Search results

This research investigates when and how brands influence attribute importance weights. Most past studies modelling consumer decision processes treated the brand of a product as an attribute parallel to the price, color or size of a product, and as a result, those studies assigned an equal (i.e. non-contingent) importance weight across brands for each attribute. In contrast, this study introduces a brand-contingent attribute-weighting process, in which brand is a higher-order construct that influences attribute importance.

This study presents a multi-level choice model in which the importance weight of an attribute can vary across brands. This study then estimates the model using real purchase data and survey data from an airline industry.

This study finds that attribute importance weights are contingent upon two aspects of a brand – the perceived relative position of the brand and consumers’ brand usage experiences. Specifically, when consumers perceive a brand to be inferior to its competitors in a given attribute, they generally place greater weight on that attribute for that brand. In contrast, when consumers perceive a brand to be superior to its competitors in a given attribute, only consumers with extensive brand usage experiences place greater weight on that attribute for that brand.

The findings provide managerial insights on brand positioning and segmentation strategies using consumers’ brand usage experiences.

This study advances the literature on consumer decision processes by modeling an attribute-weighting process that is contingent upon brands. The present study models this process based on consumer behavior theories and estimates the model using real market data.

This paper reviews the current literature on theoretical and methodological issues in discrete choice experiments, which have been widely used in non-market value analysis, such as elicitation of residents' attitudes toward recreation or biodiversity conservation of forests.

We review the literature, and attribute the possible biases in choice experiments to theoretical and empirical aspects. Particularly, we introduce regret minimization as an alternative to random utility theory and sheds light on incentive compatibility, status quo, attributes non-attendance, cognitive load, experimental design, survey methods, estimation strategies and other issues.

The practitioners should pay attention to many issues when carrying out choice experiments in order to avoid possible biases. Many alternatives in theoretical foundations, experimental designs, estimation strategies and even explanations should be taken into account in practice in order to obtain robust results.

The paper summarizes the recent developments in methodological and empirical issues of choice experiments and points out the pitfalls and future directions both theoretically and empirically.

The purpose of this paper is to study the effects of sports sponsorship on brand equity using two managerially related outcomes: price premium and market share.

This study uses a best–worst discrete choice experiment (BWDCE) and compares the outcome with that of the purchase intention scale, an established probabilistic measure of purchase intention. The total sample consists of 409 fans of three soccer teams sponsored by three different competing brands: Nike, Adidas and Puma.

With sports sponsorship, fans were willing to pay more for the sponsor’s product, with the sponsoring brand obtaining the highest market share. Prominent brands generally performed better than less prominent brands. The best–worst scaling method was also 35% more accurate in predicting brand choice than a purchase intention scale.

Future research could use the same method to study other types of sponsors, such as title sponsors or other product categories.

Sponsorship managers can use this methodology to assess the return on investment in sponsorship engagement.

Prior sponsorship studies on brand equity tend to ignore market share or fans’ willingness to pay a price premium for a sponsor’s goods and services. However, these two measures are crucial in assessing the effectiveness of sponsorship. This study demonstrates how to conduct such an assessment using the BWDCE method. It provides a clearer picture of sponsorship in terms of its economic value, which is more managerially useful.

The purpose of this study is to determine what the history of research in marketing implies for the reaction of the field to recent developments in technology due to the internet and associated developments.

This paper examines the introduction of new research topics over 10-year intervals from 1960 to the present. These provide the basic body of knowledge that drives the field at the present time.

While researchers have always borrowed techniques, they have refined them to make them applicable to marketing problems. Moreover, the field has always responded to new developments in technology, such as more powerful computers, scanners and scanner data, and the internet with a flurry of research that applies the technologies.

Marketing will adapt to changes brought on by the internet, increased computer power and big data. While the field faces competition for other disciplines, its established body of knowledge about solving marketing problems gives it a unique advantage.

This paper traces the history of academic marketing from 1960 to the present to show how major changes in the field responded to changes in computer power and technology. It also derives implications for the future from this analysis.

El objetivo de este estudio es examinar qué implica la historia de la investigación académica en marketing en la reacción del campo de conocimiento a los recientes desarrollos tecnológicos como consecuencia de la irrupción de Internet.

Esta investigación analiza la introducción de nuevos temas de investigación en intervalos de diez años desde 1960 hasta la actualidad. Estos periodos proporcionan el cuerpo de conocimiento básico que conduce al ámbito del marketing hasta el presente.

Aunque los investigadores tradicionalmente han tomado prestadas ciertas técnicas, las han ido refinando para aplicarlas a los problemas de marketing. Además, el ámbito del marketing siempre ha respondido a los nuevos desarrollos tecnológicos, más poder de computación, datos de escáner o el desarrollo de Internet, con un amplio número de investigaciones aplicando tales tecnologías.

El marketing se adaptará a los cambios provocados por Internet, aumentando el poder de computación y el big data. Aunque el marketing se enfrenta a la competencia de otras disciplinas, su sólido cuerpo de conocimiento orientado a la resolución de problemas le otorga una ventaja diferencial única.

Describe la historia académica del marketing desde 1960 hasta la actualidad, para mostrar cómo los principales cambios en este campo respondieron a los cambios tecnológicos. Se derivan interesantes implicaciones para el futuro.

Historia, Revisión, Cambio, Tecnología, Conocimiento, Internet, Datos, Métodos

Revisión general

The paper aims to identify and analyze passengers’ riding paths for providing better operational support for digital transformation in megacity metro systems.

The authors develop a method to leverage certain passengers’ deterministic riding paths to corroborate other passengers’ uncertain paths. Using Automatic Fare Collection data and train schedules, a witness model is built to recover the actual riding paths for passengers whose paths are unknown otherwise. The identification and analysis of passenger riding paths between three different types of origin–destination) pairs reveal the complexity of passenger path choice.

The results show that passenger path choice modeling is usually characterized by complexity, experience and partial blindness. Some passengers choose paths that are not optimal due to their experience and limited access to overall metro system information. These passengers could be the subject of improved path guidance in light of riding efficiency improved through digital transformation.

This research contributes to the improvement of metro management and operations by leveraging ongoing digital transformation in megacity metro systems. Based on the riding paths and trip chains of a large number of individual passengers identified by the proposed method, metro operation management could prevent risks in areas with concentrated passenger flow in advance, optimally adjust train schedules on a daily basis and deliver real-time riding guidance station by station, which would greatly improve megacity metro systems’ service safety, quality and operational efficacy over time.