Abud F. week 3 discussion #2I’m starting off with this ad .docxdaniahendric
Abud F. week 3 discussion
#2
I’m starting off with this ad by loreal for two reasons. The first being that in our last class I wrote my paper on the feminist movement. The second being that I come from a family of extremely strong, confident, exceptional women, so ads like this really speak to me. This ad has several ways that it draws you in. The obvious thing: The title says it’s an ad for men, yet it’s an ad from a makeup company that traditionally sells makeup for women. That in itself is very catchy. The ad claims that profitability will be higher the more women there are in leadership roles. I don’t know if this is real fact, but it can be researched. I love this ad. I think its powerful.
#1
Next up is this advertisement that at first glance you think might be for Marlboro cigarettes. Then it grabs you. Why are there children’s crayons in a box of cigarettes? This one is black and white except for the crayons themselves. Really catches the eyes that way. The statement, “DO YOU WANT THEM TO TAKE AFTER EVERYTHING YOU DO?” plays to your emotions. Obviously, you wouldn’t want your children to pick up your unhealthy habits. This is an extremely powerful usage of emotion and color.
#3
This ad uses a very powerful visual. It poses a question and the visual poses as the possible answer to the stated question. The posed question is: “DO YOU KNOW HOW MUCH YOU REALLY SPEND ON CIGARETTES?”. The question is also small compared to the visual of the car in the shape of a cigarette butt being “put out”. This in and of itself is a powerful image as well. This is food for thought. How much money could you really save if you didn’t spend it on cigarettes? A car? Maybe. Quit and save. You’ll find out. It’s very convincing.
Editor’s Comments
EDITOR’S COMMENTS
A Critical Look at the Use of PLS-SEM in MIS Quarterly
By: Christian M. Ringle
Professor of Management
Hamburg University of Technology (TUHH) and
University of Newcastle (Australia)
[email protected]
Marko Sarstedt
Assistant Professor of Quantitative Methods in Marketing and Management
Ludwig-Maximilians-University Munich and
University of Newcastle (Australia)
[email protected]
Detmar W. Straub
Editor-in-Chief, MIS Quarterly
Professor of CIS
Georgia State University
[email protected]
Introduction
Wold’s (1974, 1982) partial least squares structural equation modeling (PLS-SEM) approach and the advanced PLS-SEM algorithms
by Lohmöller (1989) have enjoyed steady popularity as a key multivariate analysis method in management information systems (MIS)
research (Gefen et al. 2011). Chin’s (1998b) scholarly work and technology acceptance model (TAM) applications (e.g., Gefen and
Straub 1997) are milestones that helped to reify PLS-SEM in MIS research. In light of the proliferation of SEM techniques, Gefen et
al. (2011), updating Gefen et al. (2000), presented a comprehensive, organized, and contemporary summary of the minimum reporting
requirements for SEM applications.
Such guidelines ...
This document presents a literature review and proposed framework for structuring research on business models (BMs). The framework categorizes BM research into six sub-domains: definitions, components, taxonomies, representations, change methodologies, and evaluation models. The framework maps these sub-domains based on their level of integration with other areas and timeliness for further research. Existing literature is then reviewed and organized within this framework, showing focus on definitions and components but lack of work synthesizing all areas into a comprehensive BM theory. The framework aims to advance BM research by providing a structure to identify gaps and opportunities.
This document summarizes a text mining analysis of 625 publications related to business analytics and supply chain/operations management from 1994-2015. The analysis identified several key findings:
1) There was a large increase in practical/industry publications from 2001-2005, while academic publications increased more steadily over time and saw a large rise after 2010.
2) Topic analysis showed academic publications had more narrowly focused topics, while practical publications discussed more diverse and technique-oriented topics.
3) Cluster analysis was used to group publications by similarity, identifying differences between academic and practical publications over time.
4) The top journals publishing on this topic were both information systems and traditional operations management journals, and more journals across disciplines
IRJET- Testing Improvement in Business Intelligence AreaIRJET Journal
1) The document discusses testing techniques in business intelligence and data warehousing. It examines how testing has evolved from an ad hoc process to a more systematic discipline.
2) While research has produced many sound testing methods, few have been successfully applied in industry due to a "testing gap" between research and practice. Methods remain time-consuming and implementations are not well-automated.
3) The paper aims to analyze how testing techniques have matured, barriers to their adoption, and how to better transfer methods to industry use. It focuses on theoretical underpinnings of techniques and how they can be developed into systematic methodologies.
The document discusses a goal-oriented and ontology-driven requirement analysis method for extraction-transformation-loading (ETL) processes in data warehouse systems. It proposes using Tropos methodology and ontology modeling to systematically analyze requirements and transform them into formal ETL specifications. This would help address problems of defining and maintaining ETL specifications as well as handling semantic heterogeneity. The approach is demonstrated through a case study of the University of Utara Malaysia.
This document discusses the opportunities and challenges of using functional data analysis (FDA) to analyze data from electronic commerce (eCommerce). FDA is well-suited for eCommerce data because it can represent the combination of longitudinal and cross-sectional data that is common in eCommerce. Some challenges in applying FDA to eCommerce data include unevenly spaced time series, nonstationarity due to fast changes, and large databases. The document outlines methods for recovering functional objects from eCommerce data and discusses challenges in choosing the appropriate smoothing method.
Running head BUSINESS ANALYTICS1BUSINESS ANALYTICS 9.docxsusanschei
Running head: BUSINESS ANALYTICS 1
BUSINESS ANALYTICS 9
Business Analytics and Decision Making
Hakim Callahan
Argosy University
Contents
Introduction and Company Summary 3
Summary of Business Analytics 3
Benefits and Shortcomings of Business Analytics 4
Challenges of Applying Business Analytics 5
Business Analytics Techniques 6
a) Predictive Analytics 6
b) Decision Analytics 7
c) Descriptive Analytics 7
Implementation Plan 8
Backup Implementation Plan 9
Conclusion 9
References 10
Business Analytics and Decision Making Introduction and Company Summary
Business analytics is a platform for integrating technology, skills, and practices in exploring previous business events. BA is mainly important in forging the future through gathered insights and formulated business plans. In this context, business analytics will be applied to a design firm that has the resources of technology but does not engage in data analysis. Additionally, the design firm has not interconnected its technology systems, and as a result, the databases are independent. Looking forward, the company is aiming at opening a second branch of business. In rectifying the operational performance of the company in context, it is necessary that the company utilizes business analytics in the functions of decision making, description of historical data and predicting. The primary goal of this paper is to identify the role of business analytics in decision making. Summary of Business Analytics
The business in context has been defined above, and this section will work towards providing a summary of business analytics required in decision making. Before engaging in business analytics, it is important that the firm integrates its technology platform into a single system. Integrating technological components will help the firm to centralize its data for purposes of business analytics (Evans & Lindner, 2012). Primary, this firm can apply business analytics in analyzing historical data for purposes of developing trends that will help in the decision-making process. The multiple instances that the business analytics can be applied include understanding resource allocation, identifying the optimal number of employees and identifying the appropriate marketing mix. These scenarios will be critical in the opening of a new business branch that will yield optimal positive outcomes. Therefore business analytics will be applied towards predicting and simulating business conditions that will provide the optimal set of conditions. Benefits and Shortcomings of Business Analytics
Considering that the main goal of businesses is to provide valuable services and products to customers, business analytics provide a competitive advantage when appropriately utilized. The competitive advantage is provided through the alignment of business functions towards achieving consistent performance metrics. Secondly, the business analytics also help in making business information more understandable and usabl ...
