Understanding the attitude and repurchase intention towards halal food product among non-muslim consumers in Malaysia

The purpose of this study was to understand the factors that influence the attitude and repurchase intention of non-Muslim consumer in Malaysia towards Halal food product. This study highlighted the conceptual insights of the multidimensional constructs of buyer factors (i.e. religion adoption, lifestyles, halal awareness, consumers past experience); and the product attribute factors (i.e. halal logo, perceived product quality and product country-of-origins) on the attitude of non-Muslim consumer on Halal food product. The study also examined the influence of non-Muslim consumer attitude on Halal food and subjective norm as the independent variables to the repurchase intention of non-Muslim consumer towards Halal food product. Thus, this study investigated the mediating effect of consumer trust between product attribute factors and the attitude of non-Muslim consumer on Halal food product. In addition, two theoretical lenses-the Theory of Planned Behaviour and Model of Buyer Behaviour are utilized in this study to explain the influence buyer factors and product attribute factors to non-Muslim attitude on Halal food product; and the influence of attitude and subjective norm to repurchase intention of non-Muslim consumers toward halal food product in Malaysia. Results from a survey of 444 Non-Muslim consumers whom have prior experience of consuming Halal food product were used for the statistical analysis. The data were drawn from five (5) states in Malaysia which consist of Pahang, Penang, Selangor, Johor and Sabah. Smart Partial Least Square 3.0 was used to analyse the data. The findings revealed that the buyer factors of religion adoption and consumer past experience influence the attitude of non-Muslim consumer towards halal food product. This study empirically proved that product attributes factors (i.e. halal logo, perceived product quality, and product country-of-origin) positively influence the attitude of non-Muslim consumer towards halal food product. The finding of the study also evident the attitude of non-Muslim consumer towards halal food product and subjective norm does influence non-Muslim consumers‘ repurchase intention of the halal food product. Finally, mediation analysis results show that consumer trust mediated the path between the influence of product factors (halal logo, product country-of-origin, and perceived product quality) and the attitude of non-Muslim consumer towards halal food product. More importantly, this study has provide further insights into the non-Muslim consumers‘ attitude and intention behaviour towards halal product as well as the prospect of halal industry in Malaysia

Essays On Dynamic Updating Of Consumer Preferences

Consumers dynamically update their preferences over time based on information learned through product search and consumption experiences, particularly in online media. Using three unique datasets from different domains, we address specific ways in which firms can use rich information about their customers\u27 behaviors to improve (1) the visual display of products on a webpage in online shopping, (2) predictions of new product adoption in online gaming, and (3) the timing of product release in online learning. First, we explore how consumers visually search through product options using eye-tracking data from two experiments conducted on the websites of two online clothing stores, which can inform retailers on how to position products on a virtual webpage. Second, we examine how consumers\u27 variety-seeking preferences change depending on past consumption outcomes within the context of an online multi-player video game, which can be used to improve predictions of new product adoption. Third, we use clickstream data from an online education platform to test theories of goal progress, knowledge accumulation, and boundedly rational forward-looking behavior, which can be used to explain binge consumption patterns and inform content providers on the best way to structure and release content. In each of these three projects, we build a mathematical model of individual decisions, with the parameterization grounded in theories of consumer behavior, and we demonstrate through in-sample prediction that our model is able to capture specific heterogeneous patterns within the data. We then test that our model is able to make out-of-sample predictions related to managerial interventions, and empirically verify our predictions using either lab experiments or new field data following a natural experiment policy change

Forecast reconciliation : methods, structures, criteria, and a new approach with spatial data

