The determinants of green packaging that influence buyers’ willingness to pay a price premium

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The determinants of green packaging that influence buyers’ willingness to pay a price premium Gaganpreet Singh a,∗, Neeraj Pandey b a b

Department of Marketing, OP Jindal Global University, India Department of Marketing, National Institute of Industrial Engineering (NITIE), Vihar Lake, Mumbai 400087, India

a r t i c l e

i n f o

Article history: Received 12 March 2017 Revised 1 May 2018 Accepted 3 June 2018 Available online xxx Keywords: Green Packaging Price Premium Willingness to Pay

a b s t r a c t The study examined the impact of green packaging on consumer behaviour. It was measured through willingness to pay since it acts as a proxy for actual behaviour. Using a sample of 343 respondents, the study empirically confirmed the effect of six factors grounded from “theory of consumption values” and “customer value creation framework” that offered uniqueness to green packaging and influenced buyers’ willingness to pay a price premium. © 2018 Australian and New Zealand Marketing Academy. Published by Elsevier Ltd. All rights reserved.

1. Introduction This paper presents research into pre- and post-consumption of packaging through the lens of buyers’ willingness to pay a price premium for green packaging. Packaging is described as the fifth ‘P’ of the marketing mix (Nickels and Jolson, 1976). It protects, preserves and stores a product until it is consumed. It also satisfies the legal obligations of the manufacturer and conveys important brand messages to consumers. Packaging is classified in numerous categories, such as passive and active (Southgate, 1994), primary, secondary and tertiary (Vidales Giovannettri, 1995) and pre- and post-consumption (Prendergast and Pitt, 1996). The pre-consumption characteristics of packaging involve branding and graphics (Deng and Kahn, 2009; Orth and Malkewitz, 2008; Underwood and Klien, 2002). Post-consumption, however, packaging somewhat loses its functional importance and the improper disposal of used packaging causes significant environmental pollution. Companies are therefore exploring new designs to increase the post-consumption usage of those packaging materials. Packaging negatively affects the environment in three ways: by consuming resources, by generating solid, liquid, and gaseous waste and pollution and by spreading bacteria and pests (Zhang and Zhao, 2012). In 2016, Australians produced 50 million tonnes of waste, only 58% of which was recycled (MRA Consulting, 2016). As far back as 1992, Kassaye and Verma extended the literature by considering the issue of landfill sites becoming increasingly ex-

∗

Corresponding author. E-mail address: [email protected] (G. Singh).

hausted. Given the contribution that packaging makes to solid and liquid waste, consumers now desire packaging to be environmentally friendly or green. Increased consumer awareness has been demonstrated by the growing recognition of events such as EARTH DAY, support for activist organisations such as Greenpeace and the recognition of environmental safety programs. Various national and international bodies have passed environmental legislation to restrict the use of packaging. The European Union, for instance, has issued a series of directives detailing the need to reduce packaging waste and promote recycling. In another example, in September 2014, India banned the use of polyethylene terephthalate and plastic containers for the primary packaging of drugs.1 As consumers and legislative bodies across the globe, which constitute the major groups of stakeholders, have become more aware and more concerned about the negative environmental impact of packaging, scholars have been encouraged to investigate the trade-off between the marketing functions of packaging and the environment (e.g., Livingstone and Sparks, 1994; Prendergast and Pitt, 1996; Kassaye and Verma, 1992). Other research (Rokka and Uusitalo, 2008) found that green packaging was a vital criterion for consumer choice, thereby highlighting the increased importance consumers give to protecting the environment. The purpose of this paper is to understand buyers’ purchasing behaviour in relation to green packaging. While buyers’ purchasing behaviour is a complex construct that involves multiple stages (Darley et al., 2010; Hawkins et al., 2003; Engel et al., 1986), the

1 The Gazette of India, September 29, 2014, accessed on May 01, 2017, http:// www.cdsco.nic.in/writereaddata/GSR%20701%20(E)%20dated%2029_09_2014.pdf.

https://doi.org/10.1016/j.ausmj.2018.06.001 1441-3582/© 2018 Australian and New Zealand Marketing Academy. Published by Elsevier Ltd. All rights reserved.

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scope of this study was limited specifically to the actual purchase stage. Willingness to pay was used as a measurement of purchase behaviour as it was the closest approximation of actual behaviour (De Pelsmacker et al., 2005). Green packaging offers potential ‘uniqueness’ to any product (Agarwal and Rao 1996; Netemeyer et al., 2004; Wiedmann, et al., 2007) and its inclusion in any form influences buyers to pay a price premium (Kalra and Goodstein 1998; Netemeyer et al., 2004). Hence, the research objective was to find the conclusive factors that influenced buyers’ willingness to pay a price premium for green packaging. This paper is structured as follows. Section 2 covers the relevant literature review. It includes a brief about concepts investigated in this study. The variables capable of influencing buyer’s willingness to pay a price premium for green packaging are also tabulated. Section 3 covers research methodology explicating the design of questionnaire used in this study for data collection. The details about sampling, including sample size, sampling technique are also explained this section. Section 4 details data analysis, highlighting the results extracted from the collected data. Section 7 details testing of hypothesis followed by discussion and contribution this paper makes to the literature in Section 8 and 9. The paper concludes by showcasing limitations and implications drawn from the paper in Sections 10 and 11 respectively. The future outlook is detailed in Section 12.