RESEARCH ARTICLEA MULTILEVEL MODEL FOR MEASURING FIT BETWE.docxaudeleypearl
RESEARCH ARTICLE
A MULTILEVEL MODEL FOR MEASURING FIT BETWEEN A
FIRM’S COMPETITIVE STRATEGIES AND INFORMATION
SYSTEMS CAPABILITIES1
Tim S. McLaren
Ted Rogers School of Management, Ryerson University, 350 Victoria Street,
Toronto, ON M5B 2K3 CANADA {[email protected]}
Milena M. Head and Yufei Yuan
DeGroote School of Business, McMaster University, 1280 Main Street West,
Hamilton, ON L8S 4M4 CANADA {[email protected]} {[email protected]}
Yolande E. Chan
Queen’s School of Business, Queen’s University, 143 Union Street,
Kingston, ON K7L 3N6 CANADA {[email protected]}
To compete in a highly dynamic marketplace, firms must frequently adapt and align their competitive strategies
and information systems. The dominant literature on the strategic fit of a firm’s information systems focuses
primarily on high-level measures of the strategic fit of a firm’s overall IS portfolio and the impact of fit on
business performance. This paper addresses the need for a more fine-grained approach for assessing the
specific areas of misfit between a firm’s competitive strategies and IS capabilities. We describe the design and
evaluation of a multilevel strategic fit (MSF) measurement model that enables researchers and practitioners
to measure the strategic fit of a firm’s information systems at both an overall and a detailed level. The steps
in the model include identifying the relevant IS capabilities according to the type of system; measuring the
current level of support for each capability using a capabilities instrument; identifying the ideal level of support
for each capability using an adaptation of Conant et al.’s (1990) instrument to assess strategic archetype; and
comparing the ideal and realized level of support for each capability. Evidence from a multiple case study
analysis indicates that the fine-grained assessment of strategic fit can strengthen the validity, utility, and ease
of corroboration of the strategic fit measurement outputs. The paper also demonstrates how an iterative design
science research approach, with its emphasis on evaluating the utility of prototype artifacts, is well suited to
developing field-tested and theoretically grounded measurement models and instruments that are accessible
to practitioners. This focus on practical utility in turn provides researchers with results that can be more
readily corroborated, thus improving the quality and usefulness of the research findings.
Keywords: Strategic alignment, information systems capabilities, configurational theory, strategic archetypes,
design science, research methods
1
1Lars Mathiassen was the accepting senior editor for this paper. Shirley Gregor served as the associate editor.
The appendices for this paper are located in the “Online Supplements” section of the MIS Quarterly’s website (http://www.misq.org).
MIS Quarterly Vol. 35 No. 4 pp. 909-929/December 2011 909
McLaren et al./Measuring Fit Between Competitive Strategies and IS Capabilities
Intro ...
Abud F. week 3 discussion #2I’m starting off with this ad .docxdaniahendric
Abud F. week 3 discussion
#2
I’m starting off with this ad by loreal for two reasons. The first being that in our last class I wrote my paper on the feminist movement. The second being that I come from a family of extremely strong, confident, exceptional women, so ads like this really speak to me. This ad has several ways that it draws you in. The obvious thing: The title says it’s an ad for men, yet it’s an ad from a makeup company that traditionally sells makeup for women. That in itself is very catchy. The ad claims that profitability will be higher the more women there are in leadership roles. I don’t know if this is real fact, but it can be researched. I love this ad. I think its powerful.
#1
Next up is this advertisement that at first glance you think might be for Marlboro cigarettes. Then it grabs you. Why are there children’s crayons in a box of cigarettes? This one is black and white except for the crayons themselves. Really catches the eyes that way. The statement, “DO YOU WANT THEM TO TAKE AFTER EVERYTHING YOU DO?” plays to your emotions. Obviously, you wouldn’t want your children to pick up your unhealthy habits. This is an extremely powerful usage of emotion and color.
#3
This ad uses a very powerful visual. It poses a question and the visual poses as the possible answer to the stated question. The posed question is: “DO YOU KNOW HOW MUCH YOU REALLY SPEND ON CIGARETTES?”. The question is also small compared to the visual of the car in the shape of a cigarette butt being “put out”. This in and of itself is a powerful image as well. This is food for thought. How much money could you really save if you didn’t spend it on cigarettes? A car? Maybe. Quit and save. You’ll find out. It’s very convincing.
Editor’s Comments
EDITOR’S COMMENTS
A Critical Look at the Use of PLS-SEM in MIS Quarterly
By: Christian M. Ringle
Professor of Management
Hamburg University of Technology (TUHH) and
University of Newcastle (Australia)
[email protected]
Marko Sarstedt
Assistant Professor of Quantitative Methods in Marketing and Management
Ludwig-Maximilians-University Munich and
University of Newcastle (Australia)
[email protected]
Detmar W. Straub
Editor-in-Chief, MIS Quarterly
Professor of CIS
Georgia State University
[email protected]
Introduction
Wold’s (1974, 1982) partial least squares structural equation modeling (PLS-SEM) approach and the advanced PLS-SEM algorithms
by Lohmöller (1989) have enjoyed steady popularity as a key multivariate analysis method in management information systems (MIS)
research (Gefen et al. 2011). Chin’s (1998b) scholarly work and technology acceptance model (TAM) applications (e.g., Gefen and
Straub 1997) are milestones that helped to reify PLS-SEM in MIS research. In light of the proliferation of SEM techniques, Gefen et
al. (2011), updating Gefen et al. (2000), presented a comprehensive, organized, and contemporary summary of the minimum reporting
requirements for SEM applications.
Such guidelines ...
This document presents a literature review and proposed framework for structuring research on business models (BMs). The framework categorizes BM research into six sub-domains: definitions, components, taxonomies, representations, change methodologies, and evaluation models. The framework maps these sub-domains based on their level of integration with other areas and timeliness for further research. Existing literature is then reviewed and organized within this framework, showing focus on definitions and components but lack of work synthesizing all areas into a comprehensive BM theory. The framework aims to advance BM research by providing a structure to identify gaps and opportunities.
This document summarizes a text mining analysis of 625 publications related to business analytics and supply chain/operations management from 1994-2015. The analysis identified several key findings:
1) There was a large increase in practical/industry publications from 2001-2005, while academic publications increased more steadily over time and saw a large rise after 2010.
2) Topic analysis showed academic publications had more narrowly focused topics, while practical publications discussed more diverse and technique-oriented topics.
3) Cluster analysis was used to group publications by similarity, identifying differences between academic and practical publications over time.
4) The top journals publishing on this topic were both information systems and traditional operations management journals, and more journals across disciplines
IRJET- Testing Improvement in Business Intelligence AreaIRJET Journal
1) The document discusses testing techniques in business intelligence and data warehousing. It examines how testing has evolved from an ad hoc process to a more systematic discipline.
2) While research has produced many sound testing methods, few have been successfully applied in industry due to a "testing gap" between research and practice. Methods remain time-consuming and implementations are not well-automated.
3) The paper aims to analyze how testing techniques have matured, barriers to their adoption, and how to better transfer methods to industry use. It focuses on theoretical underpinnings of techniques and how they can be developed into systematic methodologies.
The document discusses a goal-oriented and ontology-driven requirement analysis method for extraction-transformation-loading (ETL) processes in data warehouse systems. It proposes using Tropos methodology and ontology modeling to systematically analyze requirements and transform them into formal ETL specifications. This would help address problems of defining and maintaining ETL specifications as well as handling semantic heterogeneity. The approach is demonstrated through a case study of the University of Utara Malaysia.
This document discusses the opportunities and challenges of using functional data analysis (FDA) to analyze data from electronic commerce (eCommerce). FDA is well-suited for eCommerce data because it can represent the combination of longitudinal and cross-sectional data that is common in eCommerce. Some challenges in applying FDA to eCommerce data include unevenly spaced time series, nonstationarity due to fast changes, and large databases. The document outlines methods for recovering functional objects from eCommerce data and discusses challenges in choosing the appropriate smoothing method.
