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Article
Publication date: 14 October 2013

Evangelos Kalampokis, Efthimios Tambouris and Konstantinos Tarabanis

The purpose of this paper is to consolidate existing knowledge and provide a deeper understanding of the use of social media (SM) data for predictions in various areas…

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7025

Abstract

Purpose

The purpose of this paper is to consolidate existing knowledge and provide a deeper understanding of the use of social media (SM) data for predictions in various areas, such as disease outbreaks, product sales, stock market volatility and elections outcome predictions.

Design/methodology/approach

The scientific literature was systematically reviewed to identify relevant empirical studies. These studies were analysed and synthesized in the form of a proposed conceptual framework, which was thereafter applied to further analyse this literature, hence gaining new insights into the field.

Findings

The proposed framework reveals that all relevant studies can be decomposed into a small number of steps, and different approaches can be followed in each step. The application of the framework resulted in interesting findings. For example, most studies support SM predictive power, however, more than one-third of these studies infer predictive power without employing predictive analytics. In addition, analysis suggests that there is a clear need for more advanced sentiment analysis methods as well as methods for identifying search terms for collection and filtering of raw SM data.

Originality/value

The proposed framework enables researchers to classify and evaluate existing studies, to design scientifically rigorous new studies and to identify the field's weaknesses, hence proposing future research directions.

Details

Internet Research, vol. 23 no. 5
Type: Research Article
ISSN: 1066-2243

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Article
Publication date: 25 June 2019

Galit Shmueli, Marko Sarstedt, Joseph F. Hair, Jun-Hwa Cheah, Hiram Ting, Santha Vaithilingam and Christian M. Ringle

Partial least squares (PLS) has been introduced as a “causal-predictive” approach to structural equation modeling (SEM), designed to overcome the apparent dichotomy…

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4180

Abstract

Purpose

Partial least squares (PLS) has been introduced as a “causal-predictive” approach to structural equation modeling (SEM), designed to overcome the apparent dichotomy between explanation and prediction. However, while researchers using PLS-SEM routinely stress the predictive nature of their analyses, model evaluation assessment relies exclusively on metrics designed to assess the path model’s explanatory power. Recent research has proposed PLSpredict, a holdout sample-based procedure that generates case-level predictions on an item or a construct level. This paper offers guidelines for applying PLSpredict and explains the key choices researchers need to make using the procedure.

Design/methodology/approach

The authors discuss the need for prediction-oriented model evaluations in PLS-SEM and conceptually explain and further advance the PLSpredict method. In addition, they illustrate the PLSpredict procedure’s use with a tourism marketing model and provide recommendations on how the results should be interpreted. While the focus of the paper is on the PLSpredict procedure, the overarching aim is to encourage the routine prediction-oriented assessment in PLS-SEM analyses.

Findings

The paper advances PLSpredict and offers guidance on how to use this prediction-oriented model evaluation approach. Researchers should routinely consider the assessment of the predictive power of their PLS path models. PLSpredict is a useful and straightforward approach to evaluate the out-of-sample predictive capabilities of PLS path models that researchers can apply in their studies.

Research limitations/implications

Future research should seek to extend PLSpredict’s capabilities, for example, by developing more benchmarks for comparing PLS-SEM results and empirically contrasting the earliest antecedent and the direct antecedent approaches to predictive power assessment.

Practical implications

This paper offers clear guidelines for using PLSpredict, which researchers and practitioners should routinely apply as part of their PLS-SEM analyses.

Originality/value

This research substantiates the use of PLSpredict. It provides marketing researchers and practitioners with the knowledge they need to properly assess, report and interpret PLS-SEM results. Thereby, this research contributes to safeguarding the rigor of marketing studies using PLS-SEM.

Details

European Journal of Marketing, vol. 53 no. 11
Type: Research Article
ISSN: 0309-0566

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Abstract

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Applying Partial Least Squares in Tourism and Hospitality Research
Type: Book
ISBN: 978-1-78756-700-9

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Article
Publication date: 5 January 2015

Hernaldo Saldías Molina, Juan Dixon Rojas and Luis Morán Tamayo

The purpose of this paper is to implement a finite set model predictive control algorithm to a shunt (or parallel), multilevel (cascaded H-bridge) active power filter…

Abstract

Purpose

The purpose of this paper is to implement a finite set model predictive control algorithm to a shunt (or parallel), multilevel (cascaded H-bridge) active power filter (APF). Specifically, the purpose is to get a controller that could compensate the mains current and, at the same time, to control the voltages of its capacitors. This strategy avoids the use of multiple PWM carriers or another type of special modulator, and requires a relatively low processing power.

