Actualizing big data analