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In developing countries, how risk management technologies influence management accounting and control (MAC) practices is under-researched. By drawing on insights from institutional studies, this study aims to examine the multiple institutional pressures surrounding an entity and influencing its risk-based management control (RBC) system – that is, how RBC appears in an emerging market attributed to institutional multiplicity.

The authors used qualitative case study research methods to collect empirical evidence from a privately owned Egyptian insurance company.

The authors observed that in the transformation to risk-based controls, especially in socio-political settings such as Egypt, changes in MAC systems were consistent with the shifts in the institutional context. Along with changes in the institutional environment, the case company sought to configure its MAC system to be more risk-based to achieve its strategic goals effectively and maintain its sustainability.

This research provides a fuller view of risk-based management controls based on the social, professional and political perspectives central to the examined institutional environment. Moreover, unlike early studies that reported resistance to RBC, this case reveals the institutional dynamics contributing to the successful implementation of RBC in an emerging market.

This paper aims to propose a system for generating actionable knowledge from Big Data and use this system to construct a comprehensive knowledge base (KB), called GrandBase.

In particular, this study extracts new predicates from four types of data sources, namely, Web texts, Document Object Model (DOM) trees, existing KBs and query stream to augment the ontology of the existing KB (i.e. Freebase). In addition, a graph-based approach to conduct better truth discovery for multi-valued predicates is also proposed.

Empirical studies demonstrate the effectiveness of the approaches presented in this study and the potential of GrandBase. The future research directions regarding GrandBase construction and extension has also been discussed.

To revolutionize our modern society by using the wisdom of Big Data, considerable KBs have been constructed to feed the massive knowledge-driven applications with Resource Description Framework triples. The important challenges for KB construction include extracting information from large-scale, possibly conflicting and different-structured data sources (i.e. the knowledge extraction problem) and reconciling the conflicts that reside in the sources (i.e. the truth discovery problem). Tremendous research efforts have been contributed on both problems. However, the existing KBs are far from being comprehensive and accurate: first, existing knowledge extraction systems retrieve data from limited types of Web sources; second, existing truth discovery approaches commonly assume each predicate has only one true value. In this paper, the focus is on the problem of generating actionable knowledge from Big Data. A system is proposed, which consists of two phases, namely, knowledge extraction and truth discovery, to construct a broader KB, called GrandBase.

Korea and China have promoted a bilateral FTA since 2005 to upgrade their economic relationships. If the Korea-China FTA is concluded, then trade between the two countries is likely to involve substantial changes both quantitatively and qualitatively. Offer lists submitted by Korea include steel, petrochemicals and machinery and those by China include some agricultural and marine products as well as nonferrous metals. Korea's interest in the Korea-China FTA has focused mainly on damage to the agricultural sector and there have been a lot of studies on the effects of the FTA on Korean agricultural sector. However, little is known about why China includes the nonferrous metals industry for early voluntary liberalization and its implications for the domestic economy. Nonferrous metals industry is one of China’s national strategic sectors and has a large supply excess in the country. This study targets the survey of Chinese nonferrous metals industry and trade structure and considers the problem of nonferrous metals in the context of negotiations for the Korea-China FTA and its implications for a higher-quality Korea-China FTA.

The link between supply chain agility (SCA) and performance has been tested in previous research with different samples and results. The present paper quantitatively analyses and summarises the impact of SCA on performance found in previous empirical papers and determines the influence of several identified moderators.

Using a meta-analysis approach based on a systematic literature review, a total of 63 empirical papers comprising a sample of 14,469 firms were meta-analysed to consider substantive (type of performance and SCA operationalisation) and extrinsic (economic region and industry) moderators.

Results confirm a significantly large, positive correlation between SCA and performance. None of the analysed moderators has enabled the identification of any significant differences between the SCA and performance correlations by subgroup. However, high heterogeneity in total variance, both in the full sample and the subgroups by moderator, demands further rigorously reported empirical research on this topic with clearly conceptualised variables and frameworks and the use of validated scales.

Several research gaps and best practice recommendations have been indicated to improve future empirical research on this topic.

Practitioners in different economic regions and industries will find consistent evidence of improvements in performance through SCA.

No meta-analysis has been found in previous research to estimate the value of the correlation between SCA and performance and the influence of moderating variables.

To determine how to best deploy the Triple-A supply chain (SC) capabilities (AAA-agility, adaptability and alignment) to improve competitive advantage (CA) by identifying the Triple-A SC model with the highest CA predictive capability.

Assessment of in-sample and out-of-sample predictive capacity of Triple-A-CA models (considering AAA as individual constructs) to find which has the highest CA predictive capacity. BIC, BIC-Akaike weights and PLSpredict are used in a multi-country, multi-informant, multi-sector 304 plant sample.

Greater direct relationship model (DRM) in-sample and out-of-sample CA predictive capacity suggests DRM's greater likelihood of achieving a higher CA predictive capacity than mediated relationship model (MRM). So, DRM can be considered a benchmark for research/practice and the Triple-A SC capabilities as independent levers of performance/CA.

DRM emerges as a reference for analysing how to trigger the three Triple-A SC levers for better performance/CA predictive capacity. Therefore, MRM proposals should be compared to DRM to determine whether their performance is significantly better considering the study's aim.

Results with our sample justify how managers can suitably deploy the Triple-A SC capabilities to improve CA by implementing AAA as independent levers. Single capability deployment does not require levels to be reached in others.

First research considering Triple-A SC capability deployment to better improve performance/CA focusing on model's predictive capability (essential for decision-making), further highlighting the lack of theory and contrasted models for Lee's Triple-A framework.

This study aims to examine the mediating effect of four antecedents of competitive advantage on the linkage of risky strategy to firm performance, measured by revenue dynamics. It considers the roots of competitive advantage to highlight different patterns and foundations of achieving superior performance. It investigates whether pursuing a risky strategy fosters revenue dynamics growth and whether different mediators are included in that relationship.

Path analysis (structural equation modeling) method is used to analyze data from 122 companies of various sizes and industries. All respondents were responsible for executing strategic management processes. The paper used the subjective perspective, which is based on the individual opinion of senior company managers and owners.

The authors find a positive relationship between risky strategy and firm performance, but no evidence of a mediating role of competitive advantage and dynamic growth in this relationship. Competitive advantage should be perceived as a set of integrated factors that can be analyzed from an aggregated perspective. Integrating all antecedents requires a holistic and systematic approach and the development of a particular mindset. Aggregated competitive advantage is related to setting dynamic growth as a priority. However, no relationship between risky strategy and achieving competitive advantage, or between implementing a risky strategy and setting dynamic growth as a priority, is observed, which was assumed to explain the revenue dynamics growth.

Secondary data should be analyzed to explore how risky strategies are manifested, and which managerial decisions are reflected in high-level risk. A multidimensional scale could be developed to check how risk shapes the constructs’ interdependence. Therefore, the dynamic capabilities approach could be further expanded.

This research offers insights into the short-term relationship between risky strategy and revenue dynamics, although competitive advantage does not mediate that relationship. Special attention should be paid to the selected antecedents of competitive advantage, as they influence dynamic growth.

This work provides insights into different antecedents of competitive advantage, which is not necessarily based on making risky decisions, and into factors that facilitate firm performance measured by revenue dynamics.