2. Literature review In line with the research objective, the literature review was undertaken in two parts. The intention of the first part was to gain an understanding of the theoretical foundation of the three concepts used in this study, namely, willingness to pay, price premium and green packaging. These are covered briefly immediately below. The second part of the literature review was devoted to exploring aspects (variables) relevant specifically to green packaging that could influence buyers’ willingness to pay a price premium.

2.1. Part 1 2.1.1. Willingness to pay The willingness to pay concept originated in the pricing and consumer behaviour domain of marketing (Breidert et al., 2006). It accurately predicts buyers’ purchasing behaviour and ultimately assists organisations to develop their pricing strategies (De Pelsmacker et al., 2005). Breidert et al. (2006) revealed techniques that could be used to determine buyers’ willingness to pay. Their recommendations from a comparative analysis of willingness to pay techniques were considered when selecting the research methodology for this study.

2.1.2. Price premium A price premium is defined as the additional amount that (a) is paid over the average price and (b) represents improvements in the quality of a product or service (Rao and Bergen, 1992). In the economics literature, it was Klein and Leffler (1981) who formally started the discussion on price premium. Determining a willingness to pay a price premium can have significant impact on revenues and profits (Pandey et al., 2016, Marn et al., 2004). The literature review on price premium that Singh and Pandey undertook in 2015 led to their defining the numerous factors that may influence buyers’ willingness to pay a price premium (Singh and Pandey, 2015).

2.1.3. Green packaging “Green packaging or ecological packaging or environmentally friendly packaging is packaging that is completely made by natural plants, can be recycled or second used, prone to degradation and promotes sustainable development, even during its whole lifecycle, it is harmless to the environment as well as to the human body and livestock’s health” (Zhang and Zhao, 2012). Green packaging is associated with the “4R1D” principle, i.e., Reduce, Reuse, Reclaim, Recycle and Degradable. It is also described as “packaging made from eco-friendly/biodegradable/composite materials that can be broken down and assimilated by natural means back into common earth elements like carbon, oxygen and hydrogen” (Dharmadhikari, 2012). The existing literature extensively reviews buyers’ willingness to pay a price premium for green products. However, this study specifically considers green packaging. While all three of the concepts that are the focus of this study, i.e., willingness to pay, price premium and green packaging, have been widely researched in isolation, there is limited available literature on research into the three factors together. Among these few studies are those of Van Birgelen et al. (2009), who developed several hypotheses using the fundamental theories of consumer behaviour, including the work of Maslow (1967) on meta-needs, of Bem (1967) on self-perception, of Fishbein (1979) on reasoned action and of Ajzen (1991) on planned behaviour. The Van Birgelen et al study concluded that eco-friendly purchase and disposal decisions were related to environmental awareness and the eco-friendly attitude of the consumers. It implied that during the purchase process, buyers consider green packaging a vital attribute. This is largely due to the additional values that are offered by the characteristics of green packaging. According to the theory of value-based pricing, the increased perceived value encourages buyers to pay a price premium (Nagle et al., 2006). 2.2. Part 2 The second part of the literature review focused on determining holistically the relevant variables that influence willingness to pay a price premium for green packaging. A wide range of contexts that directly or indirectly affect a buyer’s behaviour were considered. The potential variables from the literature were also viewed through the lens of tangible and intangible values offered to buyers. Quality is one of the most widely researched characteristics for which buyers are willing to pay a price premium (Maguire et al., 2004; Rao and Bergen, 1992). Material quality is the foremost element that adds high differentiating value. Its property of being useful for the environment, humans and livestock carries the ability to sway a buyer’s eagerness to pay extra (Shang et al., 2010; Chuang and Yang, 2014; Dharmadhikari, 2012). Other environmentally friendly qualities that influence a buyer’s perception include recyclability, biodegradability and reclaimability (Zhang and Zhao, 2012). Design characteristics that make packaging reusable and resealable (Chuang and Yang, 2014; Kassaye and Verma, 1992) were the focus of this part of the literature review because such attributes give packaging multi-utility properties and multiple usage significantly reduces waste. The authors found potential for green packaging in the properties described by Prendergast and Pitt (1996) that improve shelf life and described by Kassaye and Verma (1992) to keep products leakproof and airtight. It is desirable qualities such as these that are likely to influence buyers’ willingness to pay a price premium. The impact of packaging information on consumer decisionmaking has been reviewed by various scholars (Thogersen, 1995;

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Table 1 Variables that influence willingness to pay a price premium for green packaging. S. no

Notation

Variable

Reference

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

Esp Rec Bio HH RecL SL CheRec CC LH LP AT Esl Ese Di SvsUn Espm Reu Res PacSiz Sp Eod Rw Mp Me Pn Pp Ga

