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This paper attempts to demystify the technique of causal path modeling for the non‐specialists by presenting aspects of its value for social science and management research and by illustrating common misunderstandings about its attributes. Special emphasis is placed on the real world validity of causal relationships depicted in causal path models and on the information that the data‐fitting properties of causal path models provide regarding this issue. Causal path models that are based on research in antecedents of career success are used to illustrate the points that are made. It is stressed that the validity of causal relationships depicted in causal path models is subject to exactly the same methodological restrictions as the validity of causality claims that are made without the use of causal path modeling; and that the purpose of using quantitative techniques in causal path modeling is not to improve certainty on causality direction.

Performance measurement has benefited from several management accounting innovations over the past decade. Guiding these advances is the explicit recognition that it is imperative to understand the causal linkage that leads a firm to profitability. In this paper, we contend that the relationship quality experienced between two organizations has a measurable impact on performance. Guided by prior models developed in distribution channel and relationship marketing research (Cannon et al., 2000; Morgan & Hunt, 1994) we build a causal model of relationship quality that identifies key relationship qualities that drive a series of financial and non-financial performance outcomes. Using the healthcare industry to illustrate its applicability, the physician practice – insurance company relationship is described within the context of the model’s constructs and causal linkages. Our model offers managers employing a causal performance measurement system such as, the balanced scorecard (Kaplan & Norton, 1996) or the action-profit-linkage model (Epstein et al., 2000), a formal framework to analyze observed outcome metrics by assessing the underlying dynamics in their third party relationships. Many of these forces have subtle, but tangible impacts on organizational performance. Recognizing them within performance measurement theory adds explanatory power to existing performance measurement systems.

The purpose of this paper is to propose a practical conceptualization of the balanced scorecard (BSC) to describe the mechanism producing creation of monetary value for hospitals in the territorial context of Lombardy region (Italy).

The authors propose a model‐building strategy that assigns key indicators to key performance areas, and identifies causal relationships between key performance areas. Second, the authors utilize a suitable statistical approach to estimate causal relationships among involved latent variables, taking into account the hierarchical structure of data. Utilizing a suitable data decomposition, the causal model is applied separately to the within data (hospitals) and to the between data (local health agencies).

In the measurement model a new latent construct (medical human capital) was found that resumes the amount of formal training and the performance of surgical staff in hospitals. The estimated causal models reflect the usual directional assumptions, supposed in a typical BSC causal scheme, with some differences. For local health agencies, fruits (financial measures) are strongly related to clinical processes (leaves) for which the medical human capital constitutes its unique trunk. However, for hospitals, fruits (financial measures) are directly linked to clinical processes and Patient Satisfaction.

The main limitations of this study are the lack of new independent data to validate the obtained causal structures and the limited number of indicators that reflect the deficiency of available information in regional administrative archives.

The present study may be useful to guide further efforts which attempt to conceptualize BSC in the health sector. As more information can be made available, other performance indicators can prove to be linked with this structure using the same methodology.

Though logistics security only took care of trading phase in the past, many countries in the world have begun to introduce logistics security system as its coverage has been extended from production stage to delivery at the final destination. Logistics security system has become indispensable element for global corporations involved in international trading and studies on logistics security keep going on. Most of the studies, however, are focused on discussion of system, cost and influence of logistics security and few of them have been specifically dealing with substantial effectiveness thereof. This study developed the models of supply chain security activities and their outcome by means of using Balanced Scorecard (BCS) which is a well known performance indicator to identify relationship between supply chain security activities and their accomplishment. In this study we have presented 8 supply chain frameworks, human resources management, information system management, facilities/freight management, security process, crisis management capability, relationship with partners, sharing of logistics information and logistics security accomplishment, with reference to standards of C-TPAT and AEO based on WCO framework, 10 supply chain security capabilities. This study further indicates that relationship with partners has more effect on logistics security accomplishment than sharing of logistic information. Just as relationship between corporations in chain of supply and sharing of information among them are important elements in management of supply chain, relationship with partners and sharing of logistic information will have positive effect on supply chain security accomplishment and raise its effectiveness.

Analyzing factors of delays in construction projects and determining their impact on project performance is necessary to better manage and control projects. Identification of root factors which may lead to project delay and increased cost is vital at the early or planning stage. Better identification of delay factors at the early stage can help the practitioners to reduce their impacts over the long run. Hence, the purpose of this paper is to propose an intelligent method to analyze causal relationships between delay factors in construction projects. The proposed approach is further validated by a real case study of the construction projects in West Azerbaijan province in Iran.

