Examining sustainability surcharges for outdoor apparel using Adaptive Choice-Based Conjoint analysis

Brand, Benedikt M.; Rausch, Theresa Maria

doi:10.1016/j.jclepro.2020.125654

Cited by 25 publications

(44 citation statements)

References 112 publications

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“…Up until now, there have only been few studies looking into consumer decisions, behavior and intentions regarding the consumption of green apparel or bio-based products (Hustvedt and Bernard, 2008;Hustvedt and Dickson, 2009;Niinimäki, 2010;Koszewska, 2013;Austgulen, 2016;Nam et al, 2017;Scherer et al, 2017Scherer et al, , 2018aKlein et al, 2019Klein et al, , 2020, even though consumer preferences and choices are an essential factor when it comes to developing innovative and sustainable products (Austgulen, 2016). Brand and Rausch (2020) found that very green consumers tended to prefer the influence of sustainabilityrelated attributes, such as materials, labels or country of origin, while price was by far the most influential driver for less green consumers when choosing bio-based outdoor apparel. Furthermore, Herbes et al (2018) revealed that consumers are less concerned with renewable origins and almost not concerned at all with activities related to production, transportation and retail use.…”

Section: Introductionmentioning

confidence: 99%

“…Hustvedt and Bernard (2008) also found that participants in an experimental auction displayed a greater willingness to pay for organic socks. Additionally, several studies showed an increase in utility of medium prices of bio-based products in particular for consumers with more environmentally friendly attitudes (Scherer et al, 2018b;Brand andRausch, 2020, Niedermeier et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Consumer Preferences in Germany for Bio-Based Apparel With Low and Moderate Prices, and the Influence of Specific Factors in Distinguishing Between These Groups

2021

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Bioplastics are a potential alternative to conventional plastics when it comes to reducing the use of the fossil resource of crude oil. There have been so far very few studies on the consumption of bio-based apparel and consumer preferences relating to it. Within this study, an online survey was conducted with 1,673 participants that is representative of the German population over the age of 16, including a choice-based consumer experiment with a bio-based rain jacket. Using latent class analysis, two clusters of consumers (39% of respondents) were identified with acceptance of moderate prices for such bio-based products. The other 61% of consumers showed preferences for the lowest prices of the tested bio-based rain jacket. A discriminant analysis revealed that the intention to purchase bio-based apparel, the Green Consumer Value and subjective norms of bio-based apparel were the strongest significant factors when distinguishing between consumer groups of bio-based apparel with a “preference for a low price” and those showing “acceptance of a moderate price.” This study helps to better illuminate the heterogeneity of consumer preferences for bio-based apparel related to pricing strategies and supports building up of a supply chain for such products.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Consumer Preferences in Germany for Bio-Based Apparel With Low and Moderate Prices, and the Influence of Specific Factors in Distinguishing Between These Groups

2021

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“…Additionally, the decision heuristics applied by consumers comparing different products may alter the COO impact. While this study has focused on decisions related to finding the most preferred option from the choice set, which assumes compensatory heuristics, forming the consideration set follows non-compensatory heuristics (Brand and Rausch, 2021).…”

Section: Limitations and Future Researchmentioning

confidence: 99%

“…Although some more recent studies investigating the COO effect using forms of conjoint analysis exist, none has yet shed light on the influence of the COO effect in online shopping. Apart from studies examining consumer preferences by applying conjoint analysis with COO playing a subordinate role (inter alia Brand and Rausch, 2021), those focusing on COO suffer from methodological and content-related limitations (see Table 1). Some solely focused on low-involvement products (Ahmed et al, 2004), whereas others exclusively relied on highinvolvement products.…”

Section: Introductionmentioning

confidence: 99%

Measuring country of origin effects in online shopping implicitly: a discrete choice analysis approach

