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Measurement of diminishing or divergent cross section dispersion in a panel plays an important role in the assessment of convergence or divergence over time in key economic indicators. Econometric methods, known as weak σ-convergence tests, have recently been developed (Kong, Phillips, & Sul, 2019) to evaluate such trends in dispersion in panel data using simple linear trend regressions. To achieve generality in applications, these tests rely on heteroskedastic and autocorrelation consistent (HAC) variance estimates. The present chapter examines the behavior of these convergence tests when heteroskedastic and autocorrelation robust (HAR) variance estimates using fixed-b methods are employed instead of HAC estimates. Asymptotic theory for both HAC and HAR convergence tests is derived and numerical simulations are used to assess performance in null (no convergence) and alternative (convergence) cases. While the use of HAR statistics tends to reduce size distortion, as has been found in earlier analytic and numerical research, use of HAR estimates in nonparametric standardization leads to significant power differences asymptotically, which are reflected in finite sample performance in numerical exercises. The explanation is that weak σ-convergence tests rely on intentionally misspecified linear trend regression formulations of unknown trend decay functions that model convergence behavior rather than regressions with correctly specified trend decay functions. Some new results on the use of HAR inference with trending regressors are derived and an empirical application to assess diminishing variation in US State unemployment rates is included.

The purpose of this paper is to answer one important question: in the aftermath of a systemic banking crisis, can the expected deviations in credit supply, liquidity, and other bank characteristics become entrenched in that they do not converge back to “normal”?

Using a panel data set of commercial banks in the Mercosur during the period 1990-2006, the authors analyze the impact of crises on four sets of financial indicators of bank behavior and outcomes – profitability, maturity preference, credit supply, and risk taking. The authors employ convergence methodology – which is often used in the growth literature – to identify the evolution of bank behavior in the region after crises.

A key finding of the paper is that bank risk-taking behavior is significantly modified leading to prolonged reduction of intermediation to the private sector in favor of less risky government securities and preference for high levels excess liquidity well after the crisis. This can be attributed to the role played by macroeconomic and institutional volatility that has nurtured a relatively high level of risk aversion in banks in the Mercosur.

To the best of the authors’ knowledge, using convergence methodology is a relatively novel approach in this area. An added advantage of using this approach over others currently used in the literature is that the authors can empirically quantify the rate of convergence and the institutional and macroeconomic factors that condition the convergence. Moreover, the methodology allows one to identify – in some hierarchical order – factors that condition persistent deviation from “normality.” The lessons learned from the Mercosur case study are useful for countries that suffered systemic banking crises in the aftermath of the global financial crisis.

The purpose of this paper is to propose multigrid methods in conjunction with explicit approximate inverses with various cycles strategies and comparison with the other smoothers.

The main motive for the derivation of the various multigrid schemes lies in the efficiency of the multigrid methods as well as the explicit approximate inverses. The combination of the various multigrid cycles with the explicit approximate inverses as smoothers in conjunction with the dynamic over/under relaxation (DOUR) algorithm results in efficient schemes for solving large sparse linear systems derived from the discretization of partial differential equations (PDE).

Application of the proposed multigrid methods on two-dimensional boundary value problems is discussed and numerical results are given concerning the convergence behavior and the convergence factors. The results are comparatively better than the V-cycle multigrid schemes presented in a recent report (Filelis-Papadopoulos and Gravvanis).

The limitations of the proposed scheme lie in the fact that the explicit finite difference approximate inverse matrix used as smoother in the multigrid method is a preconditioner for specific sparsity pattern. Further research is carried out in order to derive a generic explicit approximate inverse for any type of sparsity pattern.

A novel smoother for the geometric multigrid method is proposed, based on optimized banded approximate inverse matrix preconditioner, the Richardson method in conjunction with the DOUR scheme, for solving large sparse linear systems derived from finite difference discretization of PDEs. Moreover, the applicability and convergence behavior of the proposed scheme is examined based on various cycles and comparative results are given against the damped Jacobi smoother.

