scholarly journals EXPRESS: Timing Market Entry – the Mediation Effect of Market Potential

2021 ◽  
pp. 1069031X2110680
Author(s):  
Towhidul Islam ◽  
Nigel Meade ◽  
Ashish Sood
Keyword(s):  
Market Entry ◽  
Growth Mixture Modeling ◽  
Market Potential ◽  
Mediation Effect ◽  
Mediating Role ◽  
Growth Mixture ◽  
Entry Timing ◽  
Negative Impacts ◽  
Penetration Data ◽  
Long Term Impact

Timing a multinational firm’s entry into a new country is a pivotal decision with long-term impact on the firm’s overall performance, thus a deeper understanding of the drivers of the decision and their interrelationship can yield significant managerial benefits. We explore the mediating role of market potential by decomposing the total effects of the decision’s main drivers—macro-economic attractiveness, market concentration, social heterogeneity, population density—into direct and indirect effects. These decompositions explain the countervailing effects of some drivers that simultaneously make both positive and negative impacts. Our dataset encompasses mobile 4G broadband penetration in 130 countries, including market entry timings for 28 international operators in 79 countries. We establish the nature of the mediation effect of market potential on the drivers of entry timing. Using early penetration data, we utilize growth mixture modeling to divide the countries into four latent segments. We validate this segmentation using machine learning with the four key drivers as classifiers; the process establishes macro-economic attractiveness as the predominant classifier. Our analysis offers entry-timing guidance at both pre- and post-launch stages.

A dark side of e-HRM: mediating role of HR service delivery and HR socialization on HR effectiveness

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Asim Talukdar ◽  
Anirban Ganguly
Keyword(s):  
Service Delivery ◽  
Mediation Effect ◽  
Dark Side ◽  
Negative Consequences ◽  
Content Type ◽  
Mediating Role ◽  
Common Method Bias ◽  
Parallel Effect ◽  
Negative Effect ◽  
Negative Impacts

PurposeThe primary aim of this paper is to study a dark side of e-HRM concerning its parallel effect on human resource (HR) socialization and HR service delivery and the consequent impact of perceived HR effectiveness.Design/methodology/approachThe current study started with an in-depth review of the extant literature in the field of e-HRM to derive a set of constructs. Based on the theoretical foundation of the identified constructs, the current study went on to derive a set of hypotheses, which was subsequently validated using the uses the quantitative technique of PLS-SEM. A primary survey, in the form of a structured questionnaire, was used as the source for data collection on a sample size of 276 from the Indian industrial domain. Careful attention was paid to eliminate the common method bias in the study.FindingsThe findings of this study show a simultaneous significant full mediation effect of both HR service delivery and HR socialization is the relationship between e-HRM and HR effectiveness. However, e-HRM has a strong and significant negative effect on HR socialization. Though HR socialization is positively related to HR effectiveness, the significantly reduced level of HR socialization as a consequence of adaption of e-HRM had negatively affected the perceived HR effectiveness.Originality/valueAlthough the dark side of e-HRM has been recognized by academicians and practitioners alike, its implications have seldom been studied in the academic literature. The current study intends to shed some light on this important, but sparsely discussed topic. Further, this study makes significant and meaningful contributions in the literature of e-HRM by empirically studying together the positive and negative consequences of e-HRM and its effects on HR effectiveness. Several e-HRM scholars have discussed the implications of e-HRM adoption and highlighted the negative impacts of e-HRM, and traversing the same path, the current study advances the literature by empirically investigating the effect of e-HRM on the dehumanization of HR processes and practices.

Die Identifikation früher Veränderungsmuster in der ambulanten Psychotherapie

2007 ◽  
Vol 36 (2) ◽  
pp. 93-104 ◽  
Author(s):  
Wolfgang Lutz ◽  
Niklaus Stulz ◽  
David W. Smart ◽  
Michael J. Lambert
Keyword(s):  
Growth Mixture Modeling ◽  
Mixture Modeling ◽  
Growth Mixture