During the first phase, the fuzzy cognitive map (FCM) is drawn to indicate the causal relationships between the delay factors and the evaluation factors. For this purpose, the causal relationships between 20 delay factors and four evaluation factors are considered. Afterward, the effect of each factor on management goals is evaluated by using a hybrid learning algorithm. Delay factors are further prioritized by applying fuzzy data envelopment analysis (FDEA). In the second phase, an interpretive structural modeling (ISM) is employed to determine the root causes of delay factors.

Results of the first phase show that “supervision technical weaknesses for overcoming technical and executive workshop problems” and “Inaccurate estimation of workload, required equipment and project completion time” are the most significant delay factors. In contrary, “non-use of new engineering contracts” has the lowest impact on the management goals. Meanwhile, the results of the second phase conclude that factors like “Inaccurate estimation of workload, required equipment and project completion time” “weakness of laws and regulations related to job responsibilities” and “lack of foreseen of fines and encouragements in the contracts” are the most significant root factors of delay in construction projects.

This paper integrates three methods including FCM method, FDEA and ISM. In the first phase, FCM is drawn according to the experts’ opinions and concerning management goals and delay factors. Later, these factors are prioritized according to the results of running the algorithm and using the FDEA model. The second phase, the seven-step in the ISM methodology, is done to identify the root factors. To ensure that the root factors of the delay are at a lower level of hierarchical structure, delay factors are partitioned by drawing the ISM model.

This paper aims to introduce a crowd-based method for theorizing. The purpose is not to achieve a scientific theory. On the contrary, the purpose is to achieve a model that may challenge current scientific theories or lead research in new phenomena.

This paper describes a case study of theorizing by using a crowd-based method. The first section of the paper introduces what do the authors know about crowdsourcing, crowd science and the aggregation of non-expert views. The second section details the case study. The third section analyses the aggregation. Finally, the fourth section elaborates the conclusions, limitations and future research.

This document answers to what extent the crowd-based method produces similar results to theories tested and published by experts.

From a theoretical perspective, this study provides evidence to support the research agenda associated with crowd science. The main limitation of this study is that the crowded research models and the expert research models are compared in terms of the graph. Nevertheless, some academics may argue that theory building is about an academic heritage.

This paper exemplifies how to obtain an expert-level research model by aggregating the views of non-experts.

This study is particularly important for institutions with limited access to costly databases, labs and researchers.

Previous research suggested that a collective of individuals may help to conduct all the stages of a research endeavour. Nevertheless, a formal method for theorizing based on the aggregation of non-expert views does not exist. This paper provides the method and evidence of its practical implications.

Although the effects of both news sentiment and expectations on price in financial markets have now been extensively demonstrated, the jointness that these predictors can have in their effects on price has not been well-defined. Investigating causal ordering in their effects on price can further our understanding of both direct and indirect effects in their relationship to market price.

We use autoregressive distributed lag (ARDL) methodology to examine the relationship between agent expectations and news sentiment in predicting price in a financial market. The ARDL estimation is supplemented by Grainger causality testing.

In the ARDL models we implement, measures of expectations and news sentiment and their lags were confirmed to be significantly related to market price in separate estimates. Our results further indicate that in models of relationships between these predictors, news sentiment is a significant predictor of agent expectations, but agent expectations are not significant predictors of news sentiment. Granger-causality estimates confirmed the causal inferences from ARDL results.

Taken together, the results extend our understanding of the dynamics of expectations and sentiment as exogenous information sources that relate to price in financial markets. They suggest that the extensively cited predictor of news sentiment can have both a direct effect on market price and an indirect effect on price through agent expectations.

Even traditional financial management firms now commonly track behavioral measures of expectations and market sentiment. More complete understanding of the relationship between these predictors of market price can further their representation in predictive models.

This article extends the frequently reported bivariate relationship of expectations and sentiment to market price to examine jointness in the relationship between these variables in predicting price. Inference from ARDL estimates is supported by Grainger-causality estimates.

The purpose of this study is to investigate the causal interrelations among the four pillars of corporate sustainability, which indicate a firm’s contribution to environmental, social, governance and economic activities. Moreover, this study identifies the critical drivers of corporate sustainability by focusing on the levels of market developments and geographical regions.

Based on corporate sustainability data of 2,725 global companies in 2016, this study uses a combination of analytical techniques including cluster analysis, data mining, partial least square path modeling and importance performance map analysis.

