Robustness of statistical inferences using linear models with meta-analytic correlation matrices

To examine complex relationships among variables, researchers in human resource management, industrial-organizational psychology, organizational behavior, and related fields have increasingly used meta-analytic procedures to aggregate effect sizes across primary studies to form meta-analytic correlation matrices, which are then subjected to further analyses using linear models (e.g., multiple linear regression). Because missing effect sizes (i.e., correlation coefficients) and different sample sizes across primary studies can occur when constructing meta-analytic correlation matrices, the present study examined the effects of missingness under realistic conditions and various methods for estimating sample size (e.g., minimum sample size, arithmetic mean, harmonic mean, and geometric mean) on the estimated squared multiple correlation coefficient (R²) and the power of the significance test on the overall R² in linear regression. Simulation results suggest that missing data had a more detrimental effect as the number of primary studies decreased and the number of predictor variables increased. It appears that using second-order sample sizes of at least 10 (i.e., independent effect sizes) can improve both statistical power and estimation of the overall R² considerably. Results also suggest that although the minimum sample size should not be used to estimate sample size, the other sample size estimates appear to perform similarly.     

Improving the validity of theory testing in logistics research using correlated components regression

The purpose of this logistics research methods article is to empirically test and introduce correlated components regression (CCR) as a new statistical technique that will improve the accuracy and validity in testing logistics theoretical models and hypothesised relationships. The purpose of the current study is to use CCR analysis as technique to address multicollinearity. Customer satisfaction data with parcel carriers is analysed with using CCR and multiple regression. To determine the best regression model of these two approaches, cross-validation R² values are used. In addition, comparisons are made to examine the standardised beta coefficients from both methods and to assess the possible impact from high levels of multicollinearity. Findings of the analysis suggest that CCR has a significantly higher cross-validation R² value and thus is determined the best model of these two approaches.     

Incomplete information and the time series behaviour of consumption

Pischke ( 1995 ) uses both microeconomic and macroeconomic US data to test the idea that, within an otherwise standard PIH framework, ignorance by agents of aggregate labour income can account for the observed degree of excess smoothness and sensitivity in consumption. His tests involve only the second moments of aggregate consumption and labour income. In this paper our main aim is to identify and test the restrictions his model implies for aggregate consumption dynamics, using US quarterly data over the period 1959–1996, but our framework allows us also to test an earlier, related model of Goodfriend ( 1992 ). We find that both models can be formally rejected: ignorance of aggregate labour income cannot by itself account for aggregate consumption dynamics; some other relaxation of the assumptions of the standard PIH is required. We give an example of one possible such relaxation and present evidence indicating that Pischke's version of imperfect information may, within that framework, have a significant role to play. Copyright © 2000 John Wiley & Sons, Ltd.     

How Important are Earnings Announcements as an Information Source?

In a competitive information market, a single information source can only dominate other sources individually, not collectively. We explore whether earnings announcements constitute such a dominant source using Ball and Shivakumar's (2008) [How much new information is there in earnings?, Journal of Accounting Research, 2008, 46(5), pp. 975–1016] R ² metric: the proportion of the variation in annual returns explained by the four quarterly earnings announcement returns. We find that the earnings announcement days' R ² is 11% – higher than the corresponding R ² of days with dividend announcements, management forecasts, preannouncements, and 10-K and 10-Q filings and their amendments, and comparable to that of the four days with the largest realised absolute returns in a year. Additional analysis reveals that earnings announcements convey extreme bad news as often as management forecasts and preannouncements; for any other type of news, earnings announcements are much more frequent. We conclude that earnings announcements are an important source of new information in the equity market.     

Reward Contingency, Unemployment, and Functional Turnover

Based on the valence model of expectancy theory and the Cornell model of job satisfaction, this field study investigated the relationship between reward contingency, unemployment, pay satisfaction, job satisfaction, and functional turnover. The latter of which separates turnover into four categories: poor performing leavers, good performing leavers, poor performing stayers, and good performing stayers. It was conducted with a geographically dispersed sample of sales representatives (i.e., from 25 states and 66 cities), resulting in unemployment rates that ranged from 2 percent to 12 percent. The sales representatives were employed by four companies that paid different combinations of salary and commissions, ranging from mostly salary and little commission to 100 percent commission. A discriminant analysis accounted for 62 percent of the variance in functional turnover and achieved an overall classification hit rate of 67 percent across the four functional turnover groups. Follow-up univariate analyses indicated that objective reward contingency (R²=.34), state unemployment (R²=.11), state sales unemployment (R²=.08), education (R²=.09), and tenure (R²=.08) accounted for most of the variance in functional turnover. Perceived reward contingency, pay satisfaction, job satisfaction, age, and gender were not related to functional turnover.     