Design/methodology/approach

This paper is focussed in the application of the predictive controller to a single-phase parallel APF composed for two H-bridges connected in series. The same methodology can be applied to a three-phase APF. In the DC buses of each H-bridge, a floating capacitor was connected, whose voltage is regulated by the predictive controller. The controller is composed by, first, a model for the charge/discharge dynamics for each floating capacitor and a model for the output current of the APF; second, a cost function; and third, an optimization algorithm that is able to control all these variables at the same time, choosing in each sample period the best combination of firing pulses.

Findings

The controller can track the voltage references, compensate the current harmonics and compensate reactive power with an algorithm that evaluates only the three nearest voltage levels to the last voltage level applied in the inverter. This strategy decreases the number of calculations required by the predictive algorithm. This controller can be applied to the general case of a single-phase multilevel APF of N-levels and extend it to the three-phase case without major problems.

Research limitations/implications

The implemented controller, when the authors consider a constant sample time, gives a mains current with a Total Harmonic Distortion (THD-I) slightly greater in comparison with the base algorithm (that evaluates all the voltage levels). However, when the authors consider the processing times under the same processor, the implemented algorithm requires less time to get the optimal values, can get lower sampling times and then a best performance in terms of THD-I. To implement the controller in a three-phase APF, a faster Digital Signal Processor would be required.

Originality/value

The implemented solution uses a model for the charge/discharge of the capacitors and for the filter current that enable to operate the cascaded multilevel inverter with asymmetrical voltages while compensates the mains currents, with a predictive algorithm that requires a relatively low amount of calculations.

Details

COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, vol. 34 no. 1
Type: Research Article
ISSN: 0332-1649

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Article
Publication date: 1 October 2006

Mine Uğurlu and Hakan Aksoy

To identify predictors of corporate financial distress, using the discriminant and logit models, in an emerging market over a period of economic turbulence and to reveal…

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4464

Abstract

Purpose

To identify predictors of corporate financial distress, using the discriminant and logit models, in an emerging market over a period of economic turbulence and to reveal the comparative predictive and classification accuracies of the models in this different environmental setting.

Design/methodology/approach

The research relies on a sample of 27 failed and 27 non‐failed manufacturing firms listed in the Istanbul Stock Exchange over the 1996‐2003 period, which includes a period of high economic growth (1996‐1999) followed by an economic crisis period (2000‐2002). The two well‐known methods, discriminant analysis and logit, are compared on the basis of a better overall fit and a higher percentage of correct classification under changing economic conditions. Furthermore, this research attempts to reveal the changes, if any, in the bankruptcy predictors, from those found in the earlier studies that rested on the data from the developed markets.

Findings

The logistic regression model is found to have higher classification power and predictive accuracy, over the four years prior to bankruptcy, than the discriminant model. In this research, the discriminant and logit models identify the same number of significant predictors out of the total variables analyzed, and six of these are common in both. EBITDA/total assets is the most important predictor of financial distress in both models. The logit model identifies operating profit margin and the proportion of trade credit within total claims ratios as the second and third most important predictors, respectively.

Originality/value

This paper reveals the accuracy with which the discriminant and logit models work in an emerging market over a period when firms face high uncertainty and turbulence. This study may be extended to other emerging markets to eliminate the limitation of the small sample size in this study and to further validate the use of these models in the developing countries. This can serve to make the methods important decision tools for managers and investors in these volatile markets.

Details

Cross Cultural Management: An International Journal, vol. 13 no. 4
Type: Research Article
ISSN: 1352-7606

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Article
Publication date: 26 July 2019

Cristiano A.B. Castro, Felipe Zambaldi and Mateus Canniatti Ponchio

This paper aims to conceptualize two dimensions of active innovation resistance (AIR): cognitive active resistance and emotional active resistance. A scale to measure this…

Abstract

Purpose

This paper aims to conceptualize two dimensions of active innovation resistance (AIR): cognitive active resistance and emotional active resistance. A scale to measure this construct is proposed and tested.

