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This study aims to extend research in marketing on two important relational constructs, customer satisfaction and brand attachment, by comparing their long-term effects on customer behaviors with different levels of performance difficulty in a relatively understudied domain of durable products.

Using a two-stage quantitative study with US customers from five durable product categories, the authors first explored the hierarchy of customers’ loyalty behaviors based on increasing effort in a pretest study (N = 675). Then, the authors tested the effectiveness of satisfaction and brand attachment for customers’ loyalty behaviors over a nine-month period in a longitudinal study (N = 2,284) with customers from the same product categories.

Compared to satisfaction, brand attachment emerges as a stronger long-term predictor of customer behaviors. The performance difficulty of customer behaviors positively moderates the impact of brand attachment and negatively moderates the impact of customer satisfaction. Brand attachment is particularly effective in predicting difficult-to-perform customer behaviors, which require customers to expend resources such as time and money. Customer satisfaction is mainly effective for predicting easy-to-perform behaviors, but its long-term impact is significantly lower for easy-to-perform behaviors than brand attachment.

The use of consumer durables in the study and samples from only one country restricts the generalizability of the findings.

The complementary roles of customer satisfaction and brand attachment are highlighted. Only satisfying customers is not enough to engage customers in behaviors that require resources such as money, time and energy for the brand.

A comparative study on the long-term effectiveness of two established relational metrics in explaining different customer behaviors varying in their performance difficulty in an understudied domain of durable products.

This study aims to explore the opposite effects of single-category versus multi-category products information diversity on consumer decision making. Further, the authors investigate the moderating role of three categories of visitors – direct, hesitant and hedonic – in the relationship between product information diversity and consumer decision making.

The research utilizes a sample of 1,101,062 product click streams from 4,200 consumers. Visitors are clustered using the k-means algorithm. The diversity of information recommendations for single and multi-category products is characterized using granularity and dispersion, respectively. Empirical analysis is conducted to examine their influence on the two-stage decision-making process of heterogeneous online visitors.

The study reveals that the impact of recommended information diversity on consumer decision making differs significantly between single-category and multiple-category products. Specifically, information diversity in single-category products enhances consumers' click and purchase intention, while information diversity in multiple-category products reduces consumers' click and purchase intention. Moreover, based on the analysis of online visiting heterogeneity, hesitant, direct and hedonic features enhance the positive impact of granularity on consumer decision making; while direct features exacerbate the negative impact of dispersion on consumer decision making.

First, the article provides support for studies related to information cocoon. Second, the research contributes evidence to support the information overload theory. Third, the research enriches the field of precision marketing theory.

This paper aims to draw on values theory, associative network theory and schema congruity theory to examine how consumers attribute human-like values to product categories and products, and how these attributions affect product evaluations.

Study 1 randomly assigned 925 respondents to one of three product categories (cars, mobile phones and vacations). They were asked about their values and to attribute values to an assigned product category. Study 2 randomly assigned 919 respondents to one of the four value-expressive car conditions. They were asked to attribute values to the car, and then about their attitudes and purchase intentions.

Respondents attributed human-like values to product categories and products that were distinct and reflected the motivational compatibilities and conflicts inherent in the circular structure of human values. Moreover, multifaceted value congruity effects were found to positively influence attitudes and intentions to purchase a car, including congruity with product category values-schema, consumers’ personal values-schema and the structure of human values.

The authors demonstrate how a cognitive memory-based view can be used to better understand the complexities of the attribution of human-like values to products and product categories. Moving beyond the attribution of brand personality, this study shows the importance of not only understanding the attributions of values to a product but also considering how these attributions interact with the more abstract product category values to influence evaluations.

In marketing, most research on intention–behavior consistency (IBC) is dedicated to improving the predictive ability of stated intentions for future behaviors, with relatively less exploration into the precursors of IBC, especially those linked to regular durable goods void of ethical consumption characteristics. This study aims to focus on the antecedents of IBC for such products, specifically examining category-level and product-level IBC in light of consumer knowledge.

The authors conducted a two-round survey to collect 3,560 Chinese consumers' vehicle purchase intentions and behaviors. The authors have also leveraged a large vehicle database (containing detailed vehicle attribute information) to measure consumer product knowledge (i.e. product judgment accuracy). A trivariate probit model was proposed to account for the potential selection bias arising from sample attrition while examining the effects of consumer knowledge on category- and product-level intention-behavior consistency.

Findings reveal that 47% of the participants displayed category-level IBC, and within this group, a further 39% exhibited product-level IBC. Notably, product knowledge, manifested as accurate product judgment, correlates negatively with category-level IBC but positively with product-level IBC. Intriguingly, the negative association between inaccurate judgment and product-level IBC is less pronounced for consumers overestimating the target product than for those underestimating it. Furthermore, consumers with direct experience are less prone to show category-level IBC, but are more inclined to display product-level IBC.