Diffusion studies investigate the propagation of behavior, attitudes, or beliefs across a networked population. Some behavior is binary, e.g., whether or not to install solar panels, while other behavior is continuous, e.g., wastefulness with plastic. Similarly, attitudes and beliefs often allow nuance, but can become practically binary in polarized environments. We argue that this property of behavior and attitudes – whether they are binary or continuous – should critically affect whether a population becomes homogenous in its adoption of that behavior. Models show that only continuous behavior converges across a network. Specifically, binary behavior allows local convergence, as multiple states can be local majorities. Continuous behavior becomes uniform across the network through a logic of communicating vessels. We present a model comparing the diffusion of both types of behavior and report on a laboratory experiment that tests it. In the model, actors have to distribute an investment over two options, while a majority receives information that points to the optimal option and a minority receives misguided information that points toward the other option. We predict that when adjacent persons receive misguided information this can hinder convergence toward optimal investment behavior in small networked groups, especially when subjects cannot split their investment, i.e., binary choice. Results falsify our theoretical predictions: Although investment decisions are significantly negatively affected by local majorities only in the binary condition, this difference with the continuous condition is not itself significant. Binary and continuous behavior therefore achieve comparable incidences of optimal investment in the experiment. The failure of the theoretical predictions appears due to a substantial level of error in decision-making, which prevents local majorities from locking in on a suboptimal behavior.

The purpose of this paper is to explain why many socially responsible firms appear to converge on a standard set of corporate social responsibility (CSR) practices instead of striving to differentiate themselves from rivals and achieve competitive advantage.

Three explanations of this convergence are presented as follows: herd behaviour, institutional isomorphism and strategic cooperation. The different empirical predictions of these theories are laid down. The resulting framework is used to analyse a recent self‐regulatory scheme launched by the steel industry, in which knowledge sharing was used to stimulate poor performers to curb carbon dioxide emissions.

Social practices of firms are very often driven by pressures to conform, instead of pressures to perform. Even firms that want to be innovative may be forced by stakeholder requests to adopt passive and imitative behaviour.

The paper suggests that there are two types of CSR – convergent and divergent – and that firms need to establish which type of CSR best fits their needs before they address the issues raised by stakeholders.

The literature on CSR focuses on the relationship between stakeholders and single firms. The paper tries to add to this literature by analysing the relationship between stakeholders and industries. The paper also contributes to the debate on the financial benefits of CSR by arguing that in industries where the convergent type of CSR is dominant researchers should not expect above‐average returns for socially responsible firms.

The purpose of this paper is to propose novel factored approximate sparse inverse schemes and multi-level methods for the solution of large sparse linear systems.

The main motive for the derivation of the various generic preconditioning schemes lies to the efficiency and effectiveness of factored preconditioning schemes in conjunction with Krylov subspace iterative methods as well as multi-level techniques for solving various model problems. Factored approximate inverses, namely, Generic Factored Approximate Sparse Inverse, require less fill-in and are computed faster due to the reduced number of nonzero elements. A modified column wise approach, namely, Modified Generic Factored Approximate Sparse Inverse, is also proposed to further enhance performance. The multi-level approximate inverse scheme, namely, Multi-level Algebraic Recursive Generic Approximate Inverse Solver, utilizes a multi-level hierarchy formed using Block Independent Set reordering scheme and an approximation of the Schur complement that results in the solution of reduced order linear systems thus enhancing performance and convergence behavior. Moreover, a theoretical estimate for the quality of the multi-level approximate inverse is also provided.

Application of the proposed schemes to various model problems is discussed and numerical results are given concerning the convergence behavior and the convergence factors. The results are comparatively better than results by other researchers for some of the model problems.

Further enhancements are investigated for the proposed factored approximate inverse schemes as well as the multi-level techniques to improve quality of the schemes. Furthermore, the proposed schemes rely on the definition of multiple parameters that for some problems require thorough testing, thus adaptive techniques to define the values of the various parameters are currently under research. Moreover, parallel schemes will be investigated.

The proposed approximate inverse preconditioning schemes as well as multi-level schemes are efficient computational methods that are valuable for computer scientists and for scientists and engineers in engineering computations.

The purpose of this study is to examine whether the electricity consumption patterns across Indian states do converge.

This study considers 18 Indian states spanning over the period 1970-1971 and 2014-2015, using the recently developed Phillips and Sul panel convergence technique that accounts the multiple steady states.

The results provide the following insights. First, the authors find evidence of convergence in electricity consumption among all Indian states. This suggests that electricity consumption patterns for Indian states are converging to a common steady state. Second, to provide broader insights, we further investigate the convergence in electricity consumption among user groups such as agriculture, industry, commercial, domestic and miscellaneous. The results reveal that commercial, domestic and miscellaneous groups are also converging. Third, the non-convergence patterns in agriculture and industry enable us to investigate the possibility of clubs or the multiple common steady states. The results indicate the occurrence of three clubs in case of agriculture and two clubs in case of the industry. Fourth, this study also inspects the relative speed of convergence among the user groups. The results reveal the higher speed of convergence in case of the domestic user group.

The findings enable policymakers to formulate an appropriate energy policy to accommodate the future electricity demand across Indian states and prioritize low electricity consumption states so that they receive a greater share.