Design/methodology/approach

Three studies were conducted, with sample sizes of 195, 190 and 186, to test the discriminant, convergent, nomological and criterion validity of the proposed AIRc+e scale and to analyze its explanatory and predictive power. Data were gathered using the online platform of a US-based research company.

Findings

The authors provide evidence that AIR is a two-dimension construct comprising a cognitive and an emotional dimension. AIR was modeled as a third-order construct, comprising two second-order constructs, cognitive active resistance and emotional active resistance. The impact of adding an emotion dimension to active resistance was therefore assessed, and the results indicated that the explanatory and predictive power of the AIR measure improved as expected.

Practical implications

Consumers are most likely to resist innovations launched onto the marketplace, either prior to or after evaluating them. A better understanding of the reasons behind their resistance to innovation, as well as of its mechanisms, is of great importance in decreasing an innovation’s chances of failure.

Originality/value

This study proposes that incorporating emotion into the assessment of AIR will result in a deeper understanding of adoption and rejection behavior, expanding the current knowledge of consumer behavior in innovation-related, new product adoption and decisions.

Details

Journal of Product & Brand Management, vol. 29 no. 4
Type: Research Article
ISSN: 1061-0421

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Article
Publication date: 14 January 2019

Joseph F. Hair, Jeffrey J. Risher, Marko Sarstedt and Christian M. Ringle

The purpose of this paper is to provide a comprehensive, yet concise, overview of the considerations and metrics required for partial least squares structural equation…

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55336

Abstract

Purpose

The purpose of this paper is to provide a comprehensive, yet concise, overview of the considerations and metrics required for partial least squares structural equation modeling (PLS-SEM) analysis and result reporting. Preliminary considerations are summarized first, including reasons for choosing PLS-SEM, recommended sample size in selected contexts, distributional assumptions, use of secondary data, statistical power and the need for goodness-of-fit testing. Next, the metrics as well as the rules of thumb that should be applied to assess the PLS-SEM results are covered. Besides presenting established PLS-SEM evaluation criteria, the overview includes the following new guidelines: PLSpredict (i.e., a novel approach for assessing a model’s out-of-sample prediction), metrics for model comparisons, and several complementary methods for checking the results’ robustness.

Design/methodology/approach

This paper provides an overview of previously and recently proposed metrics as well as rules of thumb for evaluating the research results based on the application of PLS-SEM.

Findings

Most of the previously applied metrics for evaluating PLS-SEM results are still relevant. Nevertheless, scholars need to be knowledgeable about recently proposed metrics (e.g. model comparison criteria) and methods (e.g. endogeneity assessment, latent class analysis and PLSpredict), and when and how to apply them to extend their analyses.

Research limitations/implications

Methodological developments associated with PLS-SEM are rapidly emerging. The metrics reported in this paper are useful for current applications, but must always be up to date with the latest developments in the PLS-SEM method.

Originality/value

In light of more recent research and methodological developments in the PLS-SEM domain, guidelines for the method’s use need to be continuously extended and updated. This paper is the most current and comprehensive summary of the PLS-SEM method and the metrics applied to assess its solutions.

Details

European Business Review, vol. 31 no. 1
Type: Research Article
ISSN: 0955-534X

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Article
Publication date: 7 March 2008

Chen‐Yuan Chen, Hsien‐Chueh Peter Yang, Cheng‐Wu Chen and Tsung‐Hao Chen

This study aims to apply a systematic statistical approach, including several plot indexes, to diagnose the goodness of fit of a logistic regression model, and then to…

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1492

Abstract

Purpose

This study aims to apply a systematic statistical approach, including several plot indexes, to diagnose the goodness of fit of a logistic regression model, and then to detect the outliers and influential observations of the data from experimental data.

Design/methodology/approach

The proposed statistical approach is applied to analyze some experimental data on internal solitary wave propagation.

Findings

A suitable logistic regression model in which the relationship between the response variable and the explanatory variables is found. The problem of multicollinearity is tested. It was found that certain observations would not have the problem of multicollinearity. The P‐values for both the Pearson and deviance χ2 tests are greater than 0.05. However, the Pearson χ2 value is larger than the degrees of freedom. This finding indicates that although this model fits the data, it has a slight overdispersion. After three outliers and influential observations (cases 11, 27, and 49) are removed from the data, and the remaining observations are refitted the goodness‐of‐fit of the revised model to the data is improved.

