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In order to provide an updated view on the drivers of German stock returns, the authors evaluate the relative performance of nine competing neoclassical asset pricing models in the German stock market between November 1991 and December 2021.

The authors conduct asymptotically valid tests of model comparison when the extent of model mispricing is gauged by the squared Sharpe ratio improvement measure of Barillas et al. (2020).

The study finds that the Fama and French six-factor model with both traditional and updated value factors emerges as the dominant model.

The authors shed new light on the drivers of German stock returns through an updated and extended period of analysis, wider range of potential models and utilization of valid asymptotic tests of model comparison when models are nonnested (Barillas et al., 2020).

We extend Vuong’s (1989) model-selection statistic to allow for complex survey samples. As a further extension, we use an M-estimation setting so that the tests apply to general estimation problems – such as linear and nonlinear least squares, Poisson regression and fractional response models, to name just a few – and not only to maximum likelihood settings. With stratified sampling, we show how the difference in objective functions should be weighted in order to obtain a suitable test statistic. Interestingly, the weights are needed in computing the model-selection statistic even in cases where stratification is appropriately exogenous, in which case the usual unweighted estimators for the parameters are consistent. With cluster samples and panel data, we show how to combine the weighted objective function with a cluster-robust variance estimator in order to expand the scope of the model-selection tests. A small simulation study shows that the weighted test is promising.

We investigate the dynamics of competitive repositioning of firms in the deregulated U.S. airline industry (1979–1995) in terms of a firm's target market, strategic posture, and resource endowment relative to other firms in the industry. We suggest that, despite strong inertia in competitive positions, the direction of repositioning responds to external and internal alignment considerations. For external alignment, we examined how firms changed their competitive positioning to mimic the positions of similar, successful firms, and to differentiate themselves when experiencing intense rivalry. For internal alignment, we examined how firms changed their position in each dimension to align with the other dimensions of positioning. This internal alignment led to convergent positioning moves for firms with similar resource endowments and strategic postures, and divergent moves for firms with similar target markets and strategic postures. The evidence suggests that repositioning moves in terms of target markets and resource endowments are more sensitive to external and internal alignment considerations, but that changes in strategic posture are subject to very high inertia and do not appear to respond well to alignment considerations.

In this chapter we discuss model selection and predictive accuracy tests in the context of parameter and model uncertainty under recursive and rolling estimation schemes. We begin by summarizing some recent theoretical findings, with particular emphasis on the construction of valid bootstrap procedures for calculating the impact of parameter estimation error. We then discuss the Corradi and Swanson (2002) (CS) test of (non)linear out-of-sample Granger causality. Thereafter, we carry out a series of Monte Carlo experiments examining the properties of the CS and a variety of other related predictive accuracy and model selection type tests. Finally, we present the results of an empirical investigation of the marginal predictive content of money for income, in the spirit of Stock and Watson (1989), Swanson (1998) and Amato and Swanson (2001).

Cumulative capability models are dominating frameworks explaining how manufacturing organizations gain their performance capabilities, such as quality, delivery, flexibility and cost. When innovation capabilities are excluded from the framework, the models are incapable of explaining how companies sustain substantive capabilities in a changing environment. Responding to this gap, the purpose of this paper is to propose and test a “sand cone” cumulative capability model that includes the innovation competitive performance alongside the competitive performance of quality, delivery flexibility and cost.

Two competing cumulative models were proposed. The extended cumulative capability model hypothesizes the development of innovation in sequence with other competitive performance dimensions. The affected with innovation cumulative model hypothesizes innovation performance as a predecessor of other performance dimensions. The models were tested using a multimethod approach on a representative sample of 500 manufacturing companies. An analysis of correlations among competitive performance, frequencies of plants following prescribed sequences, fit statistics of covariance-based structural equation modeling and analysis of strength and statistical significance of path coefficients enabled us to select a model that best represents the collected data.

The findings reveal that innovation competitive performance operates as a predecessor of quality, delivery, flexibility and cost and is developed in relation to these performance dimensions. The modified model also provides a theoretical explanation of how innovation performance helps to sustain reliable production systems that can perform consistently over time within a tolerable range of quality, delivery, flexibility and cost performance.

The results are significant for practitioners, especially for companies that are operating in volatile environments because the results provide insight on how to develop innovation competitive performance in relation to quality, delivery, flexibility and cost performance.

This study extends the cumulative capability models with innovation competitive performance. It advances the contingency approach on cumulative capability models.