Brand

Baier

2022

IMR

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PurposeTo examine whether the country of origin (COO) effect actually exists in an e-commerce context, the authors intend to contribute to the ongoing debate by measuring the COO effect through a series of connected studies.Design/methodology/approachDrawing on cue utilization theory, the authors emphasize the urge to investigate the COO effect in multiple cue settings in order to reveal a more realistic picture of its actual effect size. In contrast to most prior research, which often does not analyze COO using methodological plurality and neglects important contextual factors, the authors employed a four-staged research design in an attempt to trigger and measure the COO’s implicit effect size in today’s pervasive context of online shopping. The importance of brands (inhering the COO) is decompositionally calculated relative to other extrinsic cues by applying a Hierarchical Bayes estimation, with the COO impact being extracted subsequently.FindingsThe results deepen concerns that the COO effect actually does not exist, particularly in the more contemporary context of online shopping. Specifically, preferences for previously favored German products faded when controlling for brand attitude for both high-involvement (p = 0.003) and low-involvement products (p = 0.024).Research limitations/implicationsThe study focused on consumers of Generation Y, as they represent one of the most important segments in online shopping. Findings might be replicated for other consumer generations. The study focused on Chinese consumers, as the Chinese e-commerce market represents the world’s largest one. Future studies might investigate other markets.Practical implicationsAs brands, rather than a COO effect, impacted consumer preferences, companies selling their products to Chinese consumers online need to establish a reputation for quality early on. Chinese companies should emphasize their COO to make use of the ethnocentrism detected. Companies profit from the Best-Worst Scaling investigation revealing which product categories Chinese consumers most preferably buy online from German companies.Originality/valueTo the best of the authors’ knowledge, this study is the first to capture the importance of COO in the contemporary context of ubiquitous online shopping. Moreover, a more realistic and less biased way of measuring the importance of COO is enabled by building upon three pre-connected studies. The findings allow to develop a generalization for both high- and low-involvement products.

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“…Conjoint analysis usually is used to identify some variables that important for a product. For example: [7] using adaptive choice-based conjoint analysis to identify surcharge for outdoor apparel, [8] evaluate domestic express coach service using conjoint analysis, [9] identify the critical attribute of smartphones using conjoint 3 analyses. Widely conjoint analysis is used to evaluate essential attributes for a product.…”

Section: )mentioning

confidence: 99%

Electronic Commerce Product Recommendation using Enhanced Conjoint Analysis

Osmond¹,

Hidayat²,

Supangkat³

2021

IJACSA

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While finding any product, there are many identical products sold in the marketplace, so buyers usually compare the items according to the desired preferences, for example, price, seller reputation, product reviews, and shipping cost. From each preference, buyers count subjectively to make a final decision on which product is should be bought. With hundreds of thousands of products to be compared, the buyer may not get the product that meets his preferences. To that end, we proposed the Enhanced Conjoint Analysis method. Conjoint Analysis is a common method to draw marketing strategy from a product or analyze important factors of a product. From its feature, this method also can be used to analyze important factors from a product in the marketplace based on price. We convert importance factor percentage as a coefficient to calculate weight from every attributes and summarize it. To evaluate this method, we compared the ECA method to another prediction algorithm: generalized linear model (GLM), decision tree (DT), random forest (RF), gradient boosted trees (GBT), and support vector machine (SVM). Our experimental results, ECA running time is 6.146s, GLM (5.537s), DT (1s), RF (10,119s), GBT (45.881s), and SVM (11.583s). With this result, our proposed method can be used to create recommendations besides the neural network or machine learning approach.

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Examining sustainability surcharges for outdoor apparel using Adaptive Choice-Based Conjoint analysis

Cited by 25 publications

References 112 publications

Consumer Preferences in Germany for Bio-Based Apparel With Low and Moderate Prices, and the Influence of Specific Factors in Distinguishing Between These Groups

Consumer Preferences in Germany for Bio-Based Apparel With Low and Moderate Prices, and the Influence of Specific Factors in Distinguishing Between These Groups

Measuring country of origin effects in online shopping implicitly: a discrete choice analysis approach

Electronic Commerce Product Recommendation using Enhanced Conjoint Analysis

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