Practical implications

A comparison of the four predictive powers: R2, max‐rescaled R2, the Somers' D, and the concordance index c, shows that the revised model has better predictive abilities than the original model.

Originality/value

The goodness‐of‐fit and prediction ability of the revised logistic regression model are more appropriate than those of the original model.

Details

Engineering Computations, vol. 25 no. 2
Type: Research Article
ISSN: 0264-4401

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Article
Publication date: 4 June 2018

Sung-Shun Weng, Ming-Hsien Yang and Pei-I Hsiao

An important issue for researchers and managers of organizations is the understanding of user-perceived values of collective intelligence (UPVoCI) in online social…

Abstract

Purpose

An important issue for researchers and managers of organizations is the understanding of user-perceived values of collective intelligence (UPVoCI) in online social networks (OSNs) with the purpose of helping organizations identify the values that cause internet users and members of OSNs to share information and knowledge during they participate in collective intelligence (co-intelligence) activities. However, the development of measurement instruments and predictive models and rules for predicting UPVoCI are inadequate. The paper aims to discuss these issues.

Design/methodology/approach

A novel measurement scale was developed to measure UPVoCI using a user-oriented research strategy that is based on qualitative and quantitative research methods. This work also identified critical indicators and constructed predictive models and rules for forecasting UPVoCI by multivariate statistical methods and data mining.

Findings

A 17-item scale of UPVoCI was developed and 17 measurement items were associated with two major dimensions, which are the user-perceived social value of co-intelligence and the user-perceived problem-solving value of co-intelligence. Ten critical indicators of UPVoCI that are important in predicting UPVoCI and 12 rules for predicting UPVoCI were identified and a refined model for predicting UPVoCI was constructed.

Research limitations/implications

The results in this work allow organizations to determine the perceived value of members of OSNs and the benefits of their participating in co-intelligence activities, as a basis for adjusting user-oriented online co-intelligence and service strategies with the goal of improving collaborative innovation performance.

Originality/value

This work systematically developed a novel scale for measuring UPVoCI in OSNs and constructed new models and rules for predicting UPVoCI in OSNs.

Details

Internet Research, vol. 28 no. 3
Type: Research Article
ISSN: 1066-2243

Keywords

Content available
Article
Publication date: 9 May 2016

Nicole Franziska Richter, Rudolf R. Sinkovics, Christian M. Ringle and Christopher Schlägel

Structural equation modeling (SEM) has been widely used to examine complex research models in international business and marketing research. While the covariance-based SEM…

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16914

Abstract

Purpose

Structural equation modeling (SEM) has been widely used to examine complex research models in international business and marketing research. While the covariance-based SEM (CB-SEM) approach is dominant, the authors argue that the field’s dynamic nature and the sometimes early stage of theory development more often require a partial least squares SEM (PLS-SEM) approach. The purpose of this paper is to critically review the application of SEM techniques in the field.

Design/methodology/approach

The authors searched six journals with an international business (and marketing) focus (Management International Review, Journal of International Business Studies, Journal of International Management, International Marketing Review, Journal of World Business, International Business Review) from 1990 to 2013. The authors reviewed all articles that apply SEM, analyzed their research objectives and methodology choices, and assessed whether the PLS-SEM papers followed the best practices outlined in the past.

Findings

Of the articles, 379 utilized CB-SEM and 45 PLS-SEM. The reasons for using PLS-SEM referred largely to sampling and data measurement issues and did not sufficiently build on the procedure’s benefits that stem from its design for predictive and exploratory purposes. Thus, the procedure’s key benefits, which might be fruitful for the theorizing process, are not being fully exploited. Furthermore, authors need to better follow best practices to truly advance theory building.

Research limitations/implications

The authors examined a subset of journals in the field and did not include general management journals that publish international business and marketing-related studies. Fur-thermore, the authors found only limited use of PLS-SEM in the journals the authors considered relevant to the study.

Originality/value

The study contributes to the literature by providing researchers seeking to adopt SEM as an analytical method with practical guidelines for making better choices concerning an appropriate SEM approach. Furthermore, based on a systematic review of current practices in the international business and marketing literature, the authors identify critical challenges in the selection and use of SEM procedures and offer concrete recommendations for better practice.

Details

International Marketing Review, vol. 33 no. 3
Type: Research Article
ISSN: 0265-1335

Keywords

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