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Generalized structured component analysis (GSCA) and partial least squares path modeling (PLSPM) are two key component-based approaches to structural equation modeling that facilitate the analysis of theoretically established models in terms of both explanation and prediction. This study aims to offer a comparative evaluation of GSCA and PLSPM in a predictive modeling framework.

A simulation study compares the predictive performance of GSCA and PLSPM under various simulation conditions and different prediction types of correctly specified and misspecified models.

The results suggest that GSCA with reflective composite indicators (GSCA_R) is the most versatile approach. For observed prediction, which uses the component scores to generate prediction for the indicators, GSCA_R performs slightly better than PLSPM with mode A. For operative prediction, which considers all parameter estimates to generate predictions, both methods perform equally well. GSCA with formative composite indicators and PLSPM with mode B generally lag behind the other methods.

Future research may further assess the methods’ prediction precision, considering more experimental factors with a wider range of levels, including more extreme ones.

When prediction is the primary study aim, researchers should generally revert to GSCA_R, considering its performance for observed and operative prediction together.

This research is the first to compare the relative efficacy of GSCA and PLSPM in terms of predictive power.

Electronic commerce (EC) strategy – performance logic has gained significant popularity in the literature, particularly from the resource-based view (RBV) of theoretical underpinning. However, such an obsession of focusing on organizations' complementary resources has been increasingly challenged, which has pressed the RBV to examine the possibility of external factors that can also impact firm performance. In this study, the authors shed light on the firm's external readiness—defined as the extent to which a firm's customers and suppliers perceive EC as important—in the relationship between SME's complementary resources and firm performance.

The authors employed a refined data set based on the British EC Award database, in which the authors sampled 430 British SMEs' senior managements and examined how EC investments made by the SMEs influenced firm performance, and how their external readiness moderated this main relationship.

The results showed that, in line with the RBV perspective, SMEs' complementary business resources and human resources both had strong and direct impacts on the firm performance. They were also strongly mediated by EC functionality. In addition, SMEs' external readiness moderated the relationship between human resources and firm performance and that of EC functionality on firm performance.

The findings contribute to RBV theory building by extending earlier research on the role of technology as performance enablers for SMEs and shed light on the often-overlooked role of SMEs' external readiness.

This paper aims to clarify some of the representations regarding philosophy of science and statistical methods, which are contained in Cadogan and Lee (this issue).

This paper uses logical argument and a review of literature.

Rigdon’s (2012) approach to construct validation is entirely consistent with scientific realism, while the “realist variable framework” revives the empiricist reification of common factors found in Bagozzi’s (1984) Holistic Construal and throughout the early literature of structural equation modeling. Factor indeterminacy is a phenomenon that makes it impossible to equate common factors with conceptual variables. The future of marketing measurement is not in the historical error-centric framework but in a measurement framework centered around uncertainty.

Researchers should avoid reification of common factors and recognize the validity gap between conceptual variables and empirical proxies, consistent with Rigdon (2012) and should move toward an uncertainty-centric approach to measurement.

Decision-makers need to acknowledge the difference between data and the underlying reality. Success or failure will be shaped by the reality, not by the data.

To the best of the author’s knowledge, this is the first paper seeking to clarify representations in Cadogan and Lee (this issue). This paper aims to save journal readers from being misled.

The purpose of this paper is to use a combination of resource-based theory and dynamic capabilities theory to explore the phenomenon of startup survival in an emerging entrepreneurial ecosystem.

The study has a phenomenological research design, with an exploratory scope and qualitative approach. It uses in-depth interviews to identify the perceptions of ecosystem agents about the phenomenon of survival.

This paper argues that startup survival should be studied as a construct that is reflected by four conditions: break-even point, accelerated growth, cash stock and continuous operation. Furthermore, it is formed by the interaction of five mainly interacting resources: human capital, social capital, entrepreneurial capital, organizational capital and the incubation process.

The study offers a holistic model of survival that could be applicable to incipient entrepreneurial ecosystems such as the Peruvian one. This model presents survival as a reflexive-formative construct and not as a dichotomic variable (enterprise operating/enterprise closed) as has been commonly considered in the literature.

En este documento se utiliza una combinación de la teoría basada en los recursos y la teoría de las capacidades dinámicas para explorar el fenómeno de la sobrevivencia de startups en un ecosistema emprendedor incipiente.

El estudio tiene un diseño de investigación fenomenológica, con un alcance exploratorio y un enfoque cualitativo. Utiliza entrevistas en profundidad para identificar las percepciones de los agentes del ecosistema sobre el fenómeno de la sobrevivencia.