Zusammenfassung. Theoretischer Hintergrund: Im Rahmen einer patientenorientierten Psychotherapieforschung werden Patientenausgangsmerkmale und Veränderungsmuster in einer frühen Therapiephase genutzt, um Behandlungsergebnisse und Behandlungsdauer vorherzusagen. Fragestellung: Lassen sich in frühen Therapiephasen verschiedene Muster der Veränderung (Verlaufscluster) identifizieren und durch Patientencharakteristika vorhersagen? Erlauben diese Verlaufscluster eine Vorhersage bezüglich Therapieergebnis und -dauer? Methode: Anhand des Growth Mixture Modeling Ansatzes wurden in einer Stichprobe von N = 2206 ambulanten Patienten einer US-amerikanischen Psychotherapieambulanz verschiedene latente Klassen des frühen Therapieverlaufs ermittelt und unter Berücksichtigung unterschiedlicher Patientenausgangscharakteristika als Prädiktoren der frühen Veränderungen mit dem Therapieergebnis und der Therapiedauer in Beziehung gesetzt. Ergebnisse: Für leicht, mittelschwer und schwer beeinträchtigte Patienten konnten je vier unterschiedliche Verlaufscluster mit jeweils spezifischen Prädiktoren identifiziert werden. Die Identifikation der frühen Verlaufsmuster ermöglichte weiterhin eine spezifische Vorhersage für die unterschiedlichen Verlaufscluster bezüglich des Therapieergebnisses und der Therapiedauer. Schlussfolgerungen: Frühe Psychotherapieverlaufsmuster können einen Beitrag zu einer frühzeitigen Identifikation günstiger sowie ungünstiger Therapieverläufe leisten.

scholarly journals Association between socioeconomic status and longitudinal sleep quality patterns mediated by depressive symptoms

SLEEP ◽  
2021 ◽  
Author(s):  
Ga Bin Lee ◽  
Hyeon Chang Kim ◽  
Ye Jin Jeon ◽  
Sun Jae Jung
Keyword(s):  
Socioeconomic Status ◽  
Depressive Symptoms ◽  
Sleep Quality ◽  
Household Income ◽  
Pittsburgh Sleep Quality Index ◽  
Multinomial Logistic Regression ◽  
Mental Healthcare ◽  
Mediation Effect ◽  
Multinomial Logistic Regression Model ◽  
Mediating Role

Abstract Study Objectives We aimed to examine whether associations between socioeconomic status (SES) and longitudinal sleep quality patterns are mediated by depressive symptoms. Methods We utilized data on 3347 participants in the Korean Genome and Epidemiology Study aged 40–69 years at baseline from 2001 to 2002 who were followed up for 16 years. A group-based modeling approach was used to identify sleep quality trajectories using the Pittsburgh Sleep Quality Index (years 2, 6, 8, 10, and 12). Educational attainment (college graduated or less), monthly household income (≥$2500 or less), and occupation (unemployed, manual labor, and professional labor) at baseline (year 0) were used for analyses. Depressive symptoms were assessed using Beck’s Depression Inventory at year 4. Associations between SES and sleep quality patterns were examined using a multinomial logistic regression model. The mediation effect of depressive symptoms was further examined using PROC CAUSALMED. Results We identified five distinct sleep quality trajectories: “normal-stable” (n = 1697), “moderate-stable” (n = 1157), “poor-stable” (n = 320), “developing to poor” (n = 84), and “severely poor-stable” (n = 89). Overall, associations between SES levels and longitudinal sleep patterns were not apparent after full adjustment for sociodemographic and lifestyle factors measured at baseline. Depressive symptoms, however, tended to fully mediate associations between SES levels and sleep quality patterns (odds ratio range for indirect effects of depressive symptoms: for education, 1.05-1.17; for income, 1.05-1.15). Conclusion A significant mediating role for depressive symptoms between SES levels and longitudinal sleep quality warrants consideration among mental healthcare professionals.

Understanding the Relationship Between Customer Satisfaction, Customer Engagement and Repeat Purchase Behaviour

2021 ◽  
pp. 097226292199259
Author(s):  
Devika Rani Sharma ◽  
Balgopal Singh
Keyword(s):  
Customer Satisfaction ◽  
Relationship Building ◽  
Customer Relationship ◽  
Mediation Effect ◽  
Customer Engagement ◽  
Mediating Role ◽  
Repeat Purchase ◽  
Building Activities ◽  
The Relationship

Emergence of technology has not only boosted the growth of customer engagement but has also paved way for customers to become active co-creators with the firms. Customer engagement activities are taking over the customer relationship building activities in the present scenario. Customers’ experience with a particular brand has its impact on satisfaction levels and their repurchasing intention in future as well. According to Rosetta Consulting report an engaged customer is likely to buy 90% more frequently and may spend 300% more than other customers. Hence, the present has tried to understand the mediating role of satisfaction on customer engagement in retaining the customers or persuading the customers to repurchase. The results show that there exists a significant mediation effect of customer satisfaction in influencing their repeat purchase behaviour.

scholarly journals Differential relationships of stress and HIV disclosure by gender: a person centered longitudinal study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chengbo Zeng ◽  
Shan Qiao ◽  
Xiaoming Li ◽  
Xueying Yang ◽  
Zhiyong Shen ◽  
...  
Keyword(s):  
Multinomial Logistic Regression ◽  
Growth Mixture Modeling ◽  
Hiv Disclosure ◽  
Lower Probability ◽  
People Living With Hiv ◽  
Target Number ◽  
Cross Sectional ◽  
Growth Mixture ◽  
Cross Sectional Design ◽  
Living With Hiv

