Co-evolutionary Dynamics Within and Between Firms: From Evolution to Co-evolution

abstract The extensive selection–adaptation literature spans diverse theoretical perspectives, but is inconclusive on the role of managerial intentionality in organizational adaptation. Indeed this voluminous literature has more to say about selection and sources and causes of structural inertia than about self‐renewing organizations that might counteract such inertia. In this introductory essay, we identify four co‐evolutionary generative mechanisms (engines) – naïve selection, managed selection, hierarchical renewal and holistic renewal – which illustrate the extensive range of evolutionary paths that can take place in a population of organizations. In particular, the managed selection engine provides the foundations of the underlying principles of co‐evolving self‐renewing organizations: managing internal rates of change, optimizing self‐organization, and balancing concurrent exploration and exploitation. However, it is altogether clear that empirical co‐evolution research represents the next frontier for empirically resolving the adaptation selection debate. The essay concludes with a discussion of requirements for co‐evolutionary empirical research and introduces the empirical papers in this Special Research Symposium.     

Empirical research consolidation: a generic overview and a classification scheme for methods

I define research consolidation as comparing and combining (amalgamating or, at least, juxtaposing) results or other output from multiple previously conducted research activities, relevant to a goal or topic of interest. The concept, not limited to empirical research or any specific method, is similar to and subsumes what is often referred to as research synthesis or research integration; I explain the concept and my rationale for the new term. After introducing the broader concept, I focus on consolidation of empirical research. As background for this, I offer a brief introduction to empirical research. Then I provide a generic overview of empirical research consolidation, abstracted from several of its specific methods, such as quantitative meta-analysis and qualitative meta-synthesis; this sort of overview—method agnostic and capturing the commonality—is not readily available in the literature. At the core of the paper, I propose a scheme to classify methods for empirical research consolidation followed by a review and classification of selected methods, illustrating the scheme. I also discuss and clarify related terminology. The classification scheme differentiates between two major attributes of methods—consolidation technique and type of content to be consolidated—that may reflect the positivist or constructivist research paradigm. Consolidation techniques could be aggregative, interpretive, or a combination thereof. Content consolidated, from empirical research reports, could be empirical or not and could be from quantitative research, qualitative research, other kinds of empirical research, or a combination thereof.     

Beyond Publication Bias

Abstract.  This review considers several meta‐regression and graphical methods that can differentiate genuine empirical effect from publication bias. Publication selection exists when editors, reviewers, or researchers have a preference for statistically significant results. Because all areas of empirical research are susceptible to publication selection, any average or tally of significant/insignificant studies is likely to be biased and potentially misleading. Meta‐regression analysis can see through the murk of random sampling error and selected misspecification bias to identify the underlying statistical structures that characterize genuine empirical effect. Meta‐significance testing and precision‐effect testing (PET) are offered as a means to identify empirical effect beyond publication bias and are applied to four areas of empirical economics research – minimum wage effects, union‐productivity effects, price elasticities, and tests of the natural rate hypothesis.     

On finite sample properties of alternative estimators of coefficients in a structural equation with many instruments

We compare four different estimation methods for the coefficients of a linear structural equation with instrumental variables. As the classical methods we consider the limited information maximum likelihood (LIML) estimator and the two-stage least squares (TSLS) estimator, and as the semi-parametric estimation methods we consider the maximum empirical likelihood (MEL) estimator and the generalized method of moments (GMM) (or the estimating equation) estimator. Tables and figures of the distribution functions of four estimators are given for enough values of the parameters to cover most linear models of interest and we include some heteroscedastic cases and nonlinear cases. We have found that the LIML estimator has good performance in terms of the bounded loss functions and probabilities when the number of instruments is large, that is, the micro-econometric models with “many instruments” in the terminology of recent econometric literature.     

Advancing the field of employee assistance programs research and practice: A systematic review of quantitative studies and future research agenda

The COVID-19 pandemic has accentuated the critical role of organizational support for the workforce. An employee assistance program (EAP) represents an inclusive strategy which organizations adopt to provide supportive and empathic care to help employees overcome undesirable situations. To date, we have limited knowledge of what EAP issues have been researched from the human resource management (HRM) perspective and what theoretical underpinning these studies have used. This article systematically reviews quantitative empirical studies on EAPs. Drawing upon 115 articles from 72 journals across 40 years (1981–2020), we trace the evolutionary trend of the construct of EAP and shed light on the internal link of EAP with HRM. After summarizing research themes, methods, theories, and approaches to the evaluation of EAPs, we identify pitfalls in the current research and contribute to extending the field by proposing several research agendas for future investigation.     

