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The assumption that a set of observed variables is a function of an underlying common factor plus some error has dominated measurement in marketing and the social sciences in general for decades. This view of measurement comes with assumptions, which, however, are rarely discussed in research. In this article, we question the legitimacy of several of these assumptions, arguing that (1) the common factor model is rarely correct in the population, (2) the common factor does not correspond to the quantity the researcher intends to measure, and (3) the measurement error does not fully capture the uncertainty associated with measurement. Our discussions call for a fundamental rethinking of measurement in the social sciences. Adapting an uncertainty-centric approach to measurement, which has become the norm in in the physical sciences, offers a means to address the limitations of current measurement practice in marketing.

As technology in tourism and hospitality (TTH) develops technical artifacts according to visitors’ demands, it must deal with both behavioral and design constructs in the context of structural equation modeling (SEM). While behavioral constructs are typically modeled as common factors, the study at hand introduces the composite into TTH to model artifacts. To deal with both kinds of constructs, this paper aims to exploit partial least squares path modeling (PLS-PM) as a confirmatory approach to estimate models containing common factors and composites.

The study at hand presents PLS-PM in its current form, i.e. as a full-fledged approach for confirmatory purposes. By introducing the composite to model artifacts, TTH scholars can use PLS-PM to answer research questions of the type “Is artifact xyz useful?”, contributing to a further understanding of TTH. To demonstrate the composite model, an empirical example is used.

PLS-PM is a promising approach when the model contains both common factors and composites. By applying the test for overall model fit, empirical evidence can be obtained for latent variables and artifacts. In doing so, researchers can statistically test whether a developed artifact is useful.

To the best of the authors’ knowledge, this is the first study to discuss the practical application of composite and common factor models in TTH research. Besides introducing the composite to model artifacts, the study at hand also guides scholars in the assessment of PLS-PM results.

因为旅游酒店科技（TTH）根据游客需求而定制科技产品, TTH必须在结构方程模型（SEM）下结合游客行为和设计等变量。一般行为变量在模型中是常见因子, 本研究将这些变量编入TTH结构成为模块。本研究采用PLS-PM方法来预估含有隐性变量和模块的模型。.

本研究设计PLS-PM模式, 即确定性全变量方法。TTH学者们通过引进结构形成模型模块, 使用PLS-PM研究方法, 以回答研究问题“模块xyz有用吗？”, 因此对TTH进一步理解。为了展示复合模型, 本论文采用实际验证。.

PLS-PM在面对模块内存在常见因子和复合模块的结构时是有力方法。实际验证结果通过整体最佳模型参数, 得到隐性变量和模块。为此, 研究者们能够在统计方法上测量是否开发的模型模块是否有用。.

据作者所知, 本论文是首个研究在TTH领域上应用模块和常见因子模型。本研究引进显性变量在模型模块中, 以指导学者评估PLS-PM结果报告。.

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.

This paper aims to examine the Chinese investment anomaly and dissect it from a perspective of rational expectation framework.

Characteristic-based sorting and Fama–MacBeth two-stage cross-sectional regression are adopted to test the relationship between corporate investment and expected returns in both portfolio and individual stock levels. Under the framework of pricing kernels, an investment-based common risk factor is constructed to test the role of risk played in the negative investment-return relationship. Moreover, a Markov regime switching model is adopted to investigate the time-varying risk premium across market regimes.

Empirical results provide ample evidence showing that there is a negative relationship between investment and expected returns in the Chinese stock market. The new investment-based risk factor is found to capture the return differences across characteristic-based portfolios. In addition, risk premium of the new risk factor is not only statistically positive throughout the sample period, but also has an asymmetry that is higher during market downturn but lower under bull market.

This paper merely tests the hypotheses derived from rational school.

Investment strategies based on characteristic-sorted portfolios should be adjusted to different market regimes.

First, this paper provides comprehensive empirical results by adopting different methodologies for investigating the investment anomaly in China. Second, an investment-based factor is constructed specifically for the Chinese stock market for the first time. Finally, this is the first paper to investigate the asymmetric risk premium across the Chinese bear and bull regimes by using a multivariate Markov regime switching model.

We generalise the spectral EM algorithm for dynamic factor models in Fiorentini, Galesi, and Sentana (2014) to bifactor models with pervasive global factors complemented by regional ones. We exploit the sparsity of the loading matrices so that researchers can estimate those models by maximum likelihood with many series from multiple regions. We also derive convenient expressions for the spectral scores and information matrix, which allows us to switch to the scoring algorithm near the optimum. We explore the ability of a model with a global factor and three regional ones to capture inflation dynamics across 25 European countries over 1999–2014.

