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Despite the benefits of historical thinking pedagogy, many teachers continue to require students to read textbooks. Contained within textbook narratives are particular types of implied causation, asyndetic constructions, which may limit students’ abilities to fully comprehend certain textbook passages. This study examines how asyndetic constructions influence students’ comprehension of causal events. Twelve middle school readers were asked to read a US History textbook passage and answer questions related to the asyndetic construction. They also were asked to reason about their answers. Findings suggest that good middle school readers do not identify asyndetic constructions as problematic to their comprehension even though they often incorrectly answer questions related to these constructions. Findings also indicate that, when middle school readers recognize the asyndetic sentences as causally related, they often disregard and/or overlook the mental processes in the text that provide clues for explaining that relationship. Based on these findings, teachers need to recognize the complexity of textbook language and structure when assigning such readings, taking special care with poor readers who have fewer linguistic resources for making meaning of asyndetic constructions than good readers.

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.

Authors analyze a business model (BM) based on organizational performance. BMs are strategic tools used to achieve high performance. This study is based on two characteristics of causal complexity: conjunction and equifinality. Authors also examine the applicability of causal asymmetry in the relationship between BMs and organizational performance.

Generally, the relationship between BM elements and organizational performance is analyzed using a correlational approach. This relationship is marked by causal complexity, which cannot be analyzed via such approach. Authors applied a fuzzy-set qualitative comparative analysis with data from three time-periods and two performance variables for pharmaceutical firms.

Qualitative comparative analysis revealed that high performance resulted from configurations (combinations) of BM elements and not from the effects of individual elements. In addition, multiple configurations are available for achieving high performance. Causal asymmetry was observed in the configuration of the BM elements for high and low performances.

Using qualitative comparative analysis of data sets from three time-periods in the context of the pharmaceutical industry BM, authors integrated the theoretical constructs of causal complexity, namely conjunction, equifinality and causal asymmetry.

Findings related to conjunctions will help managers shift their focus from individual BM elements to combinations of BM elements. Additionally, the findings related to equifinality and causal asymmetry will allow flexibility in designing their company’s BM according to the resource constraints their company faces.

This was one of the first few studies on BMs using the twin indicators of the organizational performance relationship and causal complexity.

Empirical evidence suggests that many triggers influence poor cost performance in highway projects, whereas previous studies about the cost overrun triggers stem from a positivist standpoint supported by conventional statistical techniques, thus disregarding the sophisticated interactions and overall dynamics of the triggers. This study contends for a paradigm shift in investigating and understanding cost overrun triggers by adopting a holistic perspective through the lens of system thinking. This study aims to contend for a paradigm shift in investigating and understanding cost overrun triggers by adopting a holistic perspective through the lens of system thinking.

Semi-structured interviews with industry stakeholders in Nigeria were conducted buttressed by textual data from literature sources and project documents. Data analysis based on a developed data compatible coding framework and causal relations from textual data sources was used to develop a causal loop diagram depicting the interactions of the triggers which were validated by experts.

The analysis of the causal loop diagrams (CLDs) allowed identification of action points used to suggest changes for improved cost-effective highway project delivery. Among the suggested interventions are ensuring the provision of adequate funding prior to contract award will result in timely delivery of projects and indeed delivering key projects at the contractual agreed budget. This can be achieved through ensuring strict adherence to the provisions of Section 4 (2) (b) of the Nigerian Public Procurement Act, 2007 which stipulates that no contract should be awarded if funds are not available from the onset.

The study was limited to only highway infrastructure projects in Nigeria and as such caution must be taken before using the outcome of the study to other context within Nigeria and beyond.

From a practical point of view, the causal model demonstrates that this study is capable of being used to make pragmatic decisions regarding policy leverages about improving cost performance in highway projects provision in the Nigerian highway infrastructure sector of the construction industry. Moreover, it will aid a clear understanding of the key influencing triggers of cost overrun by the relevant stakeholders within the highway sector of the industry.

The hybrid-based approach applied in the development of CLDs in this study is expected to provide new insight into understanding the linkages, interactions, feedbacks and processes among the key cost overrun triggers and suggesting leverages for cost performance improvement within the philosophy of system thinking.

This paper examines the dynamic causal relationship between education and economic growth in South Africa using annual time-series data from 1986 to 2017. The study attempts to answer one critical question: Does education, which is one of the priority sectors in South Africa, drive economic growth?

The study uses the ARDL bounds testing approach and ECM-based Granger causality model to examine this linkage. The study also uses three proxies to measure the level of education. In addition, the study uses two variables: investment and labour, as intermittent variables between the various proxies of education and economic growth, thereby creating a system of multivariate Granger-causality models.

The study finds that the causal relationship between education and economic growth in South Africa is dependent on the variable used to measure the level of education. In addition, the causality tends to change over time. Overall, the study finds the causal flow from economic growth to education to supersede the causal flow from education to economic growth.

Unlike some previous studies, the current study uses three proxies of education in South Africa and two intermittent variables in a multivariate setting. To our knowledge, this may be the first study of its kind to examine in detail the dynamic causal relationship between education and economic growth in South Africa – using the ARDL bounds testing approach and a multivariate Granger causality model.