Vehicle marketers should prioritize consumers who show interest in their products but possess inaccurate knowledge, to retain whom companies can nurture their product knowledge. As for consumers with accurate knowledge, companies should try to expedite their purchase. Vehicle marketers also need to devise suitable advertising strategies to prevent consumers from undervaluing their products. For those overestimating competitors' products, companies can provide information to correct their overestimation and draw attention to possible confirmation biases. Vehicle marketers should encourage potential buyers who have shown interest in their product to participate in test-drive events, exhibitions, and other direct experience opportunities. Yet, for consumers still in the “whether-to-buy” decision-making phase, companies should not rush them into a test drive.

In the policy-making realm, governmental administrators can implement extensive consumer education programs, with a focus on the importance of product knowledge. This may involve providing consumers with accurate information and buying guides through various channels, which can help consumers make informed purchase decisions. Moreover, to foster healthy competition among vehicle companies, governmental administrators can establish regulations that require vehicle companies and other relevant industries to provide accurate and transparent product information, including performance, safety, and environmental aspects. Finally, in order to protect consumer rights, governmental administrators can also strengthen regulations to ensure fair treatment and safeguards for consumers throughout the purchasing process. This includes cracking down on false advertising and fraudulent practices, maintaining market order, and enhancing consumer confidence and purchase consistency.

This study is among the first attempts to examine the relationship between consumer knowledge and intention-behavior consistency, especially for regular durable products void of ethical consumption characteristics. Responding to the call of previous literature (e.g. Morwitz, 1997), the authors distinguish between and examine two forms of intention-behavior consistency simultaneously (using a sample selection model) and obtain more reliable conclusions. Moreover, the study's large-scale two-round survey had obtained individual-level purchase behavioral outcomes, which allowed the authors to measure each consumer's IBC at both category and product levels. More importantly, the authors show the opposite effects of consumer knowledge on the two forms of intention-behavior consistency.

This study aims to explore a new restaurant category’s development from birth to maturity, thereby explaining the rationale for category innovation strategies.

The authors conducted a qualitative case study analysis of the New Chinese-style Fusion Restaurant category’s development from birth to maturity. Thematic analysis was conducted on data collected from semi-structured interviews and textual information.

A new restaurant category’s maturation is determined by the formation of society’s shared knowledge about the category’s crucial attributes, which is an outcome of market participants’ category-related social practices. The authors develop a novel, four-stage framework for the socialized construction of this shared knowledge: a knowledge creation (KC), knowledge diffusion (KD), knowledge integration (KI) and knowledge structuralization (KS). This knowledge evolution along this KC–KD–KI–KS sequence can holistically describe the category maturation process. This framework can help understand the rationale for a restaurant category’s maturation by analyzing the interrelationships among market participants’ social practices, knowledge-related activities and market development.

This study explains how market participants’ knowledge-related activities facilitate a new restaurant category’s maturation. This can help restaurant managers cope with increasingly homogeneous competition by applying a category-innovation strategy.

This study extends product categorization research on restaurants by articulating a product category’s maturation process from a knowledge perspective.

Front-of-packaging (FOP) is a critical branding tool that uses “cues” to communicate product attributes and establish distinct brand images. This paper aims to understand how food brands utilize cues and their relative proportions to hierarchically communicate brand image and belonging to particular subcategories.

A content analysis is used for analysing 543 food FOPs sold in Australia (breakfast cereals, chips, snack bars). Samples are collected and classified into product sub-categories defined by ingredients, consumer-audience and retail placement. A novel 10 × 10 coding grid is applied to each FOP to objectively analyse cue proportion, with statistical comparison undertaken between sub-categories.

Results reveal intrinsic cues are favoured over extrinsic cues, except for those in the eatertainment sub-category. Hierarchies are evidenced that treat product and branding cues as primary, with health cues secondary. Statistically significant differences in cue proportions are consistently evident across breakfast cereals, chips and snack-bar FOPs. Clear differentiation is evidenced through cue proportions on FOP for health/nutrition focused sub-categories and eatertainment foods.

“Cue utilization theory” research is extended to an evaluation of brand encoding (not consumer decoding). Design conventions reveal how cue proportions establish a dialogue of communicating brand/product image hierarchically, the trade-offs that occur, a “meso-level” to Gestalt theory, and achieving categorization through FOP cue proportions. Deeper understanding of packaging design techniques provides inter-disciplinary insights that extend consumer behaviour, retailing and design scholarship.

This paper aims to analyze variations in the parameters of the market share–rank power law across consumer packaged goods (CPG) categories.

The authors use a two-level hierarchical linear model to examine the relationships between category-level variables and the parameters of the market share–rank power law in 790 CPG categories.

The slope of the market share–rank power law is shallower – indicating more equal market shares – in categories of high importance to retailers and those with high levels of promotional activity or high-volume purchases. Higher levels of market share inequality are associated with categories with high overall prices.

To the best of the authors’ knowledge, this is the first research to show the systematic influence of category characteristics on the relationship between brands’ market shares and their ranks, thus, identifying a key moderator for this important empirical generalization in marketing.