A critical evaluation of alternative methods and paradigms for conducting mediation analysis in operations management research

Mediation as a theory testing approach has witnessed considerable adoption among Operations Management (OM) researchers. Although mediation-testing methods have evolved tremendously in the past decade, their dissemination in the OM field has not seen parallel growth. These advanced techniques facilitate the testing of existing and complex hypotheses in a more precise manner. With the intent of critically evaluating existing and alternative methods for conducting mediation analysis needed to support sophisticated empirical research, this paper first reviews OM studies that tested for mediation in the past eleven years (2002–2012) from top-tier OM journals. Four commonly used mediation approaches were identified. Based on principles of good theory building, type of mediation model, and properties of empirical data, we evaluate the existing methodologies and make recommendations on how to improve the rigor of OM mediation testing. Using published OM studies in top journals as examples, we then illustrate the relevance and advantages of these recommendations, as well as their ease of use. Furthermore, we empirically show that more robust and insightful results can be achieved by adopting these techniques, which in turn have the promise of leading to better theory building and testing in the field of operations management.     

International project finance: review and implications for international finance and international business

This literature review analyzes the global phenomenon of international project finance (PF) as both a management and finance instrument, allowing practitioners to realize large scale infrastructure projects in high risk contexts. After describing the characteristics of PF, its historical origins and its unique benefits for empirical inquiry, I summarize the findings of academic research from an interdisciplinary perspective. Based on this integration of Finance, Management and International Business research, I discuss the theoretical implications for each field that emanate from PF. Finally, I identify possibilities for future research and propose a more balanced, interdisciplinary academic treatment of PF.     

Modeling share returns - an empirical study on the Variance Gamma model

Due to the fact that there has been only little research on some essential issues of the Variance Gamma (VG) process, we have recognized a gap in literature as to the performance of the various estimation methods for modeling empirical share returns. While some papers present only few estimated parameters for a very small, selected empirical database, Finaly and Seneta (Int Stat Rev 76:167–186, 2008) compare most of the possible estimation methods using simulated data. In contrast to Finaly and Seneta (2008) we utilize a broad, daily, and empirical data set consisting of the stocks of each company listed on the DOW JONES over the period from 1991 to 2011. We also apply a regime switching model in order to identify normal and turbulent times within our data set and fit the VG process to the data in the respective period. We find out that the VG process parameters vary over time, and in accordance with the regime switching model, we recognize significantly increasing fitting rates which are due to the chosen periods.     

Agent based model of a simple economy

This article proposes a model of a simple economy based on a set of agent-based modeling principles. The model is based on the ??trust game?? formulated by Berg et?al. (Games Econ Behav 10:122?C142, 1995), and anticipates a random matching of partners taking in to account adaptive agent behavior. Simulation in the NetLogo programming environment, using profile distributions obtained from empirical studies, has shown the most successful agents to posses low parameters of trust in the role of Sender and high parameters of trustworthiness in the role of Receiver.     

Technological platforms An assessment of the primary types of technological platforms, their strategic issues and their linkages to organizational theory

The concept of a platform describes a set of system components that is strongly interdependent with most other system components, and that also co-determines the architecture of the system’s outcome. This concept underlies all kinds of technology-based products, collaborations that produce multi-product systems, and transactions between distinct sets of market participants. In this paper, I provide an overview of the growing scientific literature on technological platforms, and also identify three distinct, but related, literature streams: (1) product platforms; (2) industry platforms; and (3) two-sided markets. In doing so, I focus on empirical studies and mathematical models. For each stream, I then go on to describe the type of platform that the stream relates to, review the stream’s major studies (sorted by key strategic issues), and summarize and synthesize the overall findings before linking them to organizational theory and highlighting some potential gaps for future examination. In closing, I move to a comparison across streams and identify some currently unexplored aspects of the platform concept.     

Inference in Cointegrating Models: UK M1 Revisited

The paper addresses the practical determination of cointegration rank. This is difficult for many reasons: deterministic terms play a crucial role in limiting distributions, and systems may not be formulated to ensure similarity to nuisance parameters; finite-sample critical values may differ from asymptotic equivalents; dummy variables alter critical values, often greatly; multiple cointegration vectors must be identified to allow inference; the data may be I(2) rather than I(1), altering distributions; and conditioning must be done with care. These issues are illustrated by an empirical application of multivariate cointegration analysis to a small model of narrow money, prices, output and interest rates in the UK.     

Understanding machine learning-based forecasting methods: A decomposition framework and research opportunities

Machine learning (ML) methods are gaining popularity in the forecasting field, as they have shown strong empirical performance in the recent M4 and M5 competitions, as well as in several Kaggle competitions. However, understanding why and how these methods work well for forecasting is still at a very early stage, partly due to their complexity. In this paper, I present a framework for regression-based ML that provides researchers with a common language and abstraction to aid in their study. To demonstrate the utility of the framework, I show how it can be used to map and compare ML methods used in the M5 Uncertainty competition. I then describe how the framework can be used together with ablation testing to systematically study their performance. Lastly, I use the framework to provide an overview of the solution space in regression-based ML forecasting, identifying areas for further research.     

