Developments in Higher-Order PLS-PM for the building of a system of Composite Indicators

Cataldo, Rosanna; Grassia, Maria Gabriella; Lauro, Natale Carlo; Marino, Marina

doi:10.1007/s11135-016-0431-1

Cited by 36 publications

(32 citation statements)

References 15 publications

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“…There are different alternative approaches to measure composite indicators. Cataldo (2016) explained that the approaches may be theory-based, which is an approach by combining variables suggested by a theory or well-established knowledge on the subject matter; whereas, data-driven approaches follow a best possible quantitative synthesis of a suitable set of elementary indicators, which represents the different aspects of an analyzed phenomenon. However, both approaches have their limits, hence; scholars introduced a model-based approach, which is in the mid-way between the previous approaches.…”

Section: Methodological Frameworkmentioning

confidence: 99%

“…However, both approaches have their limits, hence; scholars introduced a model-based approach, which is in the mid-way between the previous approaches. This model is used to test and or estimate casual or proxy relationships of composite statistical data and qualitative assumptions (Cataldo, 2016;Hair et al, 2017). Among model-based approaches, Structural Equation Modeling (SEM) methodology was used in this study.…”

Section: Methodological Frameworkmentioning

confidence: 99%

“…To measure adaptive capacity (Higher Order Construct), each CI is related to other CI in a systemic vision by linear regression equations specifying the so-called Structural Model (or Inner Model). Accordingly, a systemic CI or a system of CIs was obtained, where the word "systemic" derives from system theory (Cataldo, 2016). Similarly, Fullan (2011 p.530) defined an organization's capacity for change as a "collective capacity building" and that collective capacity is exemplified by the team, the group and/or the system.…”

Section: Adaptive Capacity Path Modeling Using Xlstat-plspmmentioning

confidence: 99%

“…To estimate adaptive capacity the two-step approach was followed. Cataldo (2016) recommended this approach over hybrid and repeated measures approach of measuring higher-order construct due to its ability to explain relationships and parametric estimation. By following a two-step approach, the estimation was made.…”

Section: Figure 1 Latent and Manifest Variables Of The Multidimensiomentioning

confidence: 99%

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Measuring and Modelling the Adaptive Capacity of Universities: A Composite Indicators Methodology

Gedifew¹,

Muluneh²

2019

PAR

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Initiating, implementing, and sustaining reforms are the most daunting and challenging aspects of change in higher education institutions. This research argues that to survive and thrive in a changing environment, adaptive capacity is fundamental. Thus, this study aims to serve two purposes. While the leading purpose was measuring the adaptive capacity of universities using a conceptual model developed based on literature review, introducing the critical components that determine adaptive capacity was secondary. Therefore, guided by a pragmatic approach this study has tried to maintain both subjective reflections and objective data during data collection and analysis. Accordingly, to attain the purpose of the study mixed research design particularly, a sequential explanatory approach was used. To gather relevant data from the target population, the questionnaire was distributed and 594 completed and useful questionnaires were returned from 133 leaders at different position, 268 academic staffs and 193 support staffs, which was a 62.06% response rate. Besides, 15 interviews and documents were reviewed. Finally, among the composite indicators approach, structural equation modeling (SEM) mainly principal least square path modeling (PLS-PM) was used to measure and analyze adaptive capacity using XLSTAT software. As a result, the adaptive capacity of universities was found unsatisfactory i.e. lower than moderate. This indicates universities might not fully implement the change ideas they wish to introduce into their system. Consequently, the identified gaps and challenges in the studied universities strengthen the argument for the need to assess universities adaptive capacity using a model and make appropriate intervention before struggling to introduce any change initiative without a fertile ground.

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Section: Methodological Frameworkmentioning

confidence: 99%

Section: Methodological Frameworkmentioning

confidence: 99%

Section: Adaptive Capacity Path Modeling Using Xlstat-plspmmentioning

confidence: 99%

Section: Figure 1 Latent and Manifest Variables Of The Multidimensiomentioning

confidence: 99%

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Measuring and Modelling the Adaptive Capacity of Universities: A Composite Indicators Methodology

Gedifew¹,

Muluneh²

2019

PAR

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“…According to the research model (Figure 1), two reflective (mode A) composites configure the first step constructs that define CI effort as a formative composite. A two-step approach was conducted [47] because when the model defines the reflective first-order constructs and formative second-order construct, the two step approach works better than the repeated indicators approach [53]. Additionally, it is more useful if the researcher is mainly interested in higher-level estimates [48].…”

Section: Out Er Model Validationmentioning

confidence: 99%

Competitive Intelligence and Sustainable Competitive Advantage in the Hotel Industry

et al. 2019

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Competitive intelligence (CI) is a business tool within strategic management, and it is gaining significance as a process that enables companies to achieve sustainable competitive advantage. This study explores the current state of CI in the Spanish hotel industry. For this purpose, a path model has been developed which empirically investigates the relationship between CI use and its backgrounds. The results obtained suggest that environmental and organizational characteristics affect CI effort, and in turn, CI effort affects CI use. This study provides theoretical and practical implications to help managers develop sustainable competitive advantages through the potential that CI offers within the hotel industry.

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What matters most to the perception of community resilience in Romania?

Druică,

Vâlsan,

Aligică

2024

Journal Community Psychology

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We aim to measure and explain the perception of community resilience in Romania. We use survey data from a country‐representative sample of 1500 respondents. We rely on factor‐based partial least squares path modeling to measure five reflective latent constructs from a CCRAM‐type questionnaire. We use these constructs to extract a second‐order formative latent construct representing an overall measure of community resilience. Next, we use three sub‐dimensions of family resilience, along with individual resilience and several control variables to explain community resilience. Among the five sub‐dimensions of the overall measure of community resilience, social trust exerts the highest contribution, followed by place attachment. The predictors of community resilience with the largest effect sizes are the three sub‐dimensions of family resilience. The policies geared towards increasing community resilience might not be able to address the most important factors, at least in the case of Romania, because they pertain to informal group interaction, and lie outside the reach of formal administrative authority.

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Developments in Higher-Order PLS-PM for the building of a system of Composite Indicators

Cited by 36 publications

References 15 publications

Measuring and Modelling the Adaptive Capacity of Universities: A Composite Indicators Methodology

Measuring and Modelling the Adaptive Capacity of Universities: A Composite Indicators Methodology

Competitive Intelligence and Sustainable Competitive Advantage in the Hotel Industry

What matters most to the perception of community resilience in Romania?

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