Environmentally safe packaging material Recyclable Biodegradable Human health Reclaimable Shelf life Does not chemically react with the inside product. Climate conditions Livestock health Leak proof Airtight Environmentally safe labels Environmentally safe endorsements Disposal instructions Safe vs. unsafe for the environment Environmentally safe printing material Reusability Resealability Packaging size Packaging simplification Ease of disintegration Reducing waste Minimum pollution Minimum energy Personal norms Peer pressure Green advertisements

Shang et al. (2010), Zhang and Zhao (2012) Zhang and Zhao (2012), Chuang and Yang (2014), Dharmadhikari (2012) Zhang and Zhao (2012), Chuang and Yang (2014), Dharmadhikari (2012) Zhang and Zhao (2012), Chuang and Yang (2014) Zhang and Zhao (2012), Chuang and Yang (2014) Prendergast and Pitt (1996) Kassaye and Verma (1992) Kassaye and Verma, 1992 Zhang and Zhao (2012) Kassaye and Verma (1992) Kassaye and Verma (1992) Chuang and Yang (2014) Expert views Kassaye and Verma (1992) Thogersen (1995), Smallbone (2005) Expert views Chuang and Yang (2014), Kassaye and Verma (1992) Chuang and Yang (2014) Expert views Chuang and Yang (2014) Chuang and Yang (2014) Shang et al. (2010) Shang et al. (2010) Shang et al. (2010) Bech-Larsen (1996), Van Birgelen et al. (2009) Bech-Larsen (1996) Spack et al. (2012), Purohit (2012)

Smallbone, 2005). Sonsino (1990) and Hine (1995) stated that packaging acts as a silent salesman. This was supported by Vidales Giovannettri (1995), who said packaging was the first aspect of the product that buyers see before making a purchase decision, especially since the great majority (73%) of purchase decisions were made at the point of purchase (Connolly and Davidson, 1996). Similarly, Underwood et al. (2001) stated that the ability of packaging to influence a customer’s perception of how the product inside looks, tastes, feels, smells or sounds largely affects purchase decisions. Another factor was the cutback in conventional mass media advertising budgets and an increased focus on packaging to create differentiation (Semenik, 2002), which in turn can have an impact on memory-based perceptions of quality and thus enhance brand equity (Warlop et al., 2005). The other aspect of information that was likely to affect green behaviour includes green advertising (Spack et al., 2012; Purohit, 2012). Added to these are such factors as people’s individual beliefs regarding the environment that can have a positive impact on their purchase decision-making (Albayrak et al., 2013, Pandey and Kaushik, 2012; Khare, 2014) and the technology and processes used to manufacture green packaging (even though buyers would have no direct access to such knowledge) (Shang et al., 2010). The variables collected from the literature were vetted with a panel of experts that included an academic professor who was also working as a pricing consultant, a practising packaging professional and research scholars from an environment management background (see Section 3). It helped in adding newer variables into the mix of considerations, such as environmentally safe endorsements, environmentally safe printing materials and packaging size. Table 1 lists the potential variables that confer uniqueness to green packaging and are therefore likely to impact buyers’ willingness to pay a price premium for it. The variables are grounded in a review of the literature from various fields, namely, marketing, packaging design, advertising and pro-environment behaviour, as well as input from the academics and expert practitioners referred to in the previous paragraph. To find the potential determinants influencing buyers’ willingness to pay a price premium, data was

gathered through the recommended survey technique detailed below (Breidert et al., 2006).

3. Research methodology The paper incorporated both exploratory and conclusive phases. The exploratory phase was conducted primarily for back-ground study and questionnaire development. However, conclusive phase involved data collection from actual respondents through a structured questionnaire.

3.1. Formulating the questionnaire The comprehensive exploration of available literature on items capable of influencing buyers’ willingness to pay a price premium for green packaging acted as basis for questionnaire development. This exploration revealed 41 items that could be included in the ultimate model for the willingness to pay a price premium. The list was shown to a team of academic and practicing professionals mentioned previously to support face and content validity. The expert group included two industry experts (one who was involved in the packaged goods business and another in the manufacturing of packaging material), one professor who was also a consultant and trainer in the area of pricing and three research scholars who had majored in environmental management. The experts jointly supported the elimination of 16 items that had common or overlapping characteristics and recommended the addition of three others. The preliminary questionnaire was assessed on a sample of 25 respondents to determine the appropriateness of the language and sentence structure. This exploratory stage resulted in the questionnaire having 27 verified items related to green packaging that could influence buyers’ willingness to pay a price premium. The answers to each of the items were documented on a 7-point Likert scale with 1 representing strong disagreement and 7 depicting strong agreement. None of the statements were negatively worded, hence no reverse coding was used.

