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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 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.

Grounded in strategic fit theory, this study aims to identify external and internal factors that influence retailers’ strategic choices regarding their own product brands. Furthermore, it seeks to explore the variations between different own product brand strategies in achieving both external and internal strategic fit.

The systematic review method, incorporating a thematic analysis, was adopted, and 318 articles were included for review.

The factors that influence retailers’ strategic choices regarding their own product brands encompass a range of external macro and industrial environmental factors, along with various internal resource and capability factors. Moreover, the effects of these factors vary across different own product brand strategies.

To our knowledge, this is the first systematic review of research on retailers’ own product brands from a strategic management perspective, offering systematic and structured guidance for retailers.

This paper aims to identify the combination of innovation determinants driving the creation of innovative products amongst market leaders and market followers in food and beverage (F&B) firms.

This research is based on the case study methodology by using two types of data sources: (1) semi-structured interviews with industry experts and (2) in-depth interviews with managers. In addition, a questionnaire adapted from prior research was used to consider market and firm types.

Suggesting an integrated theoretical framework based on firm-based factors and market-based factors, this study identified a combination of determinants significantly impacting innovative products in the market. Specifically, these determinants are competition intensity and innovation capability (a combination of research and development (R&D) investment and marketing capabilities). The study also examined how these determinants vary depending on whether the firms are market leaders or market followers.

This research provides practical insights for managers working in the F&B industry by using case studies and exploring the determinants of developing innovative products. In doing so, suitable strategies can be selected according to the market and firm situations.

The originality of the study is shown by focussing on how different combinations of market and firm factors could be applied in creating successful innovative products in the food sector.

The Islamic world spans many different languages with different language structures. This paper aims to explore one way in which language structure affects consumer response to the marketing of cobrands.

Two between subject experiments were conducted using samples of participants from Saudi Arabia and the USA. The first manipulated partner brand category similarity and brand name order, along with the structure of the language used to communicate with the market. The data for this study includes Arabic speakers in Saudi Arabia as well as English speakers in the USA. The second study explores how targeting a population fluent in multiple languages of varied structure nullifies the findings from the first study and uses Latino participants in the USA.

This study finds that when brands come from similar product categories, name order did not affect cobrand evaluations, but it did when the brands come from dissimilar product categories. Here, evaluations of the cobrand are enhanced when the invited brand is in the position that adjectives occupy in the participant’s language. The authors also find that being proficient in two languages, each with a different default order for adjectives and nouns, quashes the effect of name order otherwise seen when brands from dissimilar product categories engage in cobranding.

By examining the impact of language structure on the effects of cobrand evaluation and conducting studies among participants with differing dominant languages, this research can rule out simple primacy or recency effects.

This study aims to examine the influence of two significant product labelling strategies (geographical indication [GI] vs country-of-origin [COO]) on shaping customer product attitude and purchase likelihood, considering consumers’ ethnocentric and cosmopolitan tendencies. The authors also investigate the boundary conditions and intervening mechanisms to manage the adverse consumer product evaluations and present mitigating procedures which reinstate favourable product evaluations and purchase likelihood.

The collected data from these all these studies were analysed using ANOVA and mediation anlaysis. The study tests the proposed hypotheses using three follow-up experimental investigations.

The study found that GI (vs COO) labels have a more significant impact on customers’ product evaluation and likelihood of purchase and supported the dispositional effect of ethnocentric and cosmopolitan inclinations. Further, the results indicated that self-product congruence can efficiently regulate consumer dispositions. Also, the results confirmed the significant impact of product identification on influencing consumer attitudes.

The above-said insights add practical insights, particularly concerning product labelling. Also, the insights on product attitudes and purchase likelihood intricacies in the context of product labelling enable companies to comprehend better the significance of GI labels, COO labels and self-product congruence.

To the best of the authors’ knowledge, this is the first time a study has compared the role of two significant product labelling strategies (GI vs COO) in shaping customer product evaluations, confirmed its boundary conditions and shown how to transform them into helpful customer product outcomes.

The aim of this study is to examine the effect of brand attribution on consumer behaviour. The authorsare interested in finding out if consumers are more likely to purchase a new sports product when it is attributed to a brand. The authors are interested in finding out if the brand positively influences the consumer's appreciation of the product in the case of the launch of a new product.

The authors completed a quantitative survey with 320 people who are familiar with the toning concept, aged between 20 and 45 years old. The questionnaire was conducted face to face in six large cities in France.

Main results show that belief in the effectiveness of the product is positively correlated with purchase intention. However, the attribution of the product to the brand does not guarantee belief in the effectiveness of the product nor does it guarantee purchase intention. Brand credibility has a positive impact on belief in the effectiveness of the product. Furthermore, exposure to advertising allows the product to be attributed to the brand. Yet, it does not have an impact on belief in the product's effectiveness or purchase intention.