This PhD dissertation is a collection of four papers that aim to explore, in the marketing field, the research on hierarchical and grouped time-series reconciliation approaches. Those approaches are necessary when different departments of an organization have different needs regarding forecast aggregations. This work focuses, besides reconciliation approaches, on time-series forecasting methods, and on the importance of geographical information to better forecast and plan marketing strategies. The first paper is theoretical and argues on the importance to marketing of having accurate forecasts. It explores the current state of marketing research on modelling in general, and on forecast specifically. It covers the classifications of methods, datasets explored on current research, the basic model studied, and existing gaps. The paper concludes that marketing focuses on explanation, living a gap on accuracy evidence and on the applicability of the models proposed. The second paper explores those gaps by applying two current topics of discussion on forecasting time-series literature: machine learning techniques and ensemble models. These methods are easy to implement and are reported in the literature to improve accuracy. The paper proposes an adaptation to portfolio optimization to calculate the weights of an ensemble based on each base model's accuracy and the covariance matrix of such accuracies. The proposed approach outperforms all 15 base models and the equal weights benchmark. The paper also provides evidence that, if single models are compared, statistical methods have better accuracy than the machine learning methods applied. The third paper uses a statistical method to forecast time-series (i.e. sales) combined with different structure and criteria of aggregation. The aim of the paper is to compare different criteria based on marketing mix variables. The empirical application presented in the paper indicates whether product category, channel type or region (geographic location) works best alone or combined. It also gives evidence of the importance of geographical considerations to improve forecast accuracy. The last paper furthers explore this finding by proposing a new reconciliation approach that distributes an aggregate forecast to lower levels of disaggregation using a gravitational model. This paper also contributes to the literature by comparing statistical, machine learning and deep learning methods (LSTM). All papers presented in this dissertation use open-source tools, combining proprietary data that is natural to the process of every organization and publicly available data. The focus is on methods and tools that are generalizable to all types of goods, can be easily applied by any organization, with relatively low investment. The contributions of the PhD dissertation are (1) to compare statistical, machine learning and deep learning methods to forecast sales on single and ensemble models; (2) to provide evidence on the criteria and structure of aggregation that improves forecast accuracy the most; and (3) to offer a new approach to distribute an aggregate forecast to new geographical regions when no historical data is available.A presente tese de doutorado é uma coleção de quatro artigos científicos desenvolvidos com o objetivo de explorar, dentro da área de marketing, a pesquisa sobre reconciliação de previsão de séries temporais com estrutura hierárquica ou agrupada. Reconciliação de previsões é necessária quando diferentes áreas de uma organização necessitam de previsões em diferentes níveis de agregação. O presente conjunto de estudos foca, além da reconciliação de previsões, em métodos de previsão de series temporais e na importância de informações geográficas para melhor prever e planejar estratégias de marketing. O primeiro artigo apresentado é uma revisão da literatura atual em modelagem de marketing, focando nos estudos sobre previsão. O artigo argumenta sobre a importância para o marketing em ter previsões, nas diferentes classificações dos métodos, nos tipos de dados usados, no modelo básico estudado e nos potenciais para estudos futuros. O artigo conclui que marketing precisa de estudos que evidenciem acurácia e sejam fáceis de implementar na prática. O segundo artigo procurar preencher essas lacunas aplicando técnicas de machine learning e ensemble. Essas técnicas são discutidas atualmente na teoria sobre previsão de séries temporais por prometerem facilidade de aplicação e melhoria em acurácia. O artigo propõe uma adaptação da otimização de portfólio como estratégia para calcular os pesos dos diferentes modelos que compõe um ensemble. A proposta do artigo tem melhor acurácia, no teste realizado, que os 15 modelos únicos (estatísticos e de machine learning) e o ensemble usando pesos iguais para todos os modelos. O artigo contribui também para a discussão sobre machine learning para previsão de séries temporais, demonstrando, nesse caso, a superioridade dos modelos estatísticos. O terceiro artigo usa um método estatístico combinado com diferentes estruturas e critérios de agregação para prever séries temporais (vendas). O objetivo do artigo é comparar diferentes critérios baseados em variáveis de marketing. A aplicação empírica dá indícios de que informações sobre a localização das vendas aumenta a acurácia das previsões. O último artigo explora esse achado ao propor uma estratégia alternativa de reconciliação de previsões. Essa estratégia distribui uma previsão feita em um nível agregado para níveis desagregados usando um modelo gravitacional. O artigo também contribui para a literatura ao comparar métodos estatísticos e de machine learning com long short-term memory (LSTM), um método de deep learning. Todos os artigos usam ferramentas open-source e combinam dados públicos com dados proprietários que resultam naturalmente dos processos de qualquer organização. O foco dos estudos são métodos e ferramentas generalizáveis para todos os segmentos que possam ser facilmente implementados por qualquer empresa, com relativamente baixos investimentos. As contribuições dessa tese de doutorado são (1) comparar métodos estatísticos, de machine learning e deep learning para prever vendas em modelos únicos e combinados (ensemble); (2) prover evidências sobre os critérios e estruturas de agregação que melhoram a acurácia das previsões; e (3) oferecer uma nova estratégia para distribuir uma previsão agregada em novas regiões geográficas quando dados históricos não estão disponíveis