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3.2. Data collection and sampling The purpose of the study was to understand buyers’ attitudes regarding their willingness to pay a price premium for green packaging. Because the study required direct interaction with the customers, a direct survey technique, i.e., a customer survey, was considered to be appropriate (Breidert et al., 2006). The option to use other measurement techniques, specifically, indirect surveys, market data and experiments, were determined to be inappropriate due to the nature of the research objective (Breidert et al., 2006). In addition, the direct survey technique was more cost effective and time efficient (Breidert et al., 2006). The data were recorded from a sample size of 343 people, thereby satisfying the minimum threshold required to perform CFA (Boomsma, 1985; Bentler and Chou, 1987; Bollen, 1989). Northern India was used as the sampling frame. Individuals who are acquainted with the knowledge and importance of the ‘green’ concept served as the population of the study and it was ensured that the respondents had purchased a product with environmentallyfriendly packaging up to but no more than 90 days before completing the questionnaire. For this study, glass was considered to be a green packaging material because it is made from natural resources such as silica (silicon dioxide), can be recycled and retains its properties even after multiple rounds of recycling. Plastic was eliminated as a potential product choice to investigate because it is environmentally unsafe and its properties deteriorate every time it is recycled. The snowballing sampling technique enabled us to reach potential respondents with great accuracy. Women formed 38% of the sample size.

Table 2 KMO and bartlett’s test. Kaiser–Meyer–Olkin measure of sampling adequacy Bartlett’s test of sphericity Approx. chi-square Df Sig.

0.794 4003.151 300 0

correlated with all the other variables, were verified. Low communalities (0.0–0.4) indicated that the variables do not load fittingly and were removed from further analysis. None of the variables had communalities of less than 0.4. As proposed by Johnson and Wichern (1982), only those factors having Eigen Value greater than 1 were extracted. Subsequently, the two-thumb rules that were proposed by Gefen and Straub (2005) were adopted. The first rule states that weights or loadings that are below 0.40 may not be considered. The second rule states that for independent variables with loadings on multiple factors greater than 0.40, the variation of 0.1 or more must prevail among the highest loading on one factor and the second highest loading on a different factor. The independent variables that did not pass these two screening rules were negated in the subsequent analysis. EFA condensed 27 variables into seven dissimilar factors. However, one of the items, “green advertisements”, loaded individually only on one factor. Considering the lack of theoretical support and requirements and/or constraints for conducting CFA, the factor was removed. At the end of this process, the six extracted factors explained 65.243% of the variance (Table 3). 5.2. Factor labelling

4. Data analysis The research objective was to find the conclusive factors that influenced buyers’ willingness to pay a price premium for green packaging. This required the list of variables to be reduced empirically until eventually arriving at the factors that did influence buyers’ willingness to pay positive price differential for green packaging. The variable reduction was accomplished through exploratory factor analysis (EFA). In addition, the data were confirmed through confirmatory factor analysis (CFA) to further strengthen the results. Finally, grounded by the confirmed factors, the hypotheses were tested using binary logistic regression because the dependent variable (willingness to pay a price premium) was captured in binary form and the independent variables were on a Likert scale. 4.1. Appropriateness of the data for factor analysis A preliminary data screening activity was conducted to determine the appropriateness of the data. The scope of this activity included gaining an understanding of the correlation analysis output, inspecting the KMO test for sampling adequacy and Bartletts’ test of sphericity (Malhotra, 2004). One of the variables, “Airtight”, had a Pearson correlation that was equal to 1 and this item was removed from further analysis (O’brien, 2007). The KMO measure of sample adequacy was 0.792, which satisfied the threshold of 0.5. All the other results confirmed that factor analysis could proceed. 5. Exploratory factor analysis 5.1. Factor extraction EFA was conducted using principal component analysis. The revised KMO measure of sample adequacy was 0.794, which is above the threshold of 0.5 (Table 2) (Hair, 2010). The communalities linked with each variable, that is the extent to which they

Factor 1: The first factor contained six variables. These included environmentally safe packaging, recyclable, biodegradable, does not affect human health, does not affect livestock health, and reclaimable. It explained 17.948% of variance. All of these variables represented the novel value offered by green packaging that influences buyers’ willingness to pay a price premium. The nomenclature for this factor was kept as epistemic value, which refers to the novelty value derived from a product (Pihlstrom and Brush, 2008). Factor 2: The second factor contained five variables. It included reusability, resealability, packing with respect to size, simplified packaging, and ease of disintegration. It explained 12.17% of the variance. These variables increase buyers’ perceived value of the object because of the property of multiple uses or functions. Grounded in the “theory of consumption values” (Sheth et al., 1991), this factor was called functional value. Sheth et al. (1991) defined functional value as “the perceived utility acquired from an alternative’s capacity for functional, utilitarian, or physical performance. An alternative acquires functional value through the possession of salient functional, utilitarian, or physical attributes”. Factor 3: The third factor included four variables, specifically resistant to climate change, improves shelf life, leak proof, and ability to protect product properties upon chemical interactions. It explained 10.715% of variance. These variables highlight the value offered by green packaging that can be measured. Hence, this factor was called economic value, since economic value refers to the monetary benefits that buyers derive from a product (Nagle et al., 2006). Factor 4: The fourth factor combined five variables that showcase the distinct self-identity dimensions often concomitant with green packaging. The variables are environmentally safe labels, environmentally safe endorsements, disposal instructions, indication of safe versus unsafe, and environmentally safe printing material. Grounded in the framework of customer value creation (Smith and Colgate, 2007), this factor was called Symbolic Value (Park et al., 1986).