Relationship quality (RQ) is nowadays seen as a main source of competitive advantage. This study aims at enhancing our understanding of RQ by testing and comparing three alternative business‐to‐business (B2B) RQ models from an interpersonal perspective.

A mail survey was employed and 1,126 firm representatives provided their perceptions of their relationships with their counterparts in hotels, yielding a 48.4 per cent response rate. Data was analysed through structural equations modelling with LISREL.

Findings clarified the way RQ key constructs connect, as well as the relative importance of their dimensions. Trust was the dominant dimension of RQ, which contributed to strengthen the pivotal role of trust as a RQ key construct. Problem‐ solving behaviour and selling orientation were the most important dimensions of customer orientation.

Due to possible limitations inherent to the chosen research approach, longitudinal studies in different research settings that may improve the generalisability of the results are suggested for future investigations.

Results emphasised customer orientation as an additional, crucial building block of RQ, drawing the attention to the pivotal role of (designated) relationship managers, the “part‐time” marketers who act as the “face” of the organization, in maximising B2B RQ.

New and relevant insights are provided regarding the relative importance of the dimensions of each RQ key construct, which will help relationship managers to evolve from a selling to a counselling approach, thereby promoting commitment, trust, and mutuality of goals, towards the improvement of the quality of the relationships between business partners.

Purpose – The purpose of this chapter is to present a subset of seven statistical and methodological myths and urban legends (SMMULs). When present, SMMULs degrade the overall research process and make manuscript evaluation problematic during the review process. SMMULs covered here included those pertaining to accepting the theoretical model, conventional cutoff values, exploratory factor analysis, common method bias, moderation analysis, Baron and Kenny's four-step mediation test, and permitting correlated item residuals.

Design/Methodology – Given that the details underlying the SMMULs have already been published, the present chapter was a summary of each. The summaries presented the urban legend and sources for it. Subsequently, the kernel of truth underlying the SMMUL was presented, and how this truth may have been lost and distorted. Each summary ends with the recommended “good” practices as presented by the original authors.

Findings/Implications – The implication for researchers is to modify their current practices to strengthen their research and to make better inferences. And for editors and reviewers, the implication is to develop accurate decision rules to strengthen the review process.

Originality/Value – The overall value of the chapter is to improve the research process in general.

We compare environmental impacts associated with incoming foreign direct investment versus domestic capital in China. We use aggregate data on Chinese provinces’ economic and pollution indicators to explore the effects of the financial origin of fixed capital. Our simultaneous models consider three prime channels through which these effects work: economic scale, sectoral composition, and pollution intensity. Results show that emissions associated with foreign financed capital are lower than with domestically financed capital for some but not all of the considered types of pollution.

The purpose of this paper is to investigate as well as select various significant demand pull (DP) strategies affecting sustainable development (SD) in manufacturing organizations. The study deploys the structural equation modeling (SEM) technique to empirically validate the interrelationships between significant DP strategies and SD indicators in an SEM-DP Model.

Confirmatory factor analysis approach is applied to generate an effective SEM-DP model using the AMOS 21 (Analysis of Moment Structures) software. The data have been collected from different manufacturing organizations practicing DP strategies, using a well-framed DP questionnaire for the evolution of the SEM-DP model.

SEM of various DP strategies like stringent implementation of government regulations (SIGR), transforming capabilities, unionized labor (UL), and customer attributes (CA) toward achieving SD in manufacturing industries has been performed. The SEM-DP model has been planned and reports obtained before and after modification indices of the model are correlated, which further establishes improvements in the model’s effectiveness. The research concludes that significant DP strategies, namely, SIGR, UL, and CA support the manufacturing industries in accomplishing SD in terms of competitiveness, business performance enhancements, flexibility, customer satisfaction, and technological development.

In the present study, contributions of DP practices are determined to accomplish SD in Indian manufacturing organizations only. Hence, the results obtained may need some modifications before applying to other countries. Moreover, issue-wise independent modeling can also be performed to assess the importance of DP practices under specific orientations.

The results of various interrelationships among DP practices and SD indicators in the SEM-DP model portray the effectiveness of DP practices for achieving organizational goals and social commitments.

The outcomes of the study will help DP practitioners, organizational managers, and HR executives in the manufacturing industries to develop a clear understanding about the significant DP strategies to be followed holistically for accomplishing SD. The manufacturing enterprises will be able to frame and organize their policies, handle their UL issues and CA in a more appropriate way. Hence, the knowledge obtained from present study will help improve the overall performance of manufacturing industries involved in the present context.