Blocked-Error-R 2: A Conceptually Improved Definition of the Proportion of Explained Variance in Models Containing Loops or Correlated Residuals

Bentler and Raykov (2000, Journal of Applied Psychology 85: 125–131), and Jöreskog (1999a, http://www.ssicentral.com/lisrel/column3.htm, 1999b http://www.ssicentral. com/lisrel/column5.htm) proposed procedures for calculating R ² for dependent variables involved in loops or possessing correlated errors. This article demonstrates that Bentler and Raykov’s procedure can not be routinely interpreted as a “proportion” of explained variance, while Jöreskog’s reduced-form calculation is unnecessarily restrictive. The new blocked-error-R ² (beR ²) uses a minimal hypothetical causal intervention to resolve the variance-partitioning ambiguities created by loops and correlated errors. Hayduk (1996) discussed how stabilising feedback models – models capable of counteracting external perturbations – can result in an acceptable error variance which exceeds the variance of the dependent variable to which that error is attached. For variables included within loops, whether stabilising or not, beR ² provides the same value as Hayduk’s (1996) loop-adjusted-R ². For variables not involved in loops and not displaying correlated residuals, beR ² reports the same value as the traditional regression R ². Thus, beR ² provides a conceptualisation of the proportion of explained variance that spans both recursive and nonrecursive structural equation models. A procedure for calculating beR ² in any SEM program is provided.     

A note on the space of preference relations

The space of irreflexive, transitive, and continuous binary relations of R^l endowed with the topology of closed convergence, and subsets of defined by various other properties of the relations are investigated. It is shown that the subsets defined by properties, which one often assumes in general equilibrium theory, have nice topological properties such as compactness or G₈-ness.     

Evaluating Pseudo-R2's for binary probit models

Many applied researchers of limited dependent variable models found it disadvantageous that a widely accepted Pseudo-R² does not exist for this type of estimation. The paper provides guidance for researchers in choosing a Pseudo-R² in the binary probit case. The starting point is that R² is best understood in the ordinary least squares (OLS) case with continuous data, which is chosen as the reference situation. It is considered which Pseudo-R² is best able to mimic the OLS-R². The results are surprisingly clear: a measure suggested by McKelvey-Zavoina performs the best under our criterion. However, in the more likely case of low Pseudo-R²'s, a normalization of a measure proposed by Aldrich-Nelson which we suggest is almost as good as the McKelvey-Zavoina, and is in general easier to calculate. We also show that if the underlying R² is predicted using cubic regressions given the Pseudo-R², all measures perform much better.     

Value Relevance of Accounting Information for Intangible-Intensive Industries and the Impact of Scale: The US Evidence

Abstract

The structural shift in the USA from a tangible- to an intangible-intensive economy raises a concern that reporting based on generally accepted accounting principles (GAAP) might have lost its usefulness to investors. Amir and Lev [(1996) Value relevance of nonfinancial information: the wireless communications industry, Journal of Accounting and Economics, 22(1–3), pp. 3–30] argue that accounting information is not useful for intangible-intensive firms. In contrast, Collins et al. [(1997) Changes in the value relevance of earnings and book values over the past forty years, Journal of Accounting and Economics, 24(1), pp. 39–67] find that the value relevance (measured by R-squared) of accounting information has increased over time and that value relevance for intangible-intensive industries is as high as that for tangible-intensive industries. In this article, we attempt to resolve the above discrepancy by examining the impact of scale on R-squared (Brown, S., Lo, K. and Lys, T. (1999) Use of R² in accounting research: measuring changes in value relevance over the last four decades, Journal of Accounting and Economics, 28(2), pp. 83–115). We find that, after controlling for scale, R-squared is lower for intangible-intensive industries than for non-intangible-intensive industries and has declined over time for intangible-intensive industries but remained stable for non-intangible-intensive industries. Interestingly, the declining trend ended with the demise of the ‘New Economy’ period (NEP) (Core, J. E., Guay, W. R. and Van Buskirk, A. (2003) Market valuations in the New Economy: an investigation of what has changed, Journal of Accounting and Economics, 34(2–3), pp. 43–67), and value relevance for both industry groups appears to be restored in the post-NEP to the pre-NEP level. We also find that R&D capitalisation increases value relevance for intangible-intensive industries, but does not completely eliminate the gap between the two groups.     