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Table 3 Total variance explained. Component

1 2 3 4 5 6

Initial Eigen values

Rotation sums of squared loadings

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

4.985 3.635 2.554 1.901 1.824 1.412

19.938 14.54 10.216 7.605 7.295 5.649

19.938 34.478 44.693 52.299 59.594 65.243

4.487 3.043 2.679 2.345 2.136 1.621

17.948 12.17 10.715 9.381 8.544 6.485

17.948 30.118 40.833 50.214 58.758 65.243

Factor 5: The fifth factor contained two variables, highlighting the role of personal norms and peer pressure. It explained 8.544% of the variance. These variables represent an individual’s personal desire to pay a price premium for green packaging, and as such they represent what was called Altruistic Value (Degroot and Steg, 2008). Factor 6: The last factor in this extraction combined the variables whose benefits end customers might not experience, without discounting their orientation towards protecting the environment, which influenced them to pay a price premium for green packaging. These variables include the ability of green packaging to reduce waste, to discharge a minimum level of pollution, and to consume minimum energy. The academic literature on the environment refers to this dimension as biospheric value. An individual with a biospheric value orientation “will mainly base their decision to act pro-environmentally or not on the perceived costs and benefits for the ecosystem and biosphere as a whole” (Degroot and Steg, 2008). Table 4 represents a snapshot of factor labeling and loading. 5.3. Reliability and validity of EFA output The reliability and validity of the factors were studied statistically. Cronbach’s alpha metric was used to estimate reliability (Table 5). All of the individual factors met the threshold criterion of 0.7 (McCrae et al., 2010). The convergent validity was investigated by monitoring the variance extracted. It was calculated by adding the squared factor loadings for all of the variables that constituted a factor and dividing total sum by the number of variables. All the factors surpassed the minimum threshold value of 0.5. The discriminant validity was confirmed by monitoring the factor loading of each variable under all the factors to ensure that each factor was different from every other. 5.4. Common Method Bias (CMB) Harman’s single factor test was used to examine the CMB of the sample. By fixing the factor extraction to one, the total variance explained was only 19.938, which is less than the threshold (Podsakoff et al., 2003). 5.5. Non-response bias The standard methodology that was suggested by Armstrong and Overton (1977) was used to check the non-response bias. They recommended that the last quartile or second wave of respondents must be similar to the first wave of respondents. A T-test was performed on both of the respondent sets. At a 5% significance level, there was no significant difference. This revealed that a nonresponse bias was not a concern in the study. 6. Confirmatory factor analysis The EFA model represents six factors (F1, F2, F3, F4, F5 and F6) that influence buyers’ willingness to pay a price premium for green

Table 4 Factor labelling and loading. F1-Epistemic value Notation

Variable

Loading

Esp Rec Bio HH LH RecL

Environmentally safe packaging Recyclable Biodegradable Does not affect human health Does not affect livestock health Reclaimable

0.883 0.733 0.837 0.857 0.851 0.874

F2-Functional value Notation

Variable

Reu Res PacSiz Sp Eod

Reusable Resealable Packing w.r.t. size Packaging simplification Ease of disintegration

Loading 0.673 0.596 0.735 0.897 0.85

F3-Economic value Notation

Variable

CC SL LP CheRec

Resistant to climate change Improves shelf life Leakproof property Does not alter product properties through chemical process

Loading 0.68 0.69 0.831 0.856

F4-Symbolic value Notation

Variable

Loading

Esl Ese Di SvsUn Espm

Environmentally safe labels Environmentally safe endorsements Carrying disposal instructions Indicating safe vs. unsafe Environmentally safe printing material

0.461 0.566 0.454 0.835 0.889

F5-Altruistic value Notation

Variable

Pn Pp

Personal norms Peer pressure

Loading 0.831 0.801

F6-Biospheric value Notation

Variable

Loading

Rw Mp Me

Reduces waste Minimises pollution Consumes minimum energy

0.769 0.843 0.79

Table 5 Reliability of factor output. Factor no

Factor name

Reliability (Cronbach alpha)

F1 F2 F3 F4 F5 F6

Epistemic Value (EPV) Functional Value (FV) Economic Value (ECV) Symbolic Value (SV) Altruistic Value (AV) Biospheric Value (BV)

0.92 0.82 0.79 0.72 0.68 0.77

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packaging. Each factor groups the related variables. The model was validated through CFA using AMOS 20.0.