Whereas studying consumer behaviour is not new, analysing consumer perceptions of innovative products and how these perceptions relate to the product brand can provide interesting implications both for consumer behaviour research and from a practical perspective for brands. Indeed, this paper shows the importance of exposure to advertising for a product to be attributed to a brand but this is not enough to induce its purchase. It is the credibility of the brand in the product category as well as the belief in the effectiveness of the product that can lead consumers to buy it.

The supply chain (SC) of the fast-moving consumer goods (FMCG) sector in India witnessed a significant change soon after introducing the Goods and Services Tax (GST). With the initiation of this tax, companies started moving from individual state-wise warehouses to consolidation warehouses model to save costs. This paper proposes a model that frames a mathematical formulation to optimize the distribution network in the downstream SC by considering the complexities of multi-product lines, multi-transport modes and consolidated warehouses.

The model is designed as mixed-integer linear programming (MILP), and an algorithm is developed that works on the feedback loop mechanism. It optimizes the transportation and warehouses rental costs simultaneously with impact analysis.

Total cost is primarily influenced by the critical factor transportation price rather than the warehouse rent. The choice of warehouses at prime locations was a trade-off between a lower distribution cost and higher rent tariffs.

The study enables FMCG firms to plan their downstream SC efficiently and to be in line with the recent trend of consolidation of warehouses. The study will help SC managers solve complexities such as multi-product categories, truck selection and consolidation warehouse selection problems and find the optimum value for each.

The issues addressed in the proposed work are transporting products with different sizes and weights, selecting consolidated warehouses, selecting suitable vehicles for transportation and optimizing distance in the distribution network by considering consolidated warehouses.

This study aims to evaluate the impact of the proposed CARICOM Octagon “High In” Warning Label (OWL), against four alternative Front-of-Package Nutrition Labels (FOPNLs): US “Facts Up Front” (FUF), UK Multi-Traffic Light (MTL), Mexican OWL and the Brazilian Magnifying Glass “High In” Warning Label, on respondents’ purchase intentions, perception of healthiness and understanding of nutritional information across and within food products.

In an online randomized control experiment, adults from eight CARICOM countries (n = 948) were randomly assigned to a control and four treatment FOPNL groups. Respondents were tasked to choose between four categories of mock products with three variations in healthfulness across and within products.

No statistically significant difference (p > 0.05) was found across FOPNL groups compared to the OWL on outcomes of purchase intentions and perception of healthfulness. Regarding the understanding of nutritional information, FUF performed the best, as participants were 1.76 times (p = 0.03) and 3.23 times (p = 0.00) more likely to correctly identify the products with the highest and lowest amount of sugar, respectively. Results were similar for products with the lowest sodium (odds ratio [OR] = 2.25, p = 0.00) and highest saturated fats (OR = 2.11, p = 0.00).

Some limitations of this study include the use of an online platform to conduct the experiment. Though this was the most cost-effective method of execution and presented many benefits, there were limitations to using this approach. Firstly, this approach may not entirely replicate the real world in store purchasing settings. Although online grocery shopping is becoming increasingly popular, in the Caribbean, most grocery purchases are made in stores. Furthermore, online surveys are more likely to lead to samples with higher educational and income levels than the average population (Bethlehem, 2010). The skewedness observed was not unique to this study and was common with similar published studies (Franco-Arellano et al., 2020; Packer et al., 2021; Talati et al., 2018). Nevertheless, all respondents were randomly assigned to groups, and it was confirmed that there were no systematic differences in the education and income levels of participants across the FOPNL groups.

Some CARICOM policy makers advocate for the use of “High In” warning labels to limit the intake of nutrients of concern (NOCs) and to encourage healthier eating habits among consumers. However, regional private sector stakeholders have expressed concern about the lack of sufficient research undertaken at a regional level, to inform the effectiveness of this model within CARICOM, and some have also expressed a preference for the use of other interpretative and reductive FOPNLs, already in use in the Region. The results of this study reveal that while interpretative FOPNLs like the Draft CARICOM Regional Standard, DCRS5 (OWL) can assist consumers in making healthier purchase decisions, it was outperformed by the MTL on perception of healthiness and by the FUF on the understanding of nutrient information. It was also noted that the DCRS5 (OWL) was more effective when choosing across products with distinct nutritional differences but performed poorly in assisting respondents with making healthy purchasing decisions when all the products contained NOCs above the relevant thresholds. This study’s findings highlight that the existing FOPNL schemes can be further enhanced for improved outcomes. This can be achieved by using a hybrid approach which includes both reductive and interpretative elements to allow for comparison across and within food products. The literature also suggests the use of colour and combining positive as well as negative elements to encourage ease of interpretation, improved understanding and healthier food choices.

A properly designed FOPNL can support consumers in making healthier food choices; however, it must be accompanied by measures to raise consumer awareness and increase the health literacy of the population to cause shifts in preferences and behavioural patterns over time. This must also be coupled with policies to make healthy food choices more affordable to the general population.

The results of this study revealed that FUF and MTL performed the best in assisting participants to correctly identify between products with the highest or lowest NOCs at the 5% significant level, and that the OWL performed poorly in assisting participants with making healthy purchasing decisions when all the products contained NOCs above the relevant thresholds.