This is the first study that examines the convergence in electricity consumption across Indian states at aggregate and user groups using a new panel club convergence technique.

Prediction markets are techniques to aggregate dispersed public opinions via market mechanisms to predict uncertain future events’ outcome. Many experiments have shown that prediction markets outperform other traditional forecasting methods in terms of accuracy. Logarithmic market scoring rules (LMSR) is one of the most simple and widely used market mechanisms; however, market makers have to confront crucial design decisions including the setting of the parameter “b” or the “liquidity parameter” in the price functions. As the liquidity parameter has significant effects on the market performance, this paper aims to provide a comprehensive basis for the setting of the parameter.

The analyses include the effects of the liquidity parameter on the forecast standard error and the amount of time for the market price to converge to the true value. These experiments use artificial prediction markets, the proposed simulation models that mimic real prediction markets.

The simulation results indicate that prediction market’s forecast standard error decreases as the value of the liquidity parameter increases. Moreover, for any given number of traders in the market, there exists an optimal liquidity parameter value that yields appropriate price adaptability and leads to the fastest price convergence.

Understanding these tradeoffs, the market makers can effectively determine the liquidity parameter value under various objectives on the standard error, the time to convergence and cost.

It has been usual to prefer an enrichment pattern independent of the mesh when applying singular functions in the Generalized/eXtended finite element method (G/XFEM). This choice, when modeling crack tip singularities through extrinsic enrichment, has been understood as the only way to surpass the typical poor convergence rate obtained with the finite element method (FEM), on uniform or quasi-uniform meshes conforming to the crack. Then, the purpose of this study is to revisit the topological enrichment strategy in the light of a higher-order continuity obtained with a smooth partition of unity (PoU). Aiming to verify the smoothness' impacts on the blending phenomenon, a series of numerical experiments is conceived to compare the two GFEM versions: the conventional one, based on piecewise continuous PoU's, and another which considers PoU's with high-regularity.

The stress approximations right at the crack tip vicinity are qualified by focusing on crack severity parameters. For this purpose, the material forces method originated from the configurational mechanics is employed. Some attempts to improve solution using different polynomial enrichment schemes, besides the singular one, are discussed aiming to verify the transition/blending effects. A classical two-dimensional problem of the linear elastic fracture mechanics (LEFM) is solved, considering the pure mode I and the mixed-mode loadings.

The results reveal that, in the presence of smooth PoU's, the topological enrichment can still be considered as a suitable strategy for extrinsic enrichment. First, because such an enrichment pattern still can treat the crack independently of the mesh and deliver some advantage in terms of convergence rates, under certain conditions, when compared to the conventional FEM. Second, because the topological pattern demands fewer degrees of freedom and impacts conditioning less than the geometrical strategy.

Several outputs are presented, considering estimations for the J–integral and the angle of probable crack advance, this last computed from two different strategies to monitoring blending/transition effects, besides some comments about conditioning. Both h- and p-behaviors are displayed to allow a discussion from different points of view concerning the topological enrichment in smooth GFEM.

The study aims to drive conceptual clarity around resistance to information technology projects, integrating multiple facets of the phenomenon from earlier studies.

The study conducts a meta-synthesis of qualitative studies on resistance to technology projects; it analyzes those studies at a case-specific level, compares and contrasts emergent concepts against each other, and “translates” those to the rest of the studies. The study uses the seven-step meta-ethnography method by Noblit and Hare to reciprocally translate emergent concepts to construct the conceptual model.

Through meta-synthesis, the study derives a new conceptual model for resistance to information technology projects, exemplifying how the identified antecedents create user resistance and how the phenomenon progresses within organizations.

This study enriches the observations and conclusions of past individual studies while explicating various facets of the mechanisms that generate and progress technology resistance within organizations. It offers fresh insights into the equivocal nature of the phenomenon and the distinctive ways it progresses from individual to group level.

Many ambitious and costly digital transformation efforts do not succeed due to user resistance. Understanding the mechanisms that create user resistance can help organizations manage technology projects better, thereby reducing the technology assimilation gap and protecting returns on related investments.

There have been extensive studies on technology acceptance (enablers) within organizations, while those relating to technology inhibitors are somewhat limited. However, the symmetry of understanding between enablers and inhibitors is vital for organizations to assimilate promising technologies and transform their business models. This model uses a new lens of sensemaking theory to explain how the antecedents trigger perceived threats and resistance behavior; it highlights the nuances around the development of resistance within individuals and its progression to groups. The resultant model offers better generalizability in organizational contexts.