A Predictive Approach to the Random Effect Model

Geisser, Seymour

doi:10.2307/2334290

Cited by 748 publications

(740 citation statements)

References 2 publications

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“…Furthermore, our dependent variable reached R² values above 0.1 (Falk and Miller 1992). The cross-validated redundancy indices (Q²) (Geisser 1975;Stone 1974) confirm that the three structural models have satisfactory predictive relevance for the endogenous variable (firm performance).…”

Section: Structural Modelmentioning

confidence: 75%

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Entrepreneurial orientation and firm performance in family SMEs: the moderating effects of family, women, and strategic involvement in the board of directors

et al. 2017

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Entrepreneurial success in family SMEs is largely determined by the knowledge, skills, and new ideas contributed by board directors, the most important actors in the formulation of corporate strategy and decision making. The composition of family SME boards has traditionally been homogeneous, as such boards usually comprise male family members. Boards' contributions, however, depend on their level of diversity and strategic involvement. This study analyzes the moderating effects of two main sources of board diversity in family firms, family involvement level and gender diversity, as potential means of enhancing family firms' success when exploiting entrepreneurial initiatives. This study also explores whether these two potential moderators depend on the strategic involvement of the board directors. Based on a sample of 230 Spanish family firms, we found that the link between entrepreneurial orientation and performance is stronger in firms with lower levels of family involvement and higher levels of gender diversity in the board. Moreover, the board's high strategic involvement may strengthen the positive impact of gender diversity and change the moderating influence of family involvement from negative to positive.

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Section: Structural Modelmentioning

confidence: 75%

“…Criterion (Q²) derived using the blindfolding procedure with an omission distance of 7 for predictive relevance (Geisser 1975;Stone 1974;Tenenhaus et al 2005). We conducted this analysis for both the total sample and the two subsamples (Real et al 2014).…”

Section: Structural Modelmentioning

confidence: 99%

Entrepreneurial orientation and firm performance in family SMEs: the moderating effects of family, women, and strategic involvement in the board of directors

et al. 2017

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“…With reference to the evaluation of the inner model, the coefficients of the determination of the endogenous latent variables (R 2 for wine consumption) reveal satisfactory values for the common data set (R 2 ¼ 0.681 and Q 2 ¼ 0.080), the Generation X (R 2 ¼ 0.661 and Q 2 ¼ 0.034) and Generation Y (R 2 ¼ 0.712 and Q 2 ¼ 0.114) sample. Moreover, Stone-Geisser's Q 2 (Stone, 1974;Geisser, 1974) yielded in all cases a value higher than zero for the endogenous latent variable, suggesting the predictive relevance of the explanatory variables.…”

Section: Evaluation Of Structural Relationsmentioning

confidence: 86%

Customer value perception: cross-generational preferences for wine

Wiedmann

Behrens

Klarmann

et al. 2014

British Food Journal

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. Purpose -A deeper understanding of the key drivers of consumer wine perception is a major challenge in the domain of wine marketing. The purpose of this paper is to examine the various dimensions of customer-perceived value that lead the customers -in general and divided into different age groups -to choose and consume a certain wine. Design/methodology/approach -In the exploratory study context of examining value-related consumer attitudes and behavioural effects, the drivers and outcomes of wine consumption based on a cross-generation sample, PLS path modelling was considered for the empirical tests of our hypotheses. Findings -Though there exist differences between Generation X and Generation Y consumers, the empirical results are supportive of the hypothesized positive relations between financial, functional, individual and social perceptions that influence the desire for and the consumption of wine.Research limitations/implications -For future research, the findings presented in the paper support the importance of enlarging the size of the sample and collecting data in different countries to compare the results on an international level. Practical implications -Successful wine marketing strategies should focus on the customer's subjective expectations and individual value perceptions by addressing the specific value aspects that are highly relevant for consumer loyalty. Originality/value -The study results are valuable for researchers, managers and marketers because they address the question of how to measure and forecast the perceived value with the greatest influence on consumers' wine choices.

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“…To prove new algorithm is more useful than the typical PLS algorithm, modeling process repeatedly must be considered, which involved such as cross validation [18,19], variable select [20] and missing data in sample data, and so on. Cross-validation techniques can help to select appropriate number of latent factors in case of without knowing the number of latent variables, and then ensure the accuracy of model and efficiency of calculation.…”

Section: Discussionmentioning

confidence: 99%

Improved Partial Least Squares Regression Recommendation Algorithm

Liao¹,

Du²,

Jin³

et al. 2013

Advances in Intelligent Systems Research

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-This paper aims to improve the performance of partial least squares regression, and then, improve efficiency of its implementation. In this paper we provide a novel derivation based on optimization for the partial least squares (PLS) algorithm. The derivation shows that only one of either the X-or the Y-matrix needs to be deflated during the sequential process of computing latent factors. And then, based on this derivation, an improved recursive exponentially weighted PLS regression algorithm was proposed. And the improved algorithm is obviously superior to traditional PLS regression algorithm on performance.Index Terms -partial least squares (PLS), kernel algorithm, algorithms improvement, recursive exponentially weighted algorithms

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A Predictive Approach to the Random Effect Model

Cited by 748 publications

References 2 publications

Entrepreneurial orientation and firm performance in family SMEs: the moderating effects of family, women, and strategic involvement in the board of directors

Entrepreneurial orientation and firm performance in family SMEs: the moderating effects of family, women, and strategic involvement in the board of directors

Customer value perception: cross-generational preferences for wine

Improved Partial Least Squares Regression Recommendation Algorithm

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