Meta-synthesis of big data impacts on information systems development

Rouhani, Saeed; Rotbei, Sayna; Shamizanjani, Mehdi

doi:10.1080/23270012.2016.1265906

Cited by 8 publications

(2 citation statements)

References 26 publications

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“…An elaborate study of unsafe behavior appears to be essential in promoting the safety of system and engineering designs of industries as a prerequisite for reducing potential error opportunities and also cognitive failures [22]. Metasynthesis can be considered as a promising tool for a better understanding of employees' behavior at workplaces considering the advantages of applying qualitative studies in extracting influential factors affecting a certain phenomenon as a problem and using its new knowledge in decisionmaking practically [23].…”

Section: Introductionmentioning

confidence: 99%

Interactions among Cognitive Factors Affecting Unsafe Behavior: Integrative Fuzzy DEMATEL ISM Approach

Shakerian

Choobineh

Jahangiri

et al. 2020

Mathematical Problems in Engineering

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The prevention of industrial accidents is not completely practical by implementing safety programs unless focusing on protecting vulnerable workers. The unsafe behavior cognitive factors (UBCFs) are essential determinants contributing to human failure. This study aimed at eliciting the most important UBCFs, along with investigating hierarchical cause-effect interactions among them. A qualitative approach using metasynthesis was utilized to extract all essential UBCFs among industrial workplaces. Afterward, the relationships between UBCFs were recognized using the fuzzy decision-making trial and evaluation laboratory (DEMATEL) method and rated by an expert panel. Also, a hierarchical model was developed based on the final matrix of DEMATEL by employing the interpretive structural modeling (ISM) method. Ten criteria were extracted as UBCFs through the metasynthesis method. The threshold value was set as 0.175 in DEMATEL following experts’ ideas. Inadequacy of persons’ resilience and habitual rule ignorance were recognized as the most important predictive UBCFs. The developed model was tested through a case study in a petrochemical company. The results of the study can be used to help industrial managers and HSE practitioners to consider workers’ capabilities either cognitively or physically when designing the required tasks to reduce unsafe behaviors. Also, the findings of the study are applicable for other researchers to prioritize the most important factors affecting unsafe behavior in different workplaces.

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Section: Introductionmentioning

confidence: 99%

Interactions among Cognitive Factors Affecting Unsafe Behavior: Integrative Fuzzy DEMATEL ISM Approach

Shakerian

Choobineh

Jahangiri

et al. 2020

Mathematical Problems in Engineering

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“…Rouhani and co-authors [46] investigated the effects of Big Data on the design and development of information systems by a systematic literature review. Their findings indicated that the volume attribute of Big Data affects software engineering tools, while the variety and velocity attributes affect software engineering methods, indicating that both of these components of software engineering will need to be adapted in order to support Big Data projects.…”

Section: Related Researchmentioning

confidence: 99%

Big Data and software engineering: prospects for mutual enrichment

Arndt

2017

Iran J Comput Sci

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Software engineering has evolved over the last 50 years, initially as a response to the so-called software crisis (the problems that organizations had producing quality software systems on time and on budget) of the 1960s and 1970s. Software engineering (SE) has been defined as "the application of a systematic, disciplined, quantifiable approach to the development, operation, and maintenance of software; that is, the application of engineering to software". Software engineering has developed a number of approaches to areas such as software requirements, software design, software testing, and software maintenance. Software development processes such as the waterfall model, incremental development, and the spiral model have been successfully applied to produce high-quality software on time and under budget. More recently, agile software development has gained popularity as an alternative to the more traditional development methods for development of complex systems. Within the last decade or so, advances in technologies such as mobile computing, social networks, cloud computing, and the Internet of things have given rise to massive datasets which have been given the name Big Data (BD). Big Data has been defined as data with 3Vs-high volume, velocity, and variety. Big Data contains so much data that low probability events are captured in the data. These events can be discovered using analytics methods and turned into actionable intelligence which can be used by businesses to gain a competitive advantage. Unfortunately, the very scale of BD often renders inadequate SQL-based relational database systems which have formed the backbone of data intensive systems for the last 30 years, requiring new NoSQL technologies to be effective. In this paper, we will explore how well-established SE technology can be adapted to support successful development of BD projects, as well as how BD techniques can be used to increase the utility of SE processes and techniques. Thus, BD and SE may mutually support and enrich each other.

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