While market leadership may be a desirable goal for many brands, the corresponding market share at the top brand does vary. Moreover, the share premium for being number one in the category (gap between the top and other highly ranked brands) can be greatly affected by retailers’ strategies. In addition, the slope of the power law has desirable qualities as a measure of market concentration. However, the empirical study shows that category characteristics must be considered when analyzing differences in concentration across categories or time.

While other studies document variations in the market share–rank power law relationship, to the best of the authors’ knowledge, this is the first that models these variations as a function of observable category characteristics. The comprehensive nature of the data demonstrates the universality of the market share–rank power law relationship across CPG categories in the USA.

Given the dominant share of India in global production of fruits and vegetables, this paper intends to analyze the export competitiveness of India and other major food exporters in the world trade. The purpose of this study is to examine export structure, substitutability and complementarity of selected fresh and processed fruits and vegetables of top ten food exporters for the period 2010-20.

Balassa’s (1965) revealed comparative advantage (RCA) index was used to measure RCA indices of selected fruits and vegetables under study. Also, revealed symmetric comparative advantage (RSCA) and normalized RCA (NRCA) indices have been calculated. Further, Spearman rank correlation coefficients were computed to analyze changes over the study period for India and other competing countries. The export data have been sourced from UN Comtrade, an electronic database of United Nations, as well as World Trade Statistical Review, a database of World Trade Organization. The analysis was undertaken at Harmonized System (HS) four-digit classification for the period 2010-20.

The results disclosed an improvement in India’s comparative advantage over the period of 2010-20 in HS 07 product category, whereas the advantage ceded to other competitive nations in HS 08 product category. Further, Spearman rank correlation coefficients revealed that India faces competition from countries like China, Indonesia, Brazil, Thailand, Argentina and European Union for HS 07 product category, while countries like Mexico, Indonesia, Brazil and Thailandare the major competitors of India in HS 08 product category.

The paper expands the existing agricultural trade literature in three ways. First, it is one of the very few studies that have analyzed RCA for Indian fresh and processed fruits and vegetables using three different types of indices, namely, Balassa’s RCA, RSCA and NRCA. Second, the authors provide a number of comparisons related to RCA for Indian fruits and vegetables with other top food exporters in the world for a period of 10 years (2010-20). Third, the authors contribute to agricultural trade literature by assessing the substitutability or complementarity of India in the export of fruits and vegetables with other competing nations by using Spearman rank correlation coefficients.

This study aims to address the global food waste problem in restaurants by analyzing customer sales information provided by restaurant tickets to gain valuable insights into directing sales of perishable products and optimizing product purchases according to customer demand.

A system based on unsupervised machine learning (ML) data models was created to provide a simple and interpretable management tool. This system performs analysis based on two elements: first, it consolidates and visualizes mutual and nontrivial relationships between information features extracted from tickets using multicomponent analysis, bootstrap resampling and ML domain description. Second, it presents statistically relevant relationships in color-coded tables that provide food waste-related recommendations to restaurant managers.

The study identified relationships between products and customer sales in specific months. Other ticket elements have been related, such as products with days, hours or functional areas and products with products (cross-selling). Big data (BD) technology helped analyze restaurant tickets and obtain information on product sales behavior.

This study addresses food waste in restaurants using BD and unsupervised ML models. Despite limitations in ticket information and lack of product detail, it opens up research opportunities in relationship analysis, cross-selling, productivity and deep learning applications.

The value and originality of this work lie in the application of BD and unsupervised ML technologies to analyze restaurant tickets and obtain information on product sales behavior. Better sales projection can adjust product purchases to customer demand, reducing food waste and optimizing profits.

As e-commerce has expanded rapidly, online shopping platforms have become widespread in India and throughout the world. Product return, which has a negative effect on the E-Commerce Industry's economic and ecological sustainability, is one of the E-Commerce Industry's greatest challenges in light of the substantial increase in online transactions. The authors have analyzed the purchasing patterns of the customers to better comprehend their product purchase and return patterns.

The authors utilized digital transformation techniques-based recency, frequency and monetary models to better understand and segment potential customers in order to address personalized strategies to increase sales, and the authors performed seller clustering using k-means and hierarchical clustering to determine why some sellers have the most sales and what products they offer that entice customers to purchase.

The authors discovered, through the application of digital transformation models to customer segmentation, that over 61.15% of consumers are likely to purchase, loyal customers and utilize firm service, whereas approximately 35% of customers have either stopped purchasing or have relatively low spending. To retain these consumer segments, special consideration and an enticing offer are required. As the authors dug deeper into the seller clustering, we discovered that the maximum number of clusters is six, while certain clusters indicate that prompt delivery of the goods plays a crucial role in customer feedback and high sales volume.

This is one of the rare study that develops a seller segmentation strategy by utilizing digital transformation-based methods in order to achieve seller group division.