Running head BUSINESS ANALYTICS1BUSINESS ANALYTICS 9.docxsusanschei
Running head: BUSINESS ANALYTICS 1
BUSINESS ANALYTICS 9
Business Analytics and Decision Making
Hakim Callahan
Argosy University
Contents
Introduction and Company Summary 3
Summary of Business Analytics 3
Benefits and Shortcomings of Business Analytics 4
Challenges of Applying Business Analytics 5
Business Analytics Techniques 6
a) Predictive Analytics 6
b) Decision Analytics 7
c) Descriptive Analytics 7
Implementation Plan 8
Backup Implementation Plan 9
Conclusion 9
References 10
Business Analytics and Decision Making Introduction and Company Summary
Business analytics is a platform for integrating technology, skills, and practices in exploring previous business events. BA is mainly important in forging the future through gathered insights and formulated business plans. In this context, business analytics will be applied to a design firm that has the resources of technology but does not engage in data analysis. Additionally, the design firm has not interconnected its technology systems, and as a result, the databases are independent. Looking forward, the company is aiming at opening a second branch of business. In rectifying the operational performance of the company in context, it is necessary that the company utilizes business analytics in the functions of decision making, description of historical data and predicting. The primary goal of this paper is to identify the role of business analytics in decision making. Summary of Business Analytics
The business in context has been defined above, and this section will work towards providing a summary of business analytics required in decision making. Before engaging in business analytics, it is important that the firm integrates its technology platform into a single system. Integrating technological components will help the firm to centralize its data for purposes of business analytics (Evans & Lindner, 2012). Primary, this firm can apply business analytics in analyzing historical data for purposes of developing trends that will help in the decision-making process. The multiple instances that the business analytics can be applied include understanding resource allocation, identifying the optimal number of employees and identifying the appropriate marketing mix. These scenarios will be critical in the opening of a new business branch that will yield optimal positive outcomes. Therefore business analytics will be applied towards predicting and simulating business conditions that will provide the optimal set of conditions. Benefits and Shortcomings of Business Analytics
Considering that the main goal of businesses is to provide valuable services and products to customers, business analytics provide a competitive advantage when appropriately utilized. The competitive advantage is provided through the alignment of business functions towards achieving consistent performance metrics. Secondly, the business analytics also help in making business information more understandable and usabl ...
RESEARCH ARTICLEA MULTILEVEL MODEL FOR MEASURING FIT BETWE.docxaudeleypearl
RESEARCH ARTICLE
A MULTILEVEL MODEL FOR MEASURING FIT BETWEEN A
FIRM’S COMPETITIVE STRATEGIES AND INFORMATION
SYSTEMS CAPABILITIES1
Tim S. McLaren
Ted Rogers School of Management, Ryerson University, 350 Victoria Street,
Toronto, ON M5B 2K3 CANADA {[email protected]}
Milena M. Head and Yufei Yuan
DeGroote School of Business, McMaster University, 1280 Main Street West,
Hamilton, ON L8S 4M4 CANADA {[email protected]} {[email protected]}
Yolande E. Chan
Queen’s School of Business, Queen’s University, 143 Union Street,
Kingston, ON K7L 3N6 CANADA {[email protected]}
To compete in a highly dynamic marketplace, firms must frequently adapt and align their competitive strategies
and information systems. The dominant literature on the strategic fit of a firm’s information systems focuses
primarily on high-level measures of the strategic fit of a firm’s overall IS portfolio and the impact of fit on
business performance. This paper addresses the need for a more fine-grained approach for assessing the
specific areas of misfit between a firm’s competitive strategies and IS capabilities. We describe the design and
evaluation of a multilevel strategic fit (MSF) measurement model that enables researchers and practitioners
to measure the strategic fit of a firm’s information systems at both an overall and a detailed level. The steps
in the model include identifying the relevant IS capabilities according to the type of system; measuring the
current level of support for each capability using a capabilities instrument; identifying the ideal level of support
for each capability using an adaptation of Conant et al.’s (1990) instrument to assess strategic archetype; and
comparing the ideal and realized level of support for each capability. Evidence from a multiple case study
analysis indicates that the fine-grained assessment of strategic fit can strengthen the validity, utility, and ease
of corroboration of the strategic fit measurement outputs. The paper also demonstrates how an iterative design
science research approach, with its emphasis on evaluating the utility of prototype artifacts, is well suited to
developing field-tested and theoretically grounded measurement models and instruments that are accessible
to practitioners. This focus on practical utility in turn provides researchers with results that can be more
readily corroborated, thus improving the quality and usefulness of the research findings.
Keywords: Strategic alignment, information systems capabilities, configurational theory, strategic archetypes,
design science, research methods
1
1Lars Mathiassen was the accepting senior editor for this paper. Shirley Gregor served as the associate editor.
The appendices for this paper are located in the “Online Supplements” section of the MIS Quarterly’s website (http://www.misq.org).
MIS Quarterly Vol. 35 No. 4 pp. 909-929/December 2011 909
McLaren et al./Measuring Fit Between Competitive Strategies and IS Capabilities
Intro ...
This document summarizes six articles on research methods in strategic management published in Organizational Research Methods. It discusses how each article advances understanding of specific methods or construct measurement. It urges readers to apply these methods more broadly to explore new areas of inquiry. It also identifies three directions for further advancing attention to measurement and methodology in strategy research: expanding application of methods to new contexts, exploring linkages between advances in measurement and theory, and considering how methods can reveal previously untested relationships.
Business Application of Operation ResearchAshim Roy
This document discusses a project on the business applications of operations research. It begins with an acknowledgment section thanking teachers and parents for their support. The main body provides an abstract, introduction and overview of operations research. It discusses the early history and development of OR, and provides examples of its applications in business such as optimizing supply chain management and power grid operations. The document outlines the various techniques, methods, and areas where OR is applied to improve decision making and efficiency.
A Topic Model of Analytics Job Adverts (Operational Research Society Annual C...Michael Mortenson
This presentation presents recent research into definitions of analytics through analysis of related job adverts. The results help us identify a new categorisation of analytics methodologies, and discusses the implications for the operational research community.
A Topic Model of Analytics Job Adverts (The Operational Research Society 55th...Michael Mortenson
This presentation presents recent research into definitions of analytics through analysis of related job adverts. The results help us identify a new categorisation of analytics methodologies, and discusses the implications for the operational research community.
The document summarizes research into HR analytics adoption models. It found that most organizations struggle to effectively adopt and use HR analytics. The available models are contradictory, static, and often contradict real-world case studies. It proposes a new agile HR analytics framework that focuses on incrementally solving business problems quickly using available data and talent. This approach helps ensure analytics are business-aligned and provide rapid ROI. The research included over 50 sources and 20 organizational interviews. It identified flaws in current models and the need for a dynamic approach to address skills gaps and emerging analytics-as-a-service options.
Looks at the application of a partial least squares SEM approach in International Business (IB). Benefits regarding theory building, recommended practices.
This is my class project using UCI Mashable dataset to determine what constitutes popular news. In this project, I used (1) multiple regression and model building and (2) PCA and factor analysis.
Data Analytics Tools: SAS and R
Business process redesignproject success the role ofsocTawnaDelatorrejs
Business process redesign
project success: the role of
socio-technical theory
Junlian Xiang
Ted Rogers School of Management, Ryerson University, Toronto, Canada, and
Norm Archer and Brian Detlor
DeGroote School of Business, McMaster University, Hamilton, Canada
Abstract
Purpose – The purpose of this paper is to seek to advance business process redesign (BPR) project
research through the generation and testing of a new research model that utilizes formative constructs
to model complex BPR project implementation issues. Instead of looking at management principles,
the paper examines the activities of improving business processes from the project perspective.
Design/methodology/approach – A survey of 145 managers and executives from medium and
large-sized USA and Canadian companies was used to validate the model.
Findings – The model, based on socio-technical theory, includes three implementation components
(change management, process redesign, and information and communication technology
infrastructure improvement), and links the effects of these components to BPR project outcomes.
