias (Chang, van Witteloostuijn, & Eden, 2010)

4 Data analysis and results

4.1 Measurement validation

To value the measures of the constructs under study, confirmatory factor analysis (CFA) with maximum likelihood estimation was employed The maximum likelihood estimation method was used because all the univariate kurtoses and skewnesses were within the range of [-1, 1] (Muthen & Kaplan, 1985) The CFA results of the final measurement model indicate that the model received an acceptable fit to the data: 2 (261.31)/df(106) = 2.47, GFI = 0.911, CFI = 0.942, and RMSEA = 0.070 All item loadings on the factors were significant and sufficient ( ≥ 0.55) Composite reliability (CR) and average variance extracted (AVE) of all constructs were acceptable (CR ≥ 0.67, AVE ≥ 0.58) These findings support the requirements for unidimensionality and within-method convergent validity (Steenkamp & van Trijp, 1991) In addition, the correlation of any pair of constructs was less than the root square of average variance extracted of each construct in the pair, supporting between-construct discriminant validity (Fornell & Larcker, 1981)

4.2 Common method bias

To control the possibility of common method bias due to the use of cross-sectional data collected from a single respondent (customers), this study used a number of procedures as follows As noted previously, in the design phase, the order of the questionnaire items was randomly distributed In the analysis phase, two statistical tests were conducted A CFA Harman’s single factor model test (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003) was first conducted, and then, an unmeasured latent variable test (allowing the unmeasured latent variable to load on each item in the trait model; Markel & Frone, 1998) followed

The CFA Harman test shows that the one-factor model received a very poor fit to data [2(966.09)/df(116) = 8.33, GFI = 0.715, CFI = 0.685, and RMSEA = 0.157], compared to the trait factor model [2 (261.31)/df(106) = 2.47, GFI = 0.911, CFI = 0.942, and RMSEA = 0.070] Further, the unmeasured latent variable test reveals that all CFA loadings of items measuring the constructs were not statistical significant and that these item-loadings were almost identical to those reported in the saturated CFA model The results of these two statistical tests indicate that if the common method variance existed, it did not make results of this study biased Note that no improper solutions were found in any results of the CFA tests (Heywood cases, i.e., negative variances, were absent; all standardized residuals were less than |2.58|)

4.3 Testing the net effect with SEM

SEM was employed to investigate the net effects of the three signal characteristics (clarity, consistency and credibility) and brand investment on WIP The results produced by SEM show that the theoretical model received an acceptable fit to the data [2 (262.98)/df(108) = 2.44, GFI = 0.910, CFI = 0.942, and RMSEA = 0.070] and that no improper solutions were found in the results of the SEM test (Heywood cases, i.e., negative variances, were absent; all standardized residuals were less than |2.58|) Table 1 demonstrates the unstandardized and standardized estimates of the structural coefficients

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