Consumer Buying Behaviour and Adoption of Green Products in Large Supermarkets in Nairobi City Kenya = سلوك شراء المستهلك وتبني المنتجات الخضراء في محلات السوبر ماركت الكبيرة في مدينة نيروبي بكينيا

Imbambi, Olinjo Samuel; Kinoti, Mary

doi:10.12816/0047944

Cited by 4 publications

(3 citation statements)

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“…Studies show that factors that determine consumers' intention and behaviour to purchase green products and engage in green consumption consist of personal influence, knowledge and attitudes towards green consumption, and internal and external regulatory factors (Albayrak et al, 2013;Paço et al, 2019;Grace & Itani, 2014;Zabkar & Hosta, 2013). Consumers' purchasing preferences are also significantly influenced by the characteristics of products (e.g., product quality, trademark or price) (Agyeman, 2014;Kaygisiz et al, 2019), and sociocultural and psychological factors (Imbambi & Kinoti, 2018).…”

Section: Theoretical Framewokmentioning

confidence: 99%

Consumers’ Behaviour and Their Willingness to Accept Green Packaging: A Case Study in Vietnam

Thuy,

Thuy

2024

J. of Law and Sust. Develop.

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Objectives: This study specifically focuses on examining customers' purchasing behaviour and their acceptance of green packaging as a replacement for single-use plastic bags. Method: The data used in this study are primary, including survey data obtained from the respondents. We designed a questionnaire related to examining customers' purchasing behaviour and their acceptance of green packaging. We determined the observed variables (scales) of examining customers' purchasing behaviour and their acceptance of green packaging based on previous studies. Structural Equation Modelling and Willingness to Accept models were utilised to achieve the study's objectives. Results: The results demonstrate that personal attitude statistically significantly influences respondents' purchasing behaviour. Moreover, the findings indicate that the expenditure level and the types of green packaging have an impact on the respondents' willingness to accept green packaging as a substitute for single-use plastic bags. Conclusions: This article is an academic contribution that enables understanding of customers' purchasing behaviour and their acceptance of green packaging. To promote the use of green packaging, it is crucial for business planners to prioritise obtaining and maintaining necessary certifications for their green products. It is imperative for policy-makers to actively assume a crucial role in promoting the adoption of green packaging through official government channels, public institutions, and targeted communication campaigns.

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Section: Theoretical Framewokmentioning

confidence: 99%

Consumers’ Behaviour and Their Willingness to Accept Green Packaging: A Case Study in Vietnam

Thuy,

Thuy

2024

J. of Law and Sust. Develop.

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“…This product is associated with many other products, and a large portion of their consumers come to this branch to buy cat food and then purchase other items. Therefore, understanding the associated products and backtracking (tunability) is also crucial [16,17]. Nowadays, there are different methods of association rule postprocessing, as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

THAPE: A Tunable Hybrid Associative Predictive Engine Approach for Enhancing Rule Interpretability in Association Rule Learning for the Retail Sector

Alawadh,

Barnawi

2024

CMC

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Association rule learning (ARL) is a widely used technique for discovering relationships within datasets. However, it often generates excessive irrelevant or ambiguous rules. Therefore, post-processing is crucial not only for removing irrelevant or redundant rules but also for uncovering hidden associations that impact other factors. Recently, several post-processing methods have been proposed, each with its own strengths and weaknesses. In this paper, we propose THAPE (Tunable Hybrid Associative Predictive Engine), which combines descriptive and predictive techniques. By leveraging both techniques, our aim is to enhance the quality of analyzing generated rules. This includes removing irrelevant or redundant rules, uncovering interesting and useful rules, exploring hidden association rules that may affect other factors, and providing backtracking ability for a given product. The proposed approach offers a tailored method that suits specific goals for retailers, enabling them to gain a better understanding of customer behavior based on factual transactions in the target market. We applied THAPE to a real dataset as a case study in this paper to demonstrate its effectiveness. Through this application, we successfully mined a concise set of highly interesting and useful association rules. Out of the 11,265 rules generated, we identified 125 rules that are particularly relevant to the business context. These identified rules significantly improve the interpretability and usefulness of association rules for decision-making purposes.

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“…The proposed framework is expected to make a significant contribution to the retail sector in Saudi Arabia by providing retailers with actionable insights into consumer behavior. It has been proven that when an organization is able to understand the buying habits of consumers, it becomes easier for them to improve their business performance indicators [19,24]. Our research aimed to address this point, and the results and recommendations will improve marketing performance indicators such as customer satisfaction and customer loyalty.…”

Section: Introductionmentioning

confidence: 99%

A Consumer Behavior Analysis Framework toward Improving Market Performance Indicators: Saudi’s Retail Sector as a Case Study

Alawadh,

Barnawi

2024

JTAER

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Studying customer behavior and anticipating future trends is a challenging task, as customer behavior is complex and constantly evolving. To effectively anticipate future trends, businesses need to analyze large amounts of data, use sophisticated analytical techniques, and stay up-to-date with the latest research and industry trends. In this paper, we propose a comprehensive framework to identify trends in consumer behavior using multiple layers of processing, including clustering, classification, and association rule learning. The aim is to help a major retailer in Saudi Arabia better understand customer behavior by utilizing the power of big data analysis. The proposed framework is presented as being generalized to gain insight into the generated big data and enable data-driven decision-making in other relevant domains. We developed this framework in collaboration with a large supermarket chain in Saudi Arabia, which provided us with over 1,000,000 sales transaction records belonging to around 30,000 of their loyal customers. In this study, we apply our proposed framework to those data as a case study and present our initial results of consumer clustering and association rules for each cluster. Moreover, we analyze our findings to figure out how we can further utilize intelligence to predict customer behavior in clustered groups.

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Consumer Buying Behaviour and Adoption of Green Products in Large Supermarkets in Nairobi City Kenya = سلوك شراء المستهلك وتبني المنتجات الخضراء في محلات السوبر ماركت الكبيرة في مدينة نيروبي بكينيا

Cited by 4 publications

References 0 publications

Consumers’ Behaviour and Their Willingness to Accept Green Packaging: A Case Study in Vietnam

Consumers’ Behaviour and Their Willingness to Accept Green Packaging: A Case Study in Vietnam

THAPE: A Tunable Hybrid Associative Predictive Engine Approach for Enhancing Rule Interpretability in Association Rule Learning for the Retail Sector

A Consumer Behavior Analysis Framework toward Improving Market Performance Indicators: Saudi’s Retail Sector as a Case Study

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