Roles,Authority and Involvement of the Management Accounting Function: A Multiple Case-study Perspective

Recent techniques and shifts in the environment are often viewed as leading management accountants to adopt a business orientation. However, empirical evidence pointing to clear shifts in the role played by management accountants remains relatively scarce. From in-depth qualitative research based on 73 interviews in ten multinational companies, we identify four distinct styles of management accounting function: discrete, safeguarding, partner, and omnipotent. We show that each style can be associated with one main role: discrete control of managerial behaviour, socialisation of managers, facilitation of decision-making, and centralisation of power. From this in-depth analysis of management accountants' styles and roles we move on to discuss the authority they hold and the independence/involvement dilemma they face.     

Technological platforms

The concept of a platform describes a set of system components that is strongly interdependent with most other system components, and that also co-determines the architecture of the system’s outcome. This concept underlies all kinds of technology-based products, collaborations that produce multi-product systems, and transactions between distinct sets of market participants. In this paper, I provide an overview of the growing scientific literature on technological platforms, and also identify three distinct, but related, literature streams: (1) product platforms; (2) industry platforms; and (3) two-sided markets. In doing so, I focus on empirical studies and mathematical models. For each stream, I then go on to describe the type of platform that the stream relates to, review the stream’s major studies (sorted by key strategic issues), and summarize and synthesize the overall findings before linking them to organizational theory and highlighting some potential gaps for future examination. In closing, I move to a comparison across streams and identify some currently unexplored aspects of the platform concept.     

Principles and algorithms for forecasting groups of time series: Locality and globality

Global methods that fit a single forecasting method to all time series in a set have recently shown surprising accuracy, even when forecasting large groups of heterogeneous time series. We provide the following contributions that help understand the potential and applicability of global methods and how they relate to traditional local methods that fit a separate forecasting method to each series:

• •Global and local methods can produce the same forecasts without any assumptions about similarity of the series in the set.
• •The complexity of local methods grows with the size of the set while it remains constant for global methods. This result supports the recent evidence and provides principles for the design of new algorithms.
• •In an extensive empirical study, we show that purposely naïve algorithms derived from these principles show outstanding accuracy. In particular, global linear models provide competitive accuracy with far fewer parameters than the simplest of local methods.

     

审计风险研究综述

从研究范式与研究方法来看,审计风险研究主要分为三类:(1)在审计学与管理学的逻辑框架内采用理论演绎法或规范研究讨论审计目标、审计环境、审计责任、审计方法与审计风险之间的关系;(2)以实验研究与实地研究的方法在审计学、心理学、行为科学与社会学的逻辑框架内,分析审计风险的影响因素与控制方法;(3)用经验研究的方法在法与经济学的逻辑框架内,讨论制度环境、审计风险与审计市场结构之间的关系。以往国内审计风险的研究主要采用规范研究较多,实验研究、实地研究以及经验研究相对欠缺。     

Strategic CSR: A Concept Building Meta-Analysis

This study develops the concept of Strategic Corporate Social Responsibility (Strategic CSR) by meta-analyzing the available empirical evidence on the relationship between CSR and corporate financial performance (CFP). Using meta-analytic structural equation modeling on effect size data from 344 primary studies, our study documents four empirical mechanisms explaining how CSR positively affects CFP: by 1) enhancing firm reputation, 2) increasing stakeholder reciprocation, 3) mitigating firm risk, and 4) strengthening innovation capacity. We propose these four mechanisms to identify four causally relevant attributes that allow us to conceptually distinguish Strategic CSR from CSR more generally. Our findings indicate that the four mechanisms combined explain 20 per cent of the CSR-CFP relationship, suggesting that considerable room remains for future empirical research. The development of an empirically informed, causal conceptualization of Strategic CSR responds to a long-heard call for better-specified concepts in empirical CSR research.     

Analysing I(2) Systems by Transformed Vector Autoregressions*

We characterize the restrictions imposed by the minimal I(2)‐to‐I(1) transformation that underlies much applied work, e.g. on money demand relationships or open‐economy pricing relationships. The relationship between the parameters of the original I(2) vector autoregression, including the coefficients of polynomially cointegrating relationships, and the transformed I(1) model is characterized. We discuss estimation of the transformed model subject to restrictions as well as the more commonly used approach of unrestricted reduced rank regression. Only a minor loss of efficiency is incurred by ignoring the restrictions in the empirical example and a simulation study. A properly transformed vector autoregression thus provides a practical and effective means for inference on the parameters of the I(2) model.