The empirical findings indicated that all three implementation components had a significant impact
on BPR project success, with change management having the greatest effect. Interestingly, the results
also showed that productivity improvement was no longer the main focus of companies carrying out
BPR projects; instead, improvement in operational and organizational quality was more important.
Research limitations/implications – The main limitation of this study is its generalizability
with respect to company size and organizational culture. The sample in this study was drawn from
medium- and large-sized companies in Canada and the USA, but small-sized organizations were
excluded from this study due to their distinct features (e.g. superior flexibility or ability to reorient
themselves quickly). Also, this study controlled the variable of organizational culture by limiting
respondents to Canada and US companies. It would be very interesting to investigate BPR project
implementations in other countries where the organizational working culture may be different.
Practical implications – Based on the findings of this study, BPR practitioners can refer to the three
BPR project implementation components and then prioritize and sequence the tasks in a BPR project
to achieve their preset BPR goals.
Originality/value – This is the first study which utilizes formative constructs to validate the
important BPR project components.
Keywords Change management, Business process management, Business process redesign,
Information and communication technology infrastructure, Socio-technical theory
Paper type Research paper
1. Introduction
Business processes have drawn a great deal of attention from industrial practitioners
and academic researchers since the 1990s because of their great potential for improving the
efficiency and effectiveness of organizations. The roots of busines ...
Operational Research and Organizational SystemIJRES Journal
Organizational systems, as well as specific integration of social and technical systems are extremely important for the development of human society. The most part, the problems of managing these systems are reduced to operations research - a generic term for activities that define the processes involved in the functions of organizational systems, and hence the term operations research. Field of study operations research as a scientific discipline, the organizational processes and activities that are being carried out and an important determinant of the intention to find the best decisions in managing the operations undertaken to achieve the set goals of the system. The generality of operations research is reflected in the fact that apply to all types of organizational systems - commercial, industrial, agricultural, military, medical, educational, government, and the like. Users of operations research decision makers - managers, whose task is to efficiently and effectively manage organizational systems. In this paper we consider operational research and conceptual foundations that enable its effective use in solving the problem of organizational systems.
Supply chain managementtheory, practice and futurechall.docxpicklesvalery
This document summarizes the key elements of supply chain management theory discussed in academic literature. It identifies three main components of SCM theory: description of concepts and scope, prescriptions for practice, and identification of trends. However, it finds that SCM theory remains fragmented and idealistic. A central challenge is the gap between the integrated, collaborative vision of SCM in theory versus the reality of practice where barriers exist. The document calls for further work to develop SCM as a discipline through more rigorous testing of theories against practice.
Enhancement techniques for data warehouse staging areaIJDKP
This document discusses techniques for enhancing the performance of data warehouse staging areas. It proposes two algorithms: 1) A semantics-based extraction algorithm that reduces extraction time by pruning useless data using semantic information. 2) A semantics-based transformation algorithm that similarly aims to reduce transformation time. It also explores three scheduling techniques (FIFO, minimum cost, round robin) for loading data into the data warehouse and experimentally evaluates their performance. The goal is to enhance each stage of the ETL process to maximize overall performance.
This document discusses how to specify, estimate, and validate higher-order constructs in partial least squares structural equation modeling (PLS-SEM). It explains that higher-order constructs allow modeling a construct on an abstract higher dimension and its more concrete lower dimensions. The document outlines two prominent approaches for specifying higher-order constructs in PLS-SEM: 1) the repeated indicators approach and 2) the two-stage approach. It also notes that evaluating measurement quality of higher-order constructs can be challenging. The document then illustrates how to apply standard assessment criteria to validate reflective-reflective and reflective-formative higher-order constructs using the corporate reputation model example.
A Federated Search Approach to Facilitate Systematic Literature Review in Sof...ijseajournal
To impact industry, researchers developing technologies in academia need to provide tangible evidence of
the advantages of using them. Nowadays, Systematic Literature Review (SLR) has become a prominent
methodology in evidence-based researches. Although adopting SLR in software engineering does not go far
in practice, it has been resulted in valuable researches and is going to be more common. However, digital
libraries and scientific databases as the best research resources do not provide enough mechanism for
SLRs especially in software engineering. On the other hand, any loss of data may change the SLR results
and leads to research bias. Accordingly, the search process and evidence collection in SLR is a critical
point. This paper provides some tips to enhance the SLR process. The main contribution of this work is
presenting a federated search tool which provides an automatic integrated search mechanism in wellknown Software Engineering databases. Results of case study show that this approach not only reduces
required time to do SLR and facilitate its search process, but also improves its reliability and results in the
increasing trend to use SLRs.
This document analyzes gaps between business analytics skills needed in industry versus those taught in academic programs. Key findings include:
1) Academic programs emphasize statistical techniques but industries need skills like communication, experience, and skills relevant to specific fields like IT, insurance, and marketing.
2) Text mining of academic papers found a focus on techniques, applications, and theories of big data while industry articles focused more on organizational innovations and improvements from big data.
3) Analyses of literature on topics like operations, accounting, and marketing revealed both similarities and differences in focus between academic and industry discussions.
Exploratory Factor Analysis; Concepts and TheoryHamed Taherdoost
This document discusses exploratory factor analysis (EFA), including its concepts, theory, and process. EFA is commonly used to reduce a large number of variables into a smaller set of underlying factors and establish relationships between measured variables and latent constructs. The key steps of EFA include assessing suitability of the data, extracting factors, determining the number of factors to retain, rotating the factors for better interpretation, and labeling the factors. Sample size, factor extraction and rotation methods, and interpretation are also covered.
Enterprise and Data Mining Ontology Integration to Extract Actionable Knowled...hamidnazary2002
This document discusses integrating enterprise and data mining ontologies to extract actionable knowledge. It notes that existing data mining techniques provide large volumes of knowledge but much of it is not useful for making business decisions. The objectives are to 1) design an artifact to formally apply business understanding in data mining and 2) semi-automate the business understanding phase to help users. The expected outcomes are an enterprise ontology and relations between enterprise and data mining ontologies to bridge the gap between business needs and data mining results.
This document proposes an approach to developing data warehouse structures from business process models. It begins by discussing common challenges with data warehouse projects, such as failing to consider business goals and strategies. The document then reviews existing development approaches like user-oriented and operational-oriented methods. It introduces business process modeling using the Semantic Object Model technique and argues this provides a formal description of user information needs. The paper proposes deriving data warehouse structures from business process models to address limitations of other approaches. It provides an example using a student management system to illustrate connecting business processes to data warehouse schemas.
Applying systemic methodologies to bridge the gap between a process-oriented ...Panagiotis Papaioannou
This work is an application of the Soft Systems Methodology (SSM) to improve an information system to fully support the related process-based management system and help its internal improvement. Design and Control Systemic Methodology (DCSYM) is used as a modelling tool to facilitate conceptual models comparison within the SSM context.
Investigating the link between enterprise resource planning (erp) effectivene...Alexander Decker
This study investigates the relationship between enterprise resource planning (ERP) effectiveness and supply chain management. The study distributed questionnaires to 306 employees at electrical industrial companies in Jordan. 283 responses were received and analyzed statistically. The findings indicate a significant relationship between ERP effectiveness and supply chain management. Specifically, system quality, information quality, use of ERP systems, and organizational impact were found to have a significant relationship with supply chain management, while user satisfaction and individual impact did not. The study recommends further research in other countries and among managerial employees to validate these findings.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
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Partial Least Squares Structural Equation Modeling (PLS-SEM): An
Emerging Tool for Business Research
Article in European Business Review · February 2014
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2. Partial least squares structural
equation modeling (PLS-SEM)
An emerging tool in business research
Joe F. Hair Jr
Department of Marketing & Professional Sales, Kennesaw State University,
Kennesaw, Georgia, USA
Marko Sarstedt
Otto-von-Guericke-University Magdeburg, Magdeburg, Germany and
University of Newcastle, Newcastle, Australia
Lucas Hopkins
Middle Georgia State College, Macon, Georgia, USA, and
Volker G. Kuppelwieser
NEOMA Business School, Mont-Saint-Aignan, France
Abstract
Purpose – The authors aim to present partial least squares (PLS) as an evolving approach to
structural equation modeling (SEM), highlight its advantages and limitations and provide an overview
of recent research on the method across various fields.