There has been little research discussing the role that new product creativity plays in achieving alliance ambidexterity and how causal ambiguity may be a potential moderator in the association between new product creativity and alliance ambidexterity. The purpose of this study is to identify the contingent role of causal ambiguity and examine the relationships between new product creativity, causal ambiguity and alliance ambidexterity.

In this empirical study, a questionnaire approach is used to collect data, and moderated structural equation modeling is used to test the hypotheses.

The findings indicate that new product creativity has a positive influence on alliance ambidexterity, whereas causal ambiguity moderates these associations. When causal ambiguity increases, the positive impact of new product creativity on alliance ambidexterity is attenuated.

This study integrates the ambidexterity literature and the alliance literature by applying the concept of ambidexterity to a strategic alliance context. The moderating role of causal ambiguity echoes previous research postulating causal ambiguity as a barrier to knowledge transfer in alliances. Given a higher level of causal ambiguity, firms face more difficulty when they attempt to develop new product creativity to achieve the desired level of alliance ambidexterity.

Managers could initiate incentive programs and build supportive environments that facilitate new product creativity. Firms will be capable of elaborating the potential of new product creativity to further facilitate alliance ambidexterity. Managers need to pay attention to causal ambiguity that may have a moderating influence on the relationship between new product creativity and alliance ambidexterity.

This study contributes to the synthesis of new product creativity and alliance ambidexterity and helps scholars and managers to better understand the moderating effect of causal ambiguity in the context of the innovation and ambidexterity literature.

The purpose of this paper is to investigate dynamics of causal interactions and financial risk contagion among BRICS stock markets under rare events.

Two methods are adopted: The new causal inference technique, namely, the Liang causality analysis based on information flow theory and the dynamic causal index (DCI) are used to measure the financial risk contagion.

The causal relationships among the BRICS stock markets estimated by the Liang causality analysis are significantly stronger in the mid-periods of rare events than in the pre- and post-periods. Moreover, different rare events have heterogeneous effects on the causal relationships. Notably, under rare events, there is almost no significant Liang's causality between the Chinese and other four stock markets, except for a few moments, indicating that the former can provide a relatively safe haven within the BRICS. According to the DCIs, the causal linkages have significantly increased during rare events, implying that their connectivity becomes stronger under extreme conditions.

The obtained results not only provide important implications for investors to reasonably allocate regional financial assets, but also yield some suggestions for policymakers and financial regulators in effective supervision, especially in extreme environments.

This paper uses the Liang causality analysis to construct the causal networks among BRICS stock indices and characterize their causal linkages. Furthermore, the DCI derived from the causal networks is applied to measure the financial risk contagion of the BRICS countries under three rare events.

Most companies rarely work on sustainable development as a whole, which includes environmental, social, governance, and economic pillars. The purpose of this paper is to explore causal relationships between pillar scores and overall score of sustainability and identify the most critical pillar to which policy makers should allot limited resources with the highest priority.

Based on Thomson Reuters ASSET4 database of global corporate sustainability, this paper examines the causal relations between pillar scores and overall score of sustainability by using the three-stage integrative methodology consisting of cluster analysis, data mining, and partial least square path modeling.

This paper finds that each pillar has unequal effects on the overall corporate sustainability and that the overall score is affected by not only the direct effects from pillar scores but also the indirect effects from the causal interrelations among pillars. Moreover, the patterns of causal directions and the most critical pillar are sensitive to industries. Social performance is the most critical pillar for the majority of industries, followed by environmental performance, and economic performance, respectively. The governance performance, however, is not the most critical pillar in any industry.

To construct a roadmap for reform priorities, policy makers should follow the top-down approach which involves hierarchical decisions. Using the three-stage methodology, the policy makers first decide on the most critical pillar score before selecting the most critical category score underneath.

Relaxing traditional assumptions of simple average overall score of corporate sustainability, the three-stage integrative framework allows for causal interrelations among pillars and different weights on individual pillars.

Constructing and evaluating behavioral science models is a complex process. Decisions must be made about which variables to include, which variables are related to each other, the functional forms of the relationships, and so on. The last 10 years have seen a substantial extension of the range of statistical tools available for use in the construction process. The progress in tool development has been accompanied by the publication of handbooks that introduce the methods in general terms (Arminger et al., 1995; Tinsley & Brown, 2000a). Each chapter in these handbooks cites a wide range of books and articles on specific analysis topics.

The purpose of this paper is to examine the causal relationships between stock prices and macroeconomic variables in Sri Lanka, in order to examine the validity of the semi‐strong form of the efficient market hypothesis.

The paper adopts unit roots and cointegration, error‐correction modelling, variance decomposition analysis, and impulse responses analysis to examine the causal relationship between six macroeconomic variables.

The results indicate that there are both short and long‐run causal relationships between stock prices and macroeconomic variables. These findings refute the validity of the semi‐strong version of the efficient market hypothesis for the Sri Lankan share market and have implications for investors, both domestic and international.

The paper addresses several methodological weaknesses in relation to unit root and cointegration tests which previous studies in the area of the paper have overlooked. Further, it uses more variables than those used in a previous study using Sri Lankan data.