La influencia de los factores sociales en la experiencia del cliente: Un análisis de efectos de interacción

La gestión de la experiencia del cliente es una estrategia clave para los establecimientos minoristas, puesto que se considera un medido de diferenciación que es capaz de crear valor para los consumidores. En consecuencia, la literatura de marketing se ha centrado en entender la experiencia del cliente desde una perspectiva global. Sin embargo, la mayoría de las investigaciones previas es teórica o cualitativa, siendo los estudios cuantitativos muy escasos. En relación con la medida de la experiencia, no existen trabajos que consideren las respuestas psicológicas del cliente como un componente de la misma junto con sus respuestas afectivas y cognitivas. Otro aspecto a destacar es que los estudios que se centran en la gestión de los factores sociales (dependientes, acompañantes y otros clientes) como antecedentes de la experiencia en entornos comerciales son escasos y ninguno de ellos analiza los efectos de interacción de dichos factores sobre esta experiencia. Además, aunque hay aspectos situacionales y propios del consumidor que pueden afectar a la experiencia del cliente, como la motivación del cliente, hay otros que no han sido considerados todavía. Finalmente, las investigaciones previas han considerado que los resultados de marketing de centros comerciales pueden estar relacionados con los de las tiendas que se encuentran en los mismos. Sin embargo, todavía es necesario conocer cómo la experiencia del cliente en estas tiendas puede afectar a su experiencia global en el centro comercial. Para abordar estas oportunidades de investigación, esta Tesis Doctoral pretende analizar la influencia de los factores sociales en la experiencia del cliente en entornos comerciales, así como examinar la forma en que la evaluación que realiza el consumidor sobre el centro comercial puede verse afectada por su experiencia en las tiendas del mismo. Para ello, la Tesis contiene tres estudios empíricos. El primer estudio define la experiencia del cliente como su respuesta cognitiva, afectiva y psicológica ante los efectos directos y de interacción de los factores sociales que están presentes en el contexto de compra. Esta Tesis Doctoral se centra en los principales factores sociales existentes en el contexto minorista, las competencias de los dependientes, tanto sociales como funcionales, la similitud de los acompañantes con los que el cliente acude a comprar al centro comercial y la densidad social. El segundo estudio analiza la efectividad de las competencias de los dependientes en la experiencia del cliente dependiendo de características situacionales (duración de la interacción y formato de tienda) y las relacionadas con el propio cliente (orientación del cliente a la interacción y género). El tercer estudio se centra en los efectos de la experiencia del cliente en las tiendas del centro comercial sobre su evaluación global del mismo. Los datos se obtuvieron de una encuesta administrada en un centro comercial español. Finalmente, se obtuvieron un total de 1.249 cuestionarios válidos. Los resultados muestran que la tesis contribuye a la literatura de marketing incluyendo el estrés como una respuesta psicológica del consumidor en su experiencia en el comercio minorista. Además, se demuestra que los efectos de interacción de la densidad social, las competencias de los dependientes y la similitud de la compañía en la experiencia del cliente, extendiendo la Teoría del Impacto Social al contexto minorista. Además, la duración de la interacción, el formato de tienda y la orientación del consumidor hacia la interacción son características situacionales y relativas al consumidor que pueden modificar el efectos de las competencias de los dependientes sobre la experiencia del cliente. Finalmente, la experiencia del cliente en las tiendas del centro afecta a su evaluación del mismo, lo que ocurre como consecuencia de un proceso de atribución errónea de sentimientos y juicios de valor. Estos resultados sugieren que los gestores de centros comerciales deberían supervisar la experiencia del cliente en las tiendas. Además, tanto los gestores de centros comerciales, como los de los establecimientos, deberían tener en cuenta los efectos de los factores sociales sobre