Design/methodology/approach – In this review article, the authors merge literatures from the
marketing, management, and management information systems fields to present the state-of-the art of
PLS-SEM research. Furthermore, the authors meta-analyze recent review studies to shed light on
popular reasons for PLS-SEM usage.
Findings – PLS-SEM has experienced increasing dissemination ina variety of fields inrecentyears with
nonnormal data, small sample sizes and the use of formative indicators being the most prominent reasons
for its application. Recent methodological research has extended PLS-SEM’s methodological toolbox
to accommodate more complex model structures or handle data inadequacies such as heterogeneity.
Research limitations/implications – While research on the PLS-SEM method has gained
momentum during the last decade, there are ample research opportunities on subjects such as
mediation or multigroup analysis, which warrant further attention.
Originality/value – This article provides an introduction to PLS-SEM for researchers that have not
yet been exposed to the method. The article is the first to meta-analyze reasons for PLS-SEM usage
across the marketing, management, and management information systems fields. The
cross-disciplinary review of recent research on the PLS-SEM method also makes this article useful
for researchers interested in advanced concepts.
Keywords Structural equation modeling, Partial least squares, PLS-SEM
Paper type General review
Introduction
The popularity of structural equation modeling (SEM) has grown out of the need to test
complete theories and concepts (Rigdon, 1998). Much of SEM’s success can be attributed
to the method’s ability to evaluate the measurement of latent variables, while also
testing relationships between latent variables (Babin et al., 2008). Although the initial
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0955-534X.htm
European Business Review
Vol. 26 No. 2, 2014
pp. 106-121
q Emerald Group Publishing Limited
0955-534X
DOI 10.1108/EBR-10-2013-0128
EBR
26,2
106
3. application of this method embraced a covariance-based approach (CB-SEM), researchers
also have the option of choosing the variance-based partial least squares technique
(PLS-SEM).
Originally developed by Wold (1974, 1980, 1982), PLS is an SEM technique based on
an iterative approach that maximizes the explained variance of endogenous constructs
(Fornell and Bookstein, 1982). Unlike CB-SEM, which aims to confirm theories by
determining how well a model can estimate a covariance matrix for the sample data,
PLS-SEM operates much like a multiple regression analysis (Hair et al., 2011). This
characteristic makes PLS-SEM particularly valuable for exploratory research purposes:
PLS is primarily intended for research contexts that are simultaneously data-rich and
theory-skeletal. The model building is then an evolutionary process, a dialog between the
investigator and the computer. In the process, the model extracts fresh knowledge from the
data, thereby putting flesh on the theoretical bones. At each step PLS rests content with
consistency of the unknowns (Lohmöller and Wold, 1980, p. 1).
While CB-SEM is the more popular method, PLS-SEM has recently received considerable
attention in a variety of disciplines including marketing (Hair et al., 2012b), strategic
management (Hair et al., 2012a), management information systems (Ringle et al., 2012),
operations management (Peng and Lai, 2012), and accounting (Lee et al., 2011). Much of
the increased usage of PLS-SEM can be credited to the method’s ability to handle
problematic modeling issues that routinely occur in the social sciences such as unusual
data characteristics (e.g. nonnormal data) and highly complex models.
Given the popularity and expected continued growth of PLS-SEM, this paper aims
to discuss the current state of PLS-SEM by first providing an overview of past studies
that have summarized PLS-SEM usage. Next, we will explain the process and steps
used to test a model using PLS-SEM. Finally, our paper concludes by exploring many
of the advanced topics associated with the method.
Prior PLS-SEM review studies
The argument for PLS-SEM as a viable methodology is gaining acceptance throughout
many business disciplines. Several scholars have published studies summarizing
PLS-SEM usage within their respective fields. The studies summarize the application
of PLS-SEM, including the year of publication, range of years covered by the review,
number of articles analyzed, and the justifications given for using PLS-SEM. The
articles also reported the top three reasons given for applying PLS-SEM, which
included data distribution, sample size, and the use of formative indicators. Table I
summarizes the information reported in these articles.
Overall the findings indicate a substantial increase in the use of PLS-SEM in
recent years. Three of the studies explored the growth trend by conducting a
time-series analysis using the number of PLS-SEM studies. Hair et al. (2012b) and
Ringle et al. (2012) found that the use of PLS-SEM in the marketing and management
information systems fields has accelerated over time. In the strategic management
field, PLS-SEM usage has grown linearly as a function of time (Hair et al., 2012a).
When to use PLS-SEM
PLS-SEM provides numerous advantages to researchers working with structural
equation models. Given the popularity of CB-SEM, the use of PLS-SEM often requires
PLS-SEM: an
emerging tool
107
4. additional discussion to explain the rationale behind the decision (Chin, 2010). As our
meta-analysis of PLS-SEM review studies has shown, the most prominent
justifications for using PLS-SEM are attributed to:
.
nonnormal data;
.
small sample sizes; and
.
formatively measured constructs (Table I).
These concepts are discussed below.
(1) Nonnormal data
Data collected for social science research often fails to follow a multivariate normal
distribution. When attempting to evaluate a path model using CB-SEM, nonnormal
data can lead to underestimated standard errors and inflated goodness-of-fit measures
(Lei and Lomax, 2005). Fortunately, PLS-SEM is less stringent when working with
nonnormal data because the PLS algorithm transforms nonnormal data in accordance
with the central limit theorem (Beebe et al., 1998; Cassel et al., 1999). However, the caveat
to PLS-SEM providing the end-all solution to models using nonnormal data is twofold.
First, researchers should be aware that highly skewed data can reduce the statistical
power of the analysis. More precisely, the evaluation of the model parameters’
significances relies on standard errors from bootstrapping, which might be inflated when
data are highly skewed (Hair et al., 2014). Second, because CB-SEM has a variety of
alternative estimation procedures, it may be problematic to assume that PLS-SEM is
the automatic choice when considering data distribution (Hair et al., 2012b).
(2) Small sample size
Sample size can affect several aspects of SEM including parameter estimates, model fit,
and statistical power (Shah and Goldstein, 2006). However, different from CB-SEM,
PLS-SEM can be utilized with much smaller sample sizes, even when models are
Business discipline Authors Time period
Number of
studies
Top three reasons for PLS-SEM
usagea
Marketing Hair et al.
(2012b)
1981-2010 204 Nonnormal data: 50 percent
Small sample size: 46 percent
Formative indicators: 33 percent
Strategic
management
Hair et al.
(2012a)
1981-2010 37 Nonnormal data: 59 percent
Small sample size: 46 percent
Formative indicators: 27 percent
Management
information
systems
Ringle et al.
(2012)
1992-2011 65 Small sample size: 37 percent
Nonnormal data: 34 percent
Formative indicators: 31 percent
Productions and
operations
management
Peng and Lai
(2012)
2000-2011 42 Small sample size: 33 percent
Formative indicators: 19 percent
Nonnormal data: 14 percent
Accounting Lee et al. (2011) 2005-2011 20 Not analyzed
Notes: a
Percent of studies providing the corresponding reason; not all articles provided justifications
and some articles provided multiple reasons
Table I.
PLS-SEM review studies
from business disciplines
EBR
26,2
108
5. highly complex. In these situations, PLS-SEM generally achieves higher levels of
statistical power and demonstrates much better convergence behavior than CB-SEM
(Henseler, 2010; Reinartz et al., 2009). A popular heuristic states that the minimum
sample size for a PLS model should be equal to the larger of the following:
.
ten times the largest number of formative indicators used to measure one
construct; or
.
ten times the largest number of inner model paths directed at a particular
construct in the inner model (Barclay et al., 1995).
