Partial Least Squares Modeling in Marketing Research: A Tailor-Made Model of Wine e-Commerce Consumers in Switzerland

Morard, Bernard; Simonin, Dimitri

doi:10.18178/joebm.2016.4.5.427

Cited by 7 publications

(21 citation statements)

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“…The best way to confirm a model"s statistical reliability is to use a variety of quality tests. By considering several tests instead of just one, each test confirms the quality result of the others (Morard and Simonin, 2016) [19].…”

Section: Model Constructionmentioning

confidence: 65%

“…In other words, depending on the chosen model, the estimation will always be considered correct. "A model will always only be a representation of the reality: a generalization of a multiplicity of realities" (Morard and Simonin, p. 413) [19].…”

Section: General Overviewmentioning

confidence: 99%

“…Subsequently, the company strategy will be discussed and optimized if necessary. In data computation, there is always a presumption that an implicit model exists (Morard and Simonin, 2016) [19].…”

Section: Model Constructionmentioning

confidence: 99%

“…Morard, Stancu, and Jeannette (2012;2013;2015) [27] - [29] - [2] use this method in the field of accounting, but the same steps could also be taken in any other academic fields, such as marketing research and business strategy (Morard and Simonin, 2016) [19]. Furthermore, due to of the type of connection between latent variables, certain rules must be followed in order to build a model using O-PLS (Jeannette, 2014) [1]: There must be (1) no causal loop, (2) at least one connection per latent variable, (3) at least one assigned indicator per latent variable, (4) indicators that cannot be related to more than one latent variable, and (5) [4] in organization, Chin et al (2003) in management information system, and Hulland (1999) in business strategy.…”

Section: Model Constructionmentioning

confidence: 99%

“…Morard and Simonin (2016) [19] used PLS, specifically O-PLS, in a context of consumer behavior analysis by building the optimal consumer behavior description (OCBD) computed by PLS-A; the authors built a tailor-made model of the e-consumer of wine without any pre-existing model. Their model was built from scratch and is aimed at precisely explaining consumer behavior in this specific market.…”

Section: B Consumer Behavior Researchmentioning

confidence: 99%

See 4 more Smart Citations

Introducing an Optimal (and a Simpler) Approach to Partial Least Squares Analyses

Simonin¹,

Morard²

2017

IJTEF

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Abstract-The purpose of this study is to show the many possibilities that partial least squares (PLS) analysis offers, as well as its ease of use. This analysis is a useful tool, because it brings an additional point of view to statistical analysis beyond that of structural equation modeling analysis. Here, the authors suggest using a different approach to PLS, called "optimal PLS." It combines principal component analysis and PLS analysis to compute the data; by convergent iterations, this approach produces an optimal model not based on a reference model to best explain a given situation. The study illustrates this approach with two practical applications that create optimal models from the ground up: one in management controlling and the other in marketing. The software, which is used as a computational tool, has an algorithm based on optimal PLS. The study is original, because it chooses two opposing fields of research, namely management controlling (a quantitative discipline) and consumer behavior research (a qualitative discipline), in an attempt to understand when optimal PLS provides reliable results. The authors conclude that the use of PLS is multifaceted, and optimal PLS has a high capacity to explain the actual components, which helps researchers and analysts reach appropriate strategic decisions. With regard to the study's practical implications, the overview and the accompanying explanations will enable academics and analysts to use PLS analysis more easily by means of optimal PLS approach's five steps. They can add PLS and optimal PLS to their list of analytical tools to bring fresh points of view to their research.Index Terms-Partial least squares path modeling, optimal PLS, optimal strategy, marketing research, consumer behavior, management controlling, algorithm, software.

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Section: Model Constructionmentioning

confidence: 65%

Section: General Overviewmentioning

confidence: 99%

Section: Model Constructionmentioning

confidence: 99%

Section: Model Constructionmentioning

confidence: 99%

Section: B Consumer Behavior Researchmentioning

confidence: 99%

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Introducing an Optimal (and a Simpler) Approach to Partial Least Squares Analyses

Simonin¹,

Morard²

2017

IJTEF

Self Cite

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Does innovation propensity influence wineries’ distribution channel decisions?