la experiencia del cliente, intentando reducir los efectos negativos de la densidad social, proporcionando a los dependientes con altas competencias funcionales y sociales y animando a los consumidores a acudir al centro acompañados por personas similares. Finalmente, los dependientes deberían aprender en qué casos son más relevantes un tipo de competencias u otro.Customer experience management has been a key strategy for retail companies in recent years, since it is considered a mean of differentiation that can create value for customers. As a consequence, the marketing literature has focused on how to understand the customer experience as a whole. However, most research is theoretical or qualitative, and there are only a few quantitative studies that try to address customer experience comprehensibly. Related to the customer experience measurement, no studies have considered psychological states as a component of customer experience together with the customers’ cognitive and affective responses. Another aspect to highlight is that there are scant studies that focus on the management of social factors (other customers, shop assistants and companions) as antecedents that can affect the customer experience in a retail setting with no previous research that analyses the interaction effects of social factors on customer experience. Furthermore, although there are situational and customer-related aspects that can affect the customer experience such as customers’ motivation, there are others that have not been considered yet. Finally, previous research has considered that marketing outcomes of shopping malls are related to those of the stores they host. However, little is known about how the store customer experience can affect the overall mall experience. To address these gaps, this doctoral thesis aims to analyze the influence of social factors on retail customer experience, as well as how the customers’ assessment of a shopping mall could be affected by their shopping experience in the stores it hosts. To do so, the thesis consists of three empirical studies. The first study defines customer experience as the customer cognitive, affective and psychological response to the direct and interaction effects of social factors that are present in the shopping context. This doctoral thesis is focused on shop assistants’ competencies, social and functional, on companionship similarity and on crowding as the main social factors to consider in a retail context. The second study analyzes the effectiveness of shop assistants’ competencies on customer experience depending on situational (interaction length and retail format) and customer-related (customer interaction orientation and gender) characteristics. The third study focuses on how customers’ assessment of a shopping mall can be influenced by their shopping experience at the stores it hosts. Data was gathered from a survey conducted in a Spanish shopping mall. A total of 1,249 valid questionnaires were obtained. The findings of the present thesis contribute to the marketing literature by including stress as a psychological response of customer experience. Furthermore, they demonstrate the interaction effects of perceived crowding, shop assistants’ competencies and companionship similarity on customer experience, extending the Social Impact Theory to the retail context. In addition, interaction length, retail format and customer interaction orientation are situational or customer-related characteristics that can modify the effect of shop assistants’ competencies in managing customer experience. Finally, the customer experience lived in the stores can influence the customer’s evaluation of the shopping mall through a judgment, affect, and arousal misattribution procedure. These results suggest that mall managers should monitor customer experience in the stores located in the shopping mall. Specifically, both retail and mall managers should take into account the effects of social factors on customer experience, trying to reduce the negative effects of perceived crowding, to provide their shop assistants with high functional and social competencies and to encourage customers to go shopping with a similar person. Finally, shop assistants should learn which competence is more critical in each purchase situation