Table 6 CFA results. Model fit Summary CMIN

6.1. CFA results and model fit The initial model had 31 exogenous variables and 25 endogenous variables. The first minimum discrepancy/degree of freedom (χ ²/df) was 2.532. The other goodness-of-fit indices, AGFI and CFI, had values of 0.828 and 0.90, respectively. The root mean square error of approximation (RMSEA) was .067. The values met the threshold for goodness-of-fit statistics (Hu and Bentler, 1999). The standard regression weight of one of the items, “Labels”, was less than 0.4, hence it was removed from further analysis. Once the “Labels” was eliminated, (χ ²/df) improved to 2.423. The AGFI and CFI indices were 0.842 and 0.909 respectively and the RMSEA was reduced to 0.064.

6.2. Modification indices and changes in iterative phase

Model

NPAR

CMIN

DF

P

CMIN/DF

Default model Saturated model Independence model

67 300 24

428.875 0 3987.301

233 0 276

0

1.827

0

14.447

Model

RMR

GFI

AGFI

PGFI

Default model Saturated model Independence model

0.109 0.0 0 0 0.423

0.913 1.0 0 0 0.431

0.886

0.704

0.382

0.397

Model

NFI

RFI

IFI

TLI

Default model Saturated model Independence model

Delta1 0.892 1.0 0 0 0.0 0 0

rho1 0.881

Delta2 0.952 1.0 0 0 0.0 0 0

rho2 0.942

RMR, GFI

Baseline Comparisons

0.0 0 0

0.0 0 0

CFI 0.952 1.0 0 0 0.0 0 0

RMSEA

Although the goodness-of-fit statistics provided satisfactory results, the model fit was further refined through modification indices. The results revealed four notable changes that could further improve the model fit. The adjustments involved covering the error terms that were associated with the same factors. The four iterations improved the model fit and other associated goodness-offit indices, reducing (χ ²/df) to 1.841. The other goodness-of-fit indices, namely GFI, AGFI and CFI improved to 0.907, 0.880 and 0.947, respectively. The RMSEA was reduced to 0.049 (Hu and Bentler, 1999). After the goodness-of-fit indices and model fit were evaluated, the convergent and discriminant validity of the data were checked. The evaluating metrics included composite reliability (CR), average variance extracted (AVE), maximum shared variance (MSV) and average shared variance (ASV). The CR of all of the extracted factors produced significant (above 0.7) results. With AVE < 0.5, the convergent validity showed concerns for one factor (Informative). The discriminant validity explained satisfactory results as MSV < AVE and ASV < AVE for all of the factors (Hair, 2010) 6.3. Final analysis In overseeing issues in the convergent validity, the standardised regression weights that correspond to each variable were checked. One of the variables (endorsements) that was linked to the Informative factor was removed due to a loading equivalent to 0.437. The final model had 29 exogenous variables and 23 endogenous variables. The χ ²/df was reduced to 1.827. The goodness-of-fit indices GFI, AGFI and incremental fit index had values that were close to 1.0 0 0 (GFI = 0.913, AGFI = 0.886, IFI = 0.952). CFI, RFI and TLI had values that were close to 0.95 (CFI = 0.952, RFI = 0.881, TLI = 0.942). The RMSEA value was less than 0.05 (0.049). Both the 0.05 and 0.01 values of Hoelter’s Critical N for the hypothesised model exceeded 200 (0.05 = 221, 0.01 = 235). All of these results showed a good model fit (Hair, 2010; Hu and Bentler, 1999). Table 6 details the complete CFA results.

Model

RMSEA

LO 90

HI 90

PCLOSE

Default model Independence model

0.049 0.197

0.042 0.192

0.057 0.203

0.554 0.0 0 0

7. Testing of hypothesis Based on the output of CFA, the following hypotheses were proposed: H1: The epistemic value of green packaging has a positive influence on a buyer’s willingness to pay a price premium. H2: The functional value of green packaging has a positive influence on a buyer’s willingness to pay a price premium. H3: The economic value of green packaging has a positive influence on a buyer’s willingness to pay a price premium. H4: The symbolic value of green packaging has a positive influence on a buyer’s willingness to pay a price premium. H5: Altruistic values influence a buyer’s willingness to pay a price premium for green packaging. H6: Biospheric value orientation influences a buyer’s willingness to pay a price premium for green packaging To test the hypotheses, a binary logistic regression model was used. This was because both the dependent variable (whether a buyer is willing to pay a price premium for green packaging) was in binary form, and the independent variables (variables that encourage buyers to pay a price premium for green packaging) were captured using a seven-point Likert scale.

p = ea+b1 x1 +b2 x2 1− p where • •

p stands for the odds of a particular event. x1 , x2 are the variables that determine the odds of occurrence of that event.

6.4. Convergent and discriminant validity of final model

8. Results

The composite reliability (CR) of all of the factors highlighted values that were above 0.7. For the convergent validity, the AVE values matched the threshold of 0.5 (Table 7). The discriminant validity showed satisfactory results, MSV < AVE and ASV < AVE (Hair, 2010).

The hypotheses (H1, H2, H3, H4, H5 and H6) stand validated, as the coefficient that corresponds to each of the factors was positive and significant (Mai et al., 2010). The results (shown in Table 8) reveal that all of the factors were significant, and hence all of the proposed hypotheses are accepted. The binary logistic regression

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Table 7 Reliability, convergent and discriminant validity.