Abstract Background Existing literature mostly consider HIV disclosure as a static event and investigate its relationship with stress using a cross-sectional design. It is unclear about the dynamic changes of HIV disclosure levels (defined as the number of disclosure targets) and how stress may influence these changes. This study explored different disclosure levels using a person-centered longitudinal approach, examined whether stress could predict these disclosure levels, and investigated if this relationship differed by gender among people living with HIV (PLWH). Methods Data were derived from a prospective cohort study conducted from November 2016 to January 2018 in Guangxi, China. Four hundred forty-four PLWH were included. Participants were assessed on perceived stress, sociodemographic characteristics, and number of HIV disclosure targets at baseline, 6-month, and 12-month follow-ups. Growth mixture modeling was used to characterize disclosure levels based on the changes of disclosure target number. Multinomial logistic regression was used to predict disclosure levels with baseline stress after adjusting for covariates. The interaction effect of stress by gender was examined. Adjusted odds ratio (AOR) with its 95% confidence interval were reported to show the strength of association.  Results Three levels of disclosure were characterized as “Low levels of disclosure” (Level One), “Increased levels of disclosure” (Level Two), and “High levels of disclosure” (Level Three). Accordingly, 355 (81.2%), 28 (6.4%), and 64 (12.4%) of PLWH were categorized respectively under low, increased, and high levels of disclosure. The interaction of baseline stress by gender was significant in differentiating Level One from Three (AOR = 0.85 [0.74 ~ 0.99]) while it was not significant between Level One and Two (AOR = 0.96 [0.81 ~ 1.15]). Compared to female, male PLWH with higher baseline stress had lower probability to have consistent high disclosure levels over time. PLWH who were married/cohabited had lower probability of being classified into consistent high levels of disclosure than low level (AOR = 0.43 [0.19 ~ 0.94]). Conclusions There was gender difference in the relationship between stress and levels of HIV disclosure. To promote HIV disclosure, gender tailored interventions should be employed to help PLWH cope with stress.

A Monte Carlo evaluation of growth mixture modeling

2021 ◽  
pp. 1-14
Author(s):  
Tiffany M. Shader ◽  
Theodore P. Beauchaine
Keyword(s):  
Monte Carlo ◽  
Growth Mixture Modeling ◽  
Mixture Modeling ◽  
Fit Indices ◽  
Linear Quadratic ◽  
Growth Mixture ◽  
True Number ◽  
Monte Carlo Evaluation ◽  
Wrong Number ◽  
State Of Affairs

Abstract Growth mixture modeling (GMM) and its variants, which group individuals based on similar longitudinal growth trajectories, are quite popular in developmental and clinical science. However, research addressing the validity of GMM-identified latent subgroupings is limited. This Monte Carlo simulation tests the efficiency of GMM in identifying known subgroups (k = 1–4) across various combinations of distributional characteristics, including skew, kurtosis, sample size, intercept effect size, patterns of growth (none, linear, quadratic, exponential), and proportions of observations within each group. In total, 1,955 combinations of distributional parameters were examined, each with 1,000 replications (1,955,000 simulations). Using standard fit indices, GMM often identified the wrong number of groups. When one group was simulated with varying skew and kurtosis, GMM often identified multiple groups. When two groups were simulated, GMM performed well only when one group had steep growth (whether linear, quadratic, or exponential). When three to four groups were simulated, GMM was effective primarily when intercept effect sizes and sample sizes were large, an uncommon state of affairs in real-world applications. When conditions were less ideal, GMM often underestimated the correct number of groups when the true number was between two and four. Results suggest caution in interpreting GMM results, which sometimes get reified in the literature.

scholarly journals Methods and Measures: Growth mixture modeling: A method for identifying differences in longitudinal change among unobserved groups

2009 ◽  
Vol 33 (6) ◽  
pp. 565-576 ◽  
Author(s):  
Nilam Ram ◽  
Kevin J. Grimm
Keyword(s):  
Stress Response ◽  
Growth Curve ◽  
Growth Modeling ◽  
Growth Mixture Modeling ◽  
Mixture Modeling ◽  
Longitudinal Change ◽  
Growth Curve Model ◽  
Growth Mixture ◽  
Multiple Group ◽  
Curve Model

Growth mixture modeling (GMM) is a method for identifying multiple unobserved sub-populations, describing longitudinal change within each unobserved sub-population, and examining differences in change among unobserved sub-populations. We provide a practical primer that may be useful for researchers beginning to incorporate GMM analysis into their research. We briefly review basic elements of the standard latent basis growth curve model, introduce GMM as an extension of multiple-group growth modeling, and describe a four-step approach to conducting a GMM analysis. Example data from a cortisol stress-response paradigm are used to illustrate the suggested procedures.

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