However, researchers should approach this guideline with caution, as
misunderstandings have caused skepticism about the general uses of PLS-SEM
(Hair et al., 2014). As with any other model-based data analysis technique, researchers
must consider sample size as it relates to the model complexity and data characteristics
(Hair et al., 2011). For example, while the rule of thumb put forth by Barclay et al. (1995)
provides a rough estimate of minimum sample size, it fails to take into account the
effect size, reliability, number of indicators, or other factors that are known to affect
power (Henseler et al., 2009).
(3) Formative indicators
The central difference between reflective and formative constructs is that formative
measures represent instances in which the indicators cause the construct (i.e. the arrows
point from the indicators to the construct), whereas reflective indicators are caused by
the construct (i.e. the arrows point from the construct to the indicators). While both,
PLS-SEM and CB-SEM can estimate models using formative indicators, PLS-SEM has
received considerable support as the recommended method (Hair et al., 2014). Because
analyzing formative indicators with CB-SEM often leads to identification problems
(Jarvis et al., 2003), it is not uncommon for researchers to believe that PLS-SEM is the
superior option. However, formative indicators should be approached with caution
when using PLS-SEM. Researchers should be aware that the evaluation of formatively
measured constructs relies on a totally different set of criteria compared to their
reflective counterparts. Prior PLS-SEM review studies (Hair et al., 2012a, b) have
criticized the careless handling of formative indicators and researchers should apply
the most recent set of evaluation criteria when examining the validity of formatively
measured constructs (Hair et al., 2014).
How to use PLS-SEM
When applying PLS-SEM, researchers need to follow a multi-stage process which
involves the specification of the inner and outer models, data collection and examination,
the actual model estimation, and the evaluation of results. In the following, this review
centers around the three most salient steps:
(1) model specification;
(2) outer model evaluation; and
(3) inner model evaluation.
Hair et al. (2014) provide an in-depth introduction into each of the stages of
PLS-SEM use.
PLS-SEM: an
emerging tool
109
6. (1) Model specification
The model specification stage deals with the set-up of the inner and outer models.
The inner model, or structural model, displays the relationships between the constructs
being evaluated. The outer models, also known as the measurement models, are used to
evaluate the relationships between the indicator variables and their corresponding
construct.
The first step in using PLS-SEM involves creating a path model that connects
variables and constructs based on theory and logic (Hair et al., 2014). In creating the path
model such as that shown in Figure 1, it is important to distinguish the location of the
constructs as well as the relationships between them. Constructs are considered either
exogenous or endogenous. Whereas exogenous constructs act as independent variables
and do not have an arrow pointing at them (Y1, Y2, and Y3 in Figure 1), endogenous
constructs are explained by other constructs (Y4 and Y5 in Figure 1). While often
considered as the dependent variable within the relationship, endogenous constructs can
also act as independent variables when they are placed between two constructs
(Y4 in Figure 1). When setting up the model, researchers need to be aware that in its basic
form, the PLS-SEM algorithm can only handle models that have no circular relationship
between the constructs. This requirement would be violated if we reversed the
relationship Y2 ! Y5 in Figure 1. In this situation, Y2 would predict Y4, Y4 would predict
Y5, and Y5 would predict Y2 again, yielding a circular loop (i.e. Y2 ! Y4 ! Y5 ! Y2).
After the inner model is designed, the researcher must specify the outer models.
This step requires the researcher to make several decisions such as whether to use a
multi-item or single-item scale (Diamantopoulos et al., 2012; Sarstedt and Wilczynski,
2009) or whether to specify the outer model in a reflective or formative manner
(Diamantopoulos and Winklhofer, 2001; Gudergan et al., 2008). The sound specification
Figure 1.
A simple path model
Y1
(exogenous)
Y2
(exogenous)
Y3
(exogenous)
Y4
(endogenous)
Y5
(endogenous)
Outer models
of the exogenous constructs
Inner
Model
Outer models
of the endogenous constructs
Item 1 (formative)
Item 2 (formative)
Item 3 (formative)
Item 1 (formative)
Item 2 (formative)
Item 3 (formative)
Item 1 (reflective)
Item 2 (reflective)
Item 3 (reflective)
Item 1 (reflective)
Item 2 (reflective)
Item 3 (reflective)
Item 1 (reflective)
Item 2 (reflective)
Item 3 (reflective)
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7. of the outer models is crucial because the relationships hypothesized in the inner model
are only as valid and reliable as the outer models. In Figure 1, Y1 and Y2 are measured
formatively, while all other constructs have a reflective measurement specification.
In this simple illustration, all constructs have an equal number of items. However, in
applied research, the number of items per construct can be much higher, especially
when formative measures are involved, as these – by definition – need to capture the
entire domain of the construct (Diamantopoulos and Winklhofer, 2001;
Diamantopoulos et al., 2008).
(2) Outer model evaluation
Once the inner and outer models have been specified, the next step is running the
PLS-SEM algorithm (for a description, see Henseler et al., 2012) and, based on the results,
evaluating the reliability and validity of the construct measures in the outer models.
By starting with the assessment of the outer models, the researcher can trust that the
constructs, which form the basis for the assessment of the inner model relationships, are
accurately measured and represented. When evaluating the outer models, the researcher
must distinguish between reflectively and formatively measured constructs (Ringle et al.,
2011; Sarstedt and Schloderer, 2010). The two approaches to measurement are based on
different concepts and therefore require consideration of different evaluative measures.
(3) Reflective indicators
Reflective indicators constitute a representative set of all possible items within the
conceptual domain of a construct (Diamantopoulos and Winklhofer, 2001). As a result,
reflective items are interchangeable, highly correlated and capable of being omitted
without changing the meaning of the construct. Reflective indicators are linked to a
construct through loadings, which are the bivariate correlations between the indicator
and the construct.
When assessing reflective outer models, researchers should verify both the
reliability and validity. The first step is using composite reliability to evaluate the
construct measures’ internal consistency reliability. While traditionally assessed using
Cronbach’s a (Cronbach and Meehl, 1955), composite reliability provides a more
appropriate measure of internal consistency reliability for at least two reasons. First,
unlike Cronbach’s a, composite reliability does not assume that all indicator loadings
are equal in the population, which is in line with the working principle of the PLS-SEM
algorithm that prioritizes the indicators based on their individual reliabilities during
model estimation. Second, Cronbach’s a is also sensitive to the number of items in the
scale and generally tends to underestimate internal consistency reliability. By using
composite reliability, PLS-SEM is able to accommodate different indicator reliabilities
(i.e. differences in the indicator loadings), while also avoiding the underestimation
associated with Cronbach’s a.
The second step in evaluating reflective indicators is the assessment of validity.
Validity is examined by noting a construct’s convergent validity and discriminant
validity. Support is provided for convergent validity when each item has outer
loadings above 0.70 and when each construct’s average variance extracted (AVE) is
0.50 or higher. The AVE is the grand mean value of the squared loadings of a set
of indicators (Hair et al., 2014) and is equivalent to the communality of a construct.
Put succinctly, an AVE of 0.50 shows that the construct explains more than half of the
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8. variance of its indicators. Discriminant validity represents the extent to which the
construct is empirically distinct from other constructs or, in other words, the construct
measures what it is intended to measure. One method for assessing the existence of
discriminant validity is the Fornell and Larcker (1981) criterion. This method states
that the construct shares more variance with its indicators than with any other
construct. To test this requirement, the AVE of each construct should be higher than
the highest squared correlation with any other construct. The second option for
verifying discriminant validity is examining the cross loadings of the indicators. This
method, often considered more liberal (Henseler et al., 2009), requires that the loadings
of each indicator on its construct are higher than the cross loadings on other constructs.