En este documento se argumenta que la sobrevivencia de los startups debe estudiarse como un constructo que se refleja en cuatro condiciones: el punto de equilibrio, el crecimiento acelerado, el stock de efectivo y la operación continua. Además, se forma por la interacción de cinco categorías de recursos organizacionales: el capital humano, el capital social, el capital emprendedor, el capital organizacional y el proceso de incubación.

El documento ofrece un modelo holístico de sobrevivencia que podría ser aplicable en ecosistemas emprendedores incipientes como el peruano. Este modelo presenta a la sobrevivencia como un constructo reflexivo-formativo y no como una variable dicotómica (empresa en actividad / empresa cerrada) como se ha considerado comúnmente en la literatura.

The purpose of this paper is to present integrated generalized structured component analysis (IGSCA) as a versatile approach for estimating models that contain both components and factors as statistical proxies for the constructs. The paper sets out to discuss the how-tos of using IGSCA by explaining how to specify, estimate, and evaluate different types of models. The paper’s overarching aim is to make business researchers aware of this promising structural equation modeling (SEM) method.

By merging works of literature from various fields of science, the paper provides an overview of the steps that are required to run IGSCA. Findings from conceptual, analytical and empirical articles are combined to derive concrete guidelines for IGSCA use. Finally, an empirical case study is used to illustrate the analysis steps with the GSCA Pro software.

Many of the principles and metrics known from partial least squares path modeling – the most prominent component-based SEM method – are also relevant in the context of IGSCA. However, there are differences in model specification, estimation and evaluation (e.g. assessment of overall model fit).

Methodological developments associated with IGSCA are rapidly emerging. The metrics reported in this paper are useful for current applications, but researchers should follow the latest developments in the field.

To the best of the authors’ knowledge, this is the first paper to offer guidelines for IGSCA use and to illustrate the method's application by means of the GSCA Pro software. The recommendations and illustrations guide researchers who are seeking to conduct IGSCA studies in business research and practice.

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

Gives introductory remarks about chapter 1 of this group of 31 papers, from ISEF 1999 Proceedings, in the methodologies for field analysis, in the electromagnetic community. Observes that computer package implementation theory contributes to clarification. Discusses the areas covered by some of the papers ‐ such as artificial intelligence using fuzzy logic. Includes applications such as permanent magnets and looks at eddy current problems. States the finite element method is currently the most popular method used for field computation. Closes by pointing out the amalgam of topics.

The purpose of this paper is to propose a graph-based representation approach for evolutionary knowledge under the big data circumstance, aiming to gradually build conceptual models from data.

A semantic data model named meaning graph (MGraph) is introduced to represent knowledge concepts to organize the knowledge instances in a graph-based knowledge base. MGraph uses directed acyclic graph–like types as concept schemas to specify the structural features of knowledge with intention variety. It also proposes several specialization mechanisms to enable knowledge evolution. Based on MGraph, a paradigm is introduced to model the evolutionary concept schemas, and a scenario on video semantics modeling is introduced in detail.

MGraph is fit for the evolution features of representing knowledge from big data and lays the foundation for building a knowledge base under the big data circumstance.

The representation approach based on MGraph can effectively and coherently address the major issues of evolutionary knowledge from big data. The new approach is promising in building a big knowledge base.

Two important methodologies having some common grounds, but based on differing contexts and paradigms are Physical System Theory (PST) and System Dynamics (SD). The developments in both the fields have taken place almost independently, and attempts have been made to integrate the two to complement their strengths and limitations. This paper provides an overview of PST in terms of its foundations, philosophy, fundamental postulates, recent developments on its simplification and enlargement, and applications to socio‐economic and managerial systems. A comparison of PST is made with SD on different fronts so as to understand their similarities and differences for carving out their place in modelling of managerial and socio‐economic systems and integrating the two more meaningfully and flexibly. The paper is concluded emphasizing the need for a ‘Flexible System Theory’ which can relate many such systems based approaches and techniques on the whole continuum from hard to soft systems thinking to cater the whole spectrum of problem situations effectively.

I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of Markov Switching models to fit the data, filter unknown regimes and states on the basis of the data, to allow a powerful tool to test hypotheses formulated in light of financial theories, and to their forecasting performance with reference to both point and density predictions. The review covers papers concerning a multiplicity of sub-fields in financial economics, ranging from empirical analyses of stock returns, the term structure of default-free interest rates, the dynamics of exchange rates, as well as the joint process of stock and bond returns.