This study finds that companies in European developed markets exhibit the highest-ranking of corporate sustainability. In line with the social impact hypothesis, environmental, social and governance performance positively affects economic performance. Moreover, there is strong evidence of causal relationships and synergistic effects among the four pillars of corporate sustainability. In accordance with the institutional theory, the patterns of causal directions and the critical pillars depend on levels of market developments and geographical regions. Overall, social and environmental pillars are among the most critical drivers of corporate sustainability.

The methodology does not aim to provide a new weighting scheme for calculating the corporate sustainability index.

Corporate managers should consider sustainability practices in all dimensions to benefit from synergistic effects among environmental, social, governance and economic activities. Furthermore, corporate sustainability strategies should not be generalized across countries with different levels of market developments and geographical regions.

This study prioritizes environmental, social, governance and economic pillars of corporate sustainability in emerging and developed markets across geographical regions.

The purpose of this study is to propose a performance measurement (PM) model for agricultural extension agents. Based on an interdisciplinary approach, management accounting-agricultural extension, the study has three main research objectives: highlight the main concepts to be embedded in a PM model for agricultural extension agents in an agricultural extension organization (RO1); identify main PM components of the proposed PM model for agricultural extension agents (RO2); and investigate empirically the causal relationships in the proposed PM model (RO3).

An interdisciplinary literature review and a proposed PM model for agricultural extension agents are presented (RO1 and RO2). An empirical survey is incorporated, carried out in early 2011 (RO3), to examine three groups, totaling around 274 respondents. Data were collected through personal interviews using structured questionnaire forms. Path analysis technique was applied.

The authors propose a PM model consisting of five components. The five components are: agricultural extension agents’ characteristics, agents’ work attitudes, services provided, use of agricultural extension services and farmers’ satisfaction with agricultural extension services. The overall findings of the empirical surveys were found to validate the suggested causal relations among the components of the model. Findings indicate that 85 per cent of changes in farmers’ satisfaction with services are explained by changes in the preceding variables in the model.

It is, however, important to view this study with a few limitations in mind; for instance, using a survey method (e.g. sampling and the use of questionnaires in data collection); and the constraints associated with the model. That is to say that the components of the model could be further increased to incorporate other aspects of stakeholders, e.g. the economic impact of governmental financial policies on tax and the customs duties on agricultural products.

A Food and Agriculture Organization of the United Nations agricultural extension reference manual recommends certain purposes for a PM in agricultural extension organizations; interestingly, all these are already embedded in the proposed PM model, which makes it unequivocally a useful PM model for agriculture extension agents in agricultural extension organizations worldwide. Furthermore, the proposed model contributes significantly to agricultural extension practitioners and academics alike. It focuses the attention of agricultural extension organizations on the causal relationships among the model’s components. These components are linked to the agricultural extension organization strategies.

In addition to the practical implications above, the proposed PM model demonstrates the need for placing equal importance on all five components included and setting performance indicator (PI) targets.

The importance of this study emerges from the fact that it is helpful to examine the development and implementation of PM models across various disciplines to enhance understanding. The PM model overcomes the shortcomings in previous PM models of agricultural extension agents’ criteria/models in the agricultural extension literature. It is not merely a theoretically proposed model because the proposed causal relations amongst its variables are empirically investigated. Following management accounting and strategy theories, the authors propose that the relative importance of the attributes of PI in the proposed model differs according to each agricultural extension organization’s strategy, size and organizational structure.

The purpose of this paper is to speculate on the potentials of the system dynamics methodology to contribute to the balanced scorecard (BSC) design and implementation by producing a detailed causal relationships model that links strategic and operational objectives in a more accurate and effective way.

The work is based on the methodological principles and the operational tools provided by the system dynamics methodology and the BSC framework.

One of the main areas that both the relevant literature and Kaplan and Norton identified as critical in developing the original BSC framework is related to the identification and the quantification of “causal relationships” across the BSC, and to the subsequent opportunity to use a mathematical‐computer model to test and simulate such assumptions and their impacts on strategy implementation. Such issues are addressed in this paper.

The paper provides information and consideration on how to model and assess causality and cause‐and‐effect relationships in BSC environments. Subsequently, it provides some reflections on the contribution of the system dynamics methodology for the design and implementation of the BSC.

The value of this paper is two‐fold: first, it shows that relying on system dynamics tools and methodological principles is possible to better define the concept of causality in BSC frameworks; second, it shows that by integrating system dynamics modelling principles and the BSC framework it is possible to develop a comprehensive approach to performance management and strategy formulation and strategy implementation.