Casali

Perano

Presenza

et al. 2018

IJWBR

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Purpose The aim of this paper is to analyze the relationships between distribution strategies and the level of innovation propensity in the winemaking industry. It intends to identify the existence of patterns around the way wineries innovate and the way distribution channels are used. These determinants can support or constrain wineries’ behaviors in their strategic choices related to distribution channels. Design/methodology/approach The sample comprised 191 Italian small- to medium-sized enterprises in the wine industry. First, a two-step cluster analysis was used to identify patterns in the level of innovation propensity and differences in distribution channel strategies. Second, the research question was tested using multinomial logit regression. Findings Five clusters of innovation propensity were identified, varying from “no propensity to innovate” to “propensity for radical innovation”, and three clusters of distribution channel strategies were found. A significant negative relationship between innovation propensity and distribution channel strategies was revealed. This means that the greater the propensity to innovate, the smaller the need for a wholesale distribution option. Research limitations/implications As with most research, there are limitations to this study. First, the sample is from only one country. A second limitation is the sample size (191 Italian firms). A sample including large firms can be used to further validate the findings. Linked to the sample, another possible limitation is that all respondents were small- and medium-sized enterprises from a single industry. Practical implications This study contributes to the current innovation research by showing the existence of a negative relationship between innovation propensity and the choice of distribution channel in the wine industry. This knowledge is precious to entrepreneurs and managers in the wine sector, allowing them to better consider not only the type of strategies related to distribution channels but also the importance of building the firm’s propensity to innovate into the strategic decision-making process. Furthermore, the paper provides an opportunity for practitioners to reflect upon the fact that changing the distribution channel is more than just changing the outlet for their product; it might also require a revision in their innovation propensity to better facilitate the process. Social implications There are also social implications, in particular providing an advantage for consumers. The major advantage is based on the fact that consumers are now aware that the level of innovation propensity in a wine industry is directly linked to the type of distribution channel adopted. Therefore, wines with low-innovation propensity are most likely found to adopt wholesale distribution strategy, while the more innovative wineries adopt the wine expert and direct distribution channels. Originality/value For the first time, a cluster analysis approach was used to review different typologies of Italian wineries based on their propensity toward to innovation and subsequent distribution strategies. This study further explains the direct relationship between innovation propensity and the strategic choice toward between long or short distribution channels.

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Risk Types and Coping Mechanisms for Ethical Tourism Entrepreneurs: A New Conceptual Framework

Power

Domenico

Miller

2019

Journal of Travel Research

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Risk is a widely accepted entrepreneurial construct and entrepreneurship is a key feature of the tourism industry. Yet, investigating types of risks and calls for research on ethical entrepreneurship in tourism have largely been neglected. This research provides an original contribution to academia about risk-types and subsequent coping mechanisms as faced by ethical tourism entrepreneurs. Using methods from Personal Construct Theory, 15 in-depth interviews with self-defined ethical tourism entrepreneurs were conducted. An existing consumer risk framework (monetary, functional, social, and psychological risk) provided a priori themes for analysis. Through constant comparison of data, different forms of intelligence (survival, system, emotional, and spiritual) have emerged as coping mechanisms. These in vivo themes have been paired with risk-types to develop an original conceptual framework for risk faced by ethical tourism entrepreneurs. The implications of this framework are significant in providing support to nascent entrepreneurs, government start-up initiatives, and entrepreneurial incubator programs.

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Partial Least Squares Modeling in Marketing Research: A Tailor-Made Model of Wine e-Commerce Consumers in Switzerland

Cited by 7 publications

References 0 publications

Introducing an Optimal (and a Simpler) Approach to Partial Least Squares Analyses

Introducing an Optimal (and a Simpler) Approach to Partial Least Squares Analyses

Does innovation propensity influence wineries’ distribution channel decisions?

Risk Types and Coping Mechanisms for Ethical Tourism Entrepreneurs: A New Conceptual Framework

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