Formative indicators. As indicated earlier, the principles underlying formative
measurement are fundamentally different from the reflective type. Although PLS-SEM’s
ability to test models using formative indicators has attracted considerable attention
across disciplines, many researchers applying the method disregard the specific steps
that need to be followed when evaluating formative outer models (Hair et al., 2012a, b).
First and foremost, the researcher needs to assess the content validity of the construct
measures using expert assessment. Content validity evaluates the extent to which the
indicators capture the major facets of the construct. Simply put, if an important item
is omitted, the nature of the construct may be altered (Diamantopoulos et al., 2008).
The empirical evaluation of formative outer models requires assessing convergent
validity, or the extent to which a measure relates to other measures of the same
phenomenon (Hair et al., 2014). This assessment is done by means of a redundancy
analysis in which each formatively measured construct is correlated with an
alternative reflective or single-item measurement of the same construct. It is important
to note that the redundancy analysis requires gathering data on the alternative
measures at the same time as the original construct measures.
Next, the outer model indicators on each construct must be tested for collinearity.
As with multiple regression (Mooi and Sarstedt, 2011), high collinearity between two or
more formative indicators can seriously bias the results. More precisely, the weights
linking the formative indicators with the constructs (which represent each indicators’
contribution to the construct, controlling for the influence of all other indicators of the
same construct) could be reversed and their significance underestimated as a result of
increased standard errors.
Finally, researchers should evaluate the significance and relevance of each
formative indicator. Since PLS-SEM does not assume a normal distribution, the
researcher must apply the bootstrapping routine to determine the level of significance
of each indicator weight. Bootstrapping is a resampling technique that draws a large
number of subsamples from the original data (with replacement) and estimates models
for each subsample. This way, the researcher obtains a large number (typically 5,000 or
more) of model estimates, which can be used to compute a standard error of each model
parameter. Drawing on the standard error, the significance of each parameter can be
determined, using t-values. The assessment of the relevance of the indicators involves
comparing the weights of the indicators to determine their relative contribution to
forming the construct (Hair et al., 2014). In specific instances (i.e. when the indicator
weight is not significant), the researcher also needs to evaluate the bivariate
correlation (loading) between the (nonsignificant) indicator and the construct in order
to decide whether to exclude the indicator from the outer model (Hair et al., 2014).
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9. However, eliminating formative indicators from the model should generally be the
exception, as formative measurement theory requires that the measures fully capture
the entire domain of a construct. In short, omitting an indicator is equivalent to
omitting a part of the construct.
Inner model evaluation. Once the reliability and validity of the outer models is
established, several steps need to be taken to evaluate the hypothesized relationships
within the inner model. This aspect of PLS-SEM is different from CB-SEM in that the
model uses the sample data to obtain parameters that best predict the endogenous
constructs, as opposed to estimating parameters that minimize the difference between
the observed sample covariance matrix and the covariance matrix estimated by the
model. As a result, PLS-SEM does not have a standard goodness-of-fit statistic and
prior efforts to establishing a corresponding statistic have proven highly problematic
(Henseler and Sarstedt, 2013). Instead, the assessment of the model’s quality is based
on its ability to predict the endogenous constructs. The following criteria facilitate this
assessment: Coefficient of determination (R 2
), cross-validated redundancy (Q 2
), path
coefficients, and the effect size (f 2
). Prior to this assessment, the researcher needs to
test the inner model for potential collinearity issues. As the inner model estimates
result from sets of regression analyzes, their values and significances can be subject to
biases if constructs are highly correlated (for a discussion and demonstration, see
Hair et al., 2014). While the Fornell-Larcker criterion usually discloses collinearity
problems in the inner model earlier in the model evaluation process, this is not the case
when formatively measured constructs are involved. The reason is that the AVE –
which forms the basis for the Fornell-Larcker assessment – is not a meaningful
measure for formative indicators. Therefore, collinearity assessment in the inner model
is of pivotal importance when the model includes formatively measured constructs.
Coefficient of determination (R2
). The R 2
is a measure of the model’s predictive
accuracy. Another way to view R 2
is that it represents the exogenous variable’s combined
effect on the endogenous variable(s). This effect ranges from 0 to 1 with 1 representing
complete predictive accuracy. Because R 2
is embraced by a variety of disciplines,
scholars must rely on a “rough” rule of thumb regarding an acceptable R 2
, with 0.75, 0.50,
0.25, respectively, describing substantial, moderate, or weak levels of predictive accuracy
(Hair et al., 2011; Henseler et al., 2009). Though R 2
is a valuable tool in assessing the
quality of a PLS model, too much reliance on R 2
can prove problematic. Specifically,
if researchers attempt to compare models with different specifications of the same
endogenous constructs, reliance only on R 2
may result in the researcher selecting a less
efficient model. For example, the R 2
will increase even if a nonsignificant yet slightly
correlated construct is added to the model. As a result, if the researcher’s only goal is to
improve the R 2
, the researcher would benefit from adding additional exogenous
constructs even if the relationships are not meaningful. Rather, the decision for a model
should be based on the adjusted R 2
, which penalizes increasing model complexity by
reducing the (adjusted) R 2
when additional constructs are added to the model.
Cross-validated redundancy (Q2
). The Q 2
is a means for assessing the inner model’s
predictive relevance. The measure builds on a sample re-use technique, which omits a
part of the data matrix, estimates the model parameters and predicts the omitted part
using the estimates. The smaller the difference between predicted and original values
the greater the Q 2
and thus the model’s predictive accuracy. Specifically, a Q 2
value
larger than zero for a particular endogenous construct indicates the path model’s
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10. predictive relevance for this particular construct. It should, however, be noted that
while comparing the Q2
value to zero is indicative of whether an endogenous construct
can be predicted, it does not say anything about the quality of the prediction (Rigdon,
2014; Sarstedt et al., 2014).
Path coefficients. After running a PLS model, estimates are provided for the path
coefficients, which represent the hypothesized relationships linking the constructs.
Path coefficient values are standardized on a range from 21 to þ1, with coefficients
closer to þ1 representing strong positive relationships and coefficients closer to 21
indicating strong negative relationships. Although values close to þ1 or 21 are
almost always statistically significant, a standard error must be obtained using
bootstrapping to test for significance (Helm et al., 2009). After verifying whether the
relationships are significant, the researcher should consider the relevance of significant
relationships. In short, are the sizes of the structural coefficients meaningful? As stated
by Hair et al. (2014), many studies overlook this step and merely rely on the
significance of effects. If this important step is omitted, researchers may focus on a
relationship that, although significant, may be too small to merit managerial attention.
Effect size (f2
). The effect size for each path model can be determined by calculating
Cohen’s f 2
. The f 2
is computed by noting the change in R 2
when a specific construct is
eliminated from the model. To calculate the f 2
, the researcher must estimate two PLS
path models. The first path model should be the full model as specified by the
hypotheses, yielding the R 2
of the full model (i.e. R2
included). The second model should be
identical except that a selected exogenous construct is eliminated from the model,
yielding the R 2
of the reduced model (i.e. R2
excluded). Based on the f 2
value, the effect size
of the omitted construct for a particular endogenous construct can be determined such
that 0.02, 0.15, and 0.35 represent small, medium, and large effects, respectively,
(Cohen, 1988). That is, if an exogenous construct strongly contributes to explaining an
endogenous construct, the difference between R2
included and R2
excluded will be high, leading
to a high f 2
value. The effect size can be calculated using the formula below:
f 2
¼
R2
included 2 R2
excluded
1 2 R2
included
Advanced topics
The growing application of PLS-SEM is accompanied by broad range of methodological
research that extends the method’s toolbox. Several of these extensions deal with
approaches to allow researchers specifying more complex model set-ups. In its simplest
form, a PLS path model considers direct relationships between (sets of) constructs.
However, more complex model set-ups are easily conceivable such as the estimation of
moderating effects, mediating effects, or hierarchical component models.