THE ROLE OF LAW IN ECONOMIC GROWTH: A LITERATURE REVIEW

Abstract Compared with other explanatory variables, such as capital accumulation, technological innovations, geographical endowments, economic openness, and cultural factors, institutions, especially legal institutions have been regarded as a crucial condition for economic growth in recent years. The importance of legal rules is systematically revealed by a series of cross‐country econometric studies conducted by La Porta et al., who claim that legal origins are central to understanding the divergence in living standards across the regions and countries of the world and, compared with civil law countries, especially those countries with French civil law tradition, common law countries have enjoyed superior economic outcomes. The controversies set off by La Porta et al.'s proposition indicate that there are a number of questions that are difficult to explain by La Porta et al.'s theory, and hence call for more work on comparative analysis of different legal families before a consensus can be reached.     

Innovativeness and involvement as determinants of website loyalty: II. Determinants of consumer loyalty in B2C e-commerce

This paper is the second of the series of studies entitled “Innovativeness and Involvement as Determinants of Website Loyalty”, which was designed to test Foxall's [(1995). Cognitive styles of consumer initiators. Technovation 15(5), 269–288] style/involvement model in the context of Internet buyer behaviours. In this paper, a consumer Website loyalty model was proposed to describe how consumer transfer their existing brand loyalty in the traditional retail market to the same brand's Website in the B2C e-commerce market and how their perceived risk at the brand's Website mediates this loyalty transformation. Data were collected via an email invitation and Web-based questionnaire. One thousand and fourty four Taiwan Internet buyers randomly selected from the database of a well-known brand's Website have completed the survey. The multiple regression technique indicated the robustness of this loyalty transformation model (adjusted R²=0.50). Findings further indicated the impact that consumers’ cognitive style/involvement have on their loyalty transformation model. After segmenting consumers via their DSI and PDI scores, the distinct loyalty transformation models are revealed: the adjusted R² of more-involved innovators’ loyalty transformation model was the highest (0.60), followed by more involved adaptors’ model (0.45), less involved innovators’ model (0.45), and finally, less involved adaptors’ model (0.42). Discussions of how consumers’ cognitive style and involvement level interact with each other and impact on the predictors of the Website loyalty are discussed. Proposals are made of how Website managers can use this knowledge to build marketing strategies.     

An Average-Based Accounting Approach to Capital Asset Investments: The Case of Project Finance

Literature and textbooks on capital budgeting endorse net present value (NPV) and generally treat accounting rates of return as not being reliable tools. This paper shows that accounting numbers can be reconciled with NPV and fruitfully employed in real-life applications. Focusing on project finance transactions, an average return on investment (AROI) is drawn from the pro forma financial statements, obtained as the ratio of aggregate income to aggregate book value. We show that such a metric correctly captures a project's economic profitability, as long as it is compared with a comprehensive weighted average cost of capital (WACC) that includes a correction factor that takes account of the capital foregone by the investors. In contrast to the internal rate of return, AROI is unique, and we provide an explicit functional relation that links it to the NPV. The approach holds for levered and unlevered projects, constant and non-constant leverage ratios, and constant and non-constant WACCs.     

A parametric test of the negativity of the substitution matrix

The negativity of the substitution matrix implies that its latent roots are non-positive. When inequality restrictions are tested, standard test statistics such as a likelihood ratio or a Wald test are not X²-distributed in large samples. We propose a Wald test for testing the negativity of the substitution matrix. The asymptotic distribution of the statistic is a mixture of X²-distributions. The Wald test provides an exact critical value for a given significance level. The problems involved in computing the exact critical value can be avoided by using the upper and lower bound critical values derived by Kodde and Palm (1986). Finally the methods are applied to the empirical results obtained by Barten and Geyskens (1975).     

The coefficient of determination and simultaneous equation systems

In this paper we show that the Carter-Nagar (1977) R²'s for single structural equations and systems are in fact R² for the reduced form where the partially restricted reduced form estimation method is employed. We also show that the results of McElroy (1977) may be used to derive the Carter-Nagar system measure. If the reduced form equations are estimated by Kakwani's (1975) k-class reduced form estimator a new R² may be defined which is shown to be asymptotically equivalent to the Carter-Nagar measure.     

Determinants of the Size of the Small Business Sector: They Are Labor Productivity, Wage Rates and Capital Intensity

Abstract . Statistical analysis of a hitherto unpublished tabulation of the 1976 Annual Survey of Manufactures provides new evidence of the small business/ large business relationship in the U.S. economy. Small business (measured by employees) seems to have an advantage over large in that it has relatively lower wage costs. But the data prove it has a higher ratio of labor inputto unit output. Higher productivity in large firms make its wage/output relationship an advantage over small firms. Large firms pay appreciably higher wages than small where most production is by large firms. Highly skilled and hence higher paid workers may be a correlate of higher mechanization. Similarly, the role of capital intensity as an indicator of technology differences explains why capital productivity is no adequate explicator of firm industry share. These variables are closely related to profit margin. Relatively high book values in large firms are associated with low small business industry shares. Nor do large businesses have an advantage in lower prices for raw materials and parts. In the U.S. large business is centered in the capital intensive industries.     