AV EPV FV ECV SV BV

CR

AVE

MSV

ASV

AV

EPV

FV

ECV

SV

BV

0.69 0.92 0.82 0.79 0.79 0.77

0.53 0.66 0.5 0.5 0.56 0.53

0.04 0.11 0.03 0.03 0.03 0.11

0.03 0.04 0.01 0.01 0.02 0.03

0.73 0.21 0.17 0.09 0.17 0.19

0.81 −0.02 0.09 0.16 0.33

0.71 0.04 0.14 0.07

0.71 0.17 0.09

0.75 0.07

0.73

Table 8 Results. Variables in the equation

Step 1

B

S.E.

Wald

df

Sig.

Exp (B)

AV BV SV ECV FV EPV Constant

2.133 1.199 5.691 3.075 4.736 2.878 –41.869

0.602 0.443 1.235 0.582 0.873 0.533 6.867

12.552 7.316 21.218 27.91 29.434 29.113 37.174

1 1 1 1 1 1 1

0.0 0 0 0.007 0.0 0 0 0.0 0 0 0.0 0 0 0.0 0 0 0.0 0 0

8.438 3.316 296.089 21.65 113.991 17.774 0

Hosmer and Lemeshow test Step

Chi-square

df

Sig.

1

0.837

8

0.999

Model summary Step

−2 Log likelihood

Cox and Snell R Square

Nagelkerke R square

1

84.388

0.501

0.823

for the full sample was:

log

 p  1− p

= −41.869 + 2.133 ∗ AV + 1.99 ∗ BV, +5.691 ∗ SV + 3.075 ∗ ECV + 4.736 ∗ FV + 2.878 ∗ EPV

The equation represents the impact of one unit increase of all factors on log odds for willingness to pay a price premium. The Nagelkerke (R2 ) = 0.82 for the above fitted model indicates that it was a good fit for the model. One unit increase in SV created a maximum influence by increasing 5.691 units in the log odds for willingness to pay a price premium, while a one unit increase in BV gave the lowest impact. However, the roles of AV and EPV were comparable. Therefore, it is the complementary and synergistic effects of the six distinct but highly inter-related values that influence buyers’ willingness to pay a price premium for green packaging. 9. Contribution and discussion The outcome of the empirical analysis revealed six factors that influenced buyers’ willingness to pay a price premium for green packaging. These included epistemic value, functional value, economic value, symbolic value, altruistic value and biospheric value. Each factor was comprised of several variables (refer Table 4). The cohesiveness of the variables forming a factor was measured through the metrics of CR and AVE. Meeting the threshold value supported the claim of high correlation among the variables forming a factor (refer Table 8). In addition, the acceptable values of MSV < AVE and ASV < AVE (discriminant validity) strengthened the claim that buyers identified the distinct role of each factor (value) in influencing buyers’ willingness to pay a price premium for green packaging.

The six factors proposed in this study are theoretically supported by the theory of consumption values (Sheth et al., 1991) and customer value creation framework (Smith and Colgate, 2007; Pihlstrom and Brush, 2008). The results support and extend the theoretical findings of Mishra et al. (2017), which examined the relationship of three variables, namely “concern for environment, knowledge about green packaging and beliefs about positive consequences of using green packaging with consumer attitude towards paying price premium”. It claims the existence of a positive relationship between beliefs, consequences and attitude towards paying a positive price differential. The knowledge about green packaging was found to play a vital role in developing positive beliefs about green packaging. The six factors proposed in this current research may act as specific pointers to understanding the uniqueness conferred by green packaging. Further, highlighting the factors through the nomenclature of different consumption values throws light on some of the differentiating values possessed by green packaging that eventually influence buyers willingness to pay a price premium (Nagle et al., 2006). The current findings delve deeper into the results of Guyader et al. (2017), which highlight several practices to influence buyers’ willingness to pay a price premium. Amongst many, some of these include “providing relevant information” to the buyers. The proposed six factors, and specifically the symbolic value (SV), may act as one of the dimensions of “information” to persuade buyers’ positive price differential. Further, SV explains the importance of related but distinct variables such as environmentally safe labels, environmentally safe endorsements, disposal instructions, highlighting safe vs. unsafe packaging and printing material in persuading buyers to pay a price premium. The results corroborate the theoretical findings of Magnier and Schoormans (2017) who propose that the credibility of a brand increases when environmental