Furthermore, methodological advances deal with the issue of heterogeneous data
structures, which threaten the validity of the results. One stream of research in this
field deals with multigroup analysis techniques to assess whether parameters (usually
path coefficients) differ significantly across two or more groups of data. A second
stream deals with the treatment of unobserved heterogeneity (i.e. heterogeneity that
cannot be attributed to a single observable variable such as demographic variables) by
means of latent class techniques. In the following, we give a brief description of
recently discussed topics.
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11. Moderation
Moderation occurs when the effect of an exogenous construct on an endogenous
construct depends on the values of another variable, which influences (i.e. moderates)
the relationship. For example, in their analysis of the relationship between dynamic
capabilities and organizational performance, Wilden et al. (2013) demonstrate that the
performance effect is contingent on the competitive intensity faced by firms as well as
the firm’s organizational structure. Research has brought forward several approaches
for estimating moderating effects in PLS-SEM, which Henseler and Fassott (2010) and
Rigdon et al. (2010) review. Henseler and Chin (2010) evaluate different approaches to
moderation in PLS-SEM in terms of their applicability to reflective and formative
measures, statistical power or predictive power.
Mediation
Mediation represents a situation in which a mediator variable to some extent absorbs the
effect of an exogenous on an endogenous construct in the PLS path model. For example,
in their study on the performance of consulting teams, Klarner et al. (2013) show that the
relationship between consulting teams’ competencies and their performance is
sequentially mediated by client communication and team adaptability. As such, their
analysis – opposed to a simple evaluation of direct effects – provides a more appropriate
picture of management consulting team performance. Several authors have criticized the
far-reaching neglect of explicitly examining mediating effects in PLS path models, which
can easily lead to erroneous conclusions when interpreting model estimates (Hair et al.,
2013, 2012a, b). A potential reason for this neglect might be that there is still some
ambiguity on how to evaluate mediating effects in PLS-SEM. Hair et al. (2014) provide an
initial illustration on how to analyze mediating effects but more research is needed to
provide guidance regarding the evaluation of more complex effects such as mediated
moderation or moderated mediation.
Hierarchical component models
In some instances, the constructs researchers wish to examine are quite complex and can
also be operationalized at higher levels of abstraction. For example, in their study on
management consulting team performance, Klarner et al. (2013) conceptualize task
competence as a two-dimensional construct with the dimensions relating to the team’s
generic and specific competencies. That is, instead of modeling these two competence types
on a single construct layer, the authors summarize them as two lower-order components
related to a single multidimensional higher-order construct. This modeling approach
leads to more theoretical parsimony, reduces model complexity and can avert confounding
effects in multidimensional model structures, such as multicollinearity (Kuppelwieser and
Sarstedt,2014;Ringleetal.,2012).Theoretically,thisprocesscanbeextendedtoanynumber
of multiple layers, but researchers usually restrict their modeling approach to two layers.
Wilson and Henseler (2007) as well as Becker et al. (2012) provide a review and evaluation
of different approaches to modeling higher-order constructs using PLS-SEM. Hair et al.
(2014) offer a tutorial on how to set-up and evaluate hierarchical component models.
Multigroup analysis
Multigroup analysis is a type of moderator analysis where the moderator variable
is categorical (usually with two categories) and is assumed to potentially affect all
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12. relationships in the inner model. For example, using multigroup analysis, Elbanna et al.
(2013) demonstrate that the role of intuition in strategic decision-making differs
significantly in situations of low vs high environmental hostility. Research has brought
forward several multigroup analysis approaches, which build on standard independent
samples t-test (Keil et al., 2000), permutation procedures (Chin, 2003; Chin and Dibbern,
2010), or bootstrap confidence intervals (Sarstedt et al., 2011a, b). Sarstedt et al. (2011a,
b) review the different approaches and propose an omnibus test of differences between
more than two groups of data, which translates the standard F-test for use with
PLS-SEM. As there are no concrete guidelines on when to use each approach, future
research should empirically compare them by means of a large-scale simulation study.
Latent class techniques
When estimating PLS path models, situations arise in which differences related to
unobserved heterogeneity prevent the model from being accurately estimated. Since
researchers never know if unobserved heterogeneity is causing estimation problems, they
need to apply complementary techniques for response-based segmentation (latent class
techniques) that allow for identifying and treating unobserved heterogeneity. Recent
research has brought forward a variety of latent class techniques which generalize, for
example, finite mixture (Hahn et al., 2002; Sarstedt et al., 2011a, b), genetic algorithm
(Ringleetal.,2013a,b),orhill-climbingapproaches(Beckeretal.,2013;Espositoetal.,2008)
to PLS-SEM. Sarstedt (2008) provides an early review of latent class techniques. In light of
the considerable biasesthat resultfromneglecting unobserved heterogeneity (Ringle etal.,
2010; Rigdon et al., 2011; Sarstedt and Ringle, 2010), recent research has called for the
routine application of latent class techniques for evaluating the PLS path models
(Becker et al., 2013; Hair et al., 2012b; Rigdon et al., 2010).
Discussion
SEM has become the dominant analytical tool for testing cause-effect-relationships
models with latent variables. When the goal of the analysis is to gain substantial
knowledge about the drivers of, for example, customer satisfaction, brand image or
corporate reputation, SEM is the technique of choice. For many researchers, SEM is
equivalent to carrying out CB-SEM. While researchers have a basic understanding of
CB-SEM, most of them have limited familiarity with the other useful approach –
PLS-SEM.
Does lack of familiarity with PLS-SEM imply the loss of opportunities? It certainly
does! Broadly speaking, the use of empirical methods in business applications has
two objectives: prediction and explanation (Sarstedt et al., 2014). Application of
CB-SEM typically overlooks a key objective of empirical studies, which is prediction.
The solution to this inherent weakness is the use of PLS-SEM, which has the
overriding objective of predicting the dependent latent variables.
Compared to CB-SEM, PLS-SEM offers other significant advantages. Many
empirical analysts pay lip service to distributional assumptions of the variables used in
the analysis. In fact, most empirical business and social sciences data is characterized
by nonnormal data. Consequently, CB-SEM applications that use the maximum
likelihood algorithm – which most do – overlook the inherent violations of this
technique’s requirements. Since PLS-SEM does not require these restrictive
distributional assumptions, it is often a more viable approach than CB-SEM.
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13. Another major advantage of PLS-SEM is that it permits the use of formative
measures, which differ considerably from the reflective measures. Formatively measured
constructs are particularly useful for studies that aim at explaining and predicting key
constructs such as the sources of competitive advantage or corporate success (Albers,
2010). While CB-SEM can principally handle formative measures, their inclusion requires
imposing considerable constraints on the model (Diamantopoulos and Riefler, 2011) or
using a MIMIC approach, which is often questioned by SEM scholars.
PLS-SEM is subject to some constraints, however, related to the assessment of
model fit (as commonly done in CB-SEM) and consistency of the parameter estimates.
Recent research advances the basic PLS-SEM algorithm to improve its statistical
properties, for example in terms of providing consistent parameter estimates. Dijkstra
and Hensler’s (2014) extension of PLS-SEM provides consistent parameter estimates
and introduces the option of testing the path model’s goodness-of-fit while maintaining
the strengths of the method. Dijkstra and Schermelleh-Engel (2014) extend this
approach to non-linear structural equation models. Further efforts at extending
non-linear structural equation models have been made by Bentler and Huang (2014).
To summarize, depending on the specific empirical context and objectives of the
study, PLS-SEM’s distinctive methodological features make it a particularly valuable
and potentially better-suited alternative to the more popular CB-SEM approaches in
practical applications. Generally, however, neither method is superior to the other
overall. Rather, the selection of the proper method depends on the objective of the study
(Rigdon, 2012; Sarstedt et al., 2014).
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Corresponding author
Marko Sarstedt can be contacted at: marko.sarstedt@ovgu.de
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