Coherent forecasting for count time series using Box–Jenkins's AR(p) model

During the last three decades, integer‐valued autoregressive process of order p [or INAR(p)] based on different operators have been proposed as a natural, intuitive and maybe efficient model for integer‐valued time‐series data. However, this literature is surprisingly mute on the usefulness of the standard AR(p) process, which is otherwise meant for continuous‐valued time‐series data. In this paper, we attempt to explore the usefulness of the standard AR(p) model for obtaining coherent forecasting from integer‐valued time series. First, some advantages of this standard Box–Jenkins's type AR(p) process are discussed. We then carry out our some simulation experiments, which show the adequacy of the proposed method over the available alternatives. Our simulation results indicate that even when samples are generated from INAR(p) process, Box–Jenkins's model performs as good as the INAR(p) processes especially with respect to mean forecast. Two real data sets have been employed to study the expediency of the standard AR(p) model for integer‐valued time‐series data.     

Introducing the supply side into the LBS Model

This Briefing Paper describes a new version of the London Business School model, which incorporates our most recent research on the supply side. The changes reflect the desire to improve the specification of the supply-side of the model, and to capture the effects of taxes on the incentives to save, invest and to work, while still retaining the basic features of the income-expenditure framework. The main features of the new model are: - Gross domestic product is determined as the sum of the outputs of five sectors. Previously GDP was determined by the demand side as the sum of the expenditure components. - Domestic demand for the output of the private sector depends on domestic absorption and on the price of this output relative to import prices. Overseas demand for exports depends on world economic activity and on the price of exports relative to the world price of exports. - Supply depends on the capital stock, real unit labour costs and real raw material prices. In the short run, input prices are allowed to affect the mix between goods which are exported and those which are supplied to the domestic market. In the long run, however, the mix depends only on the price of exports relative to the price of domestically supplied goods (i.e., relative profitability). - In the short run, disequilibrium in the goods market is reflected in adjustments to prices, inventories and the external balance. - Since gross domestic product is determined by summing the output of each sector, output decisions are reconciled with expenditure decisions by making imports the difference between final expenditure and aggregate supply. - In the long run, increases in government expenditure crowd out private expenditure, but the effect takes several years to come through. - A cut in corporation taxes which is not financed by higher taxes elsewhere boosts the supply side by raising investment and the capital stock, but not by enough to raise revenues sufficiently to pay for the tax cut. Private sector saving increases but not by enough to fund higher public sector borrowing, so the current account goes into deficit. - In the short run, both supply and demand factors influence economic activity; in the long run, the path of the economy depends only on population growth, capital accumulation and technical progress.     

Person–environment fit or person plus environment: A meta-analysis of studies using polynomial regression analysis

The purpose of this study was to examine if the non-linear terms in polynomial regression analysis (PRA) explain substantial amounts of variance in assumed outcomes over and above the linear terms. This meta-analysis reviewed a total of 30 studies from 25 articles on person–environment (P-E) fit in which PRA was used. We found that none of the 30 studies did rigorous cross-validation for PRA. In addition, the overall population R² change attributable to the non-linear terms (PE, P² and E²) in PRA was only .008. However, the P-E interaction term may be more important in some situations than in others, based on the data gathered here. Specifically, the non-linear terms in PRA added to the variance explained by the linear terms (P and E) when studies examined assumed outcome variables like contextual performance or job attitudes as opposed to task performance. In summary, PRA could potentially be effective in investigating P-E fit and other human resource management-related phenomena when it is used in appropriate scenarios.     

What Motivates Experts to Share? A Prospective Test of the Model of Knowledge‐Sharing Motivation

Knowledge sharing is an employee behavior, critical to organizational success in knowledge‐intensive work environments. This study set out to empirically test the model of knowledge‐sharing motivation designed and presented in this journal by Gagné (2009). The model combines two established behavioral theories, the theory of planned behavior and self‐determination theory, and connects various human resource practices to it. This prospective survey study ( n = 200) in a large expert organization employed structural equation modeling. The results mainly supported the proposed model, with attitudes, autonomous motivation, and sharing norms predicting knowledge‐sharing intentions ( R² = .69), which predicted knowledge‐sharing behavior ( R² = .42). We also identified potential ways to modify the model to better suit typical knowledge‐sharing contexts. Implications for practice, with the emphasis on how our findings can benefit and be used by human resource management, are discussed. © 2016 Wiley Periodicals, Inc.