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claims are displayed on the package and packaging material, thus in turn influencing a willingness to pay a price premium (Erdem et al., 2002). The significance of factor AV in influencing buyers’ willingness to pay a price premium correlates with the recommendations of Prakash and Pathak (2017), who stressed the role of personal norms in influencing people’s intentions to purchase green packaged products, specifically in the Indian context. This relationship indicates an association of individual and social consequences with purchasing a green product, indicating further the significance of BV. The relationship further highlights that buyers’ purchasing decision are swayed both by the need for personal satisfaction and a moral accountability towards the environment. This behaviour indicates an environmentally-friendly lifestyle in their consumption patterns and the importance of ‘being green’. The proposed factors in the current study support the claims reported in previous studies in the domain of pro-environment behaviour (Ha and Janda, 2012). They further provide statistical substantiation of factor FV in the role of packaging design and related variables that influence buyers’ willingness to pay a price premium. The existing literature reports price sensitivity among Indian buyers in relation to developed economies (Yadav and Pathak, 2016). However, the results from the current study indicate that buyers are willing to pay a price premium for green packaging. The results imply that Indian buyers who acknowledge being “green” as a differentiating value are also prepared to accept increased prices. The results also corroborate the findings of Pires et al. (2015), which reveal that buyers are willing to pay a premium for green packaging. Therefore, price no longer operates as an obstacle because the environmental benefits are acknowledged (Gleim and Lawson, 2014). Since a snowball sampling technique was used, the emphasis was on the total sample size, rather than being split according to gender. Nor did the exploratory phase of this study (expert reviews) produce any evidence that gender played an important role in the purchase of items with green packaging in any given region.

10. Limitations As per the standard protocol, every effort was made at the research design phase to produce valid and reliable results. Nevertheless, the study had several limitations. One of the major constraints was that the research used the same data for exploratory and confirmatory factor analyses. The literature (Anderson and Gerbing, 1988; Kelloway, 1995) articulates that this strategy is commonly followed because it streamlines research activities (Huber and Power, 1985). Because the research design involved a cross-sectional survey method, the causal inferences of the green packaging attributes with willingness to pay a price premium were derived from existing theory and research. This is undoubtedly a debated method; however, Cook and Campbell (1979) revealed that it is suitable to simplify the theory. Another limitation of our study pertains to our specific results. Any research that is connected with construct development and validation strives to examine the degree of association between the results that are obtained using a respective measurement tool and to allocate the meaning that is attributed to those results. The valid statistical results that are obtained from CFA offer convincing evidence for the factor structure. However, the factor structure needs to be reassessed in future research. Successful replication would permit researchers to exclude and considerably shorten the instrument. This is consistent with Boyer and Pagell’s (20 0 0) recommendation to remove lower-level variables when they are incorporated in higher-level constructs. The extracted factors correspond to buyers’ willingness to pay a price premium for green packaging in

general. The factor structure is subject to change in relation to specific product categories such as consumables and durables. One other limitation with the sampling technique was a concern related to selection bias and gatekeeper bias. The interpretations that are presented in the research come with a word of caution. The claims made are valid for the selected geographic regions that were used for data collection and for generalisation must be validated beyond these borders. The proposed effort is critical for studies that specifically involve willingness to pay. Because willingness to pay is distinct from ability to pay, testing the results in different demographics would therefore provide sufficient evidence to generalise the theoretical contribution. 11. Implications The study has vital implications for practitioners in the Indian context. The worldwide market for green packaging was projected to touch $244 billion by 2018, with Asia being the biggest contributor by accounting for 32% of the total market (Allen and Pira, 2014). This increased demand for green packaging in Asian countries like China and India was due to the rising middle-class gaining increased purchasing power and awareness of health and environmental concerns. However, the hurdles associated with green packaging have been mounting and over the years they have emerged as one of the major challenges facing companies, above cost and other issues (Radhakrishnan, 2016). Amongst many, one of the pressing issues relates to buyers price sensitivity towards green packaging. The results from this study offer insights and improve understanding about buyers’ willingness to pay a price premium for green packaging, when “green” is acknowledged as a differentiating factor. Marketing practitioners and the multinationals can benefit by designing attractive and effective marketing strategies to educate their consumers about green packaging. In light of the green benefits, marketers are advised to pay special attention to the six values conferred by green packaging. To that aim, they could craft some creative promotion schemes that showcase the benefits of green packaging for the environment and establish drifts towards green consumption patterns. Developing green packaging entails financial investments. In light of this, it is vital to convey the uniqueness conferred by the package. Through improved customer understanding of the differentiating features of green packaging, companies could convert their investment in sustainability to a profit through the price premiums paid by their customers for their green initiative. The fact that symbolic value on the packaging can improve willingness to pay a price premium for a brand highlights a new contribution that should be examined by brand managers. Further, it is worth noting that design-related dimensions that also reflect functional value influence willingness to pay. This last phenomenon may be of importance to product managers seeking to enhance the value of their product through the choice of packaging design. 12. Future outlook Green packaging has appeared as an answer to numerous environment-related concerns. There is growing discussion among consumers about environmental issues which is compelling manufacturers to produce more environmentally-friendly products. However, manufacturers often fall back on the idea of introducing green packaging infrastructure and/or activities to their operations. Kassaye and Holloway (2015) categorised such concerns as follows: (a) is the risk of the ongoing expenses of green packaging worth taking? (b) can the cost of green packaging be reasonably contained? (c) can the additional expenses that are associated with green packaging be recouped in a reasonable time period?

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