In this chapter, using a combination of long-run and sign restrictions to identify aggregate monetary and productivity factors, I find that the monetary factor is responsible for long swings in nominal variables but has little effect on fluctuations in output, real wage, or labor input growth. The productivity factor in addition to increasing output growth and real wage growth in the short and long run, also results in increases in labor input and decreases in prices, but the quantitative effect of the productivity factor on labor input is relatively small. These results are robust to the number of factors included in the model and to alternative priors about the short-run effects of the monetary factor, and to the inclusion of oil prices. Oil prices, in fact, appear to be largely driven by the other aggregate factors.

Many important constructs of business and social sciences are conceptualized as composites of common factors, i.e. as second-order constructs composed of reflectively measured first-order constructs. Current approaches to model this type of second-order construct provide inconsistent estimates and lack a model test that helps assess the existence and/or usefulness of a second-order construct. The purpose of this paper is to present a novel three-stage approach to model, estimate, and test second-order constructs composed of reflectively measured first-order constructs.

The authors compare the efficacy of the proposed three-stage approach with that of the dominant extant approaches, i.e. the repeated indicator approach, the two-stage approach, and the hybrid approach by means of simulated data whose underlying population model is known. Moreover, the authors apply the three-stage approach to a real research setting in business research.

The study based on simulated data illustrates that the three-stage approach is Fisher-consistent, whereas the dominant extant approaches are not. The study based on real data shows that the three-stage approach is meaningfully applicable in typical research settings of business research. Its results can differ substantially from those of the extant approaches.

Analysts aiming at modeling composites of common factors should apply the proposed procedure in order to test the existence and/or usefulness of a second-order construct and to obtain consistent estimates.

The three-stage approach is the only consistent approach for modeling, estimating, and testing composite second-order constructs made up of reflectively measured first-order constructs.

Safety climate and its impact on safety performance is well established; however, researchers in this field suggest that the absence of a common assessment framework is a reflection of the state of development of this concept. The purpose of this paper is to propose a five-factor model that can be used to diagnose and measure safety climate in construction safety research and practice.

A systematic review was adopted, and following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, 574 articles were selected at the start of the study based on a developed review protocol for investigating safety climate factors. While examining the factor analysis of different studies, data reliability and data validity of the individual research findings were considered and frequency of factors uploaded was used to determine the significance as a quantitative measure to develop the ranking of safety climate factors.

The review identified that, from the established measures of safety climate in construction, there is little uniformity on factor importance. However, management commitment safety system role of the supervisor; workers’ involvement and group safety climate were found to be the most common across the studies reviewed. It is proposed these factors are used to inform a five-factor model for investigating safety climate in the construction industry.

The findings of this study will motivate researchers and practitioners in safety to use the five-factor safety climate model presented in this paper and test it to develop a common factor structure for the construction industry. The fact that the model is comprised of five factors makes it easier to be used and implemented by small-to medium-sized construction companies, therefore enhancing its potential use.

In the previous chapters, we have explored some of the key debates around empirically supported treatments, and I have introduced the reader to the concept of evidence based practice as a tripartite model that speaks to a much wider understanding of therapy research and practice. In this chapter, I introduce the concept of the common factors and some of the research that supports the idea that in general all therapy approaches tend to be as effective as each other, indeed, a summary of this research going back as far as 1936 is highlighted. The common factor proposition rests on the premise that there are far more commonalities across diverse therapy methods, than differences, and that it is these trans-theoretical constructs that are responsible for the lion’s share of outcomes. After briefly reviewing some of the literature, several common factor models are presented for the reader to consider.

The emergence of collaborative delivery models and working practices in construction industry has created a potential area for project success research. Previous studies have addressed success factors of various collaborative delivery models (e.g. alliance and partnering). However, there is currently very limited research-based knowledge concerning core success factors for different collaborative delivery models, exploring the commonalities. Thus, this study aims to conceptualize a success model for collaborative construction projects by identifying and structuring their core success factors through the lens of project delivery elements.

A systematic literature review was conducted, and thematic as well as content analysis of the relevant studies led to the identification of mentioned success factors in the literature for different collaborative delivery models. Then, those common success factors were structured in a model based on factors' relation to project delivery elements.

The obtained results present eight core success factors (e.g. equality, mutual trust and commitment to win–win philosophy) for collaborative construction projects, structured in a model based on their contribution toward project organization, contractual relationships, and operational system in construction project delivery. Moreover, the differences between success factors for traditional and collaborative construction projects are discussed.

This study's findings provide insightful theoretical contributions on collaborative construction project success and providing a departure point for future studies based on the discussed differences between success factors of collaborative and traditional construction projects. The findings can be also practically insightful for the project professionals in collaborative construction projects to succeed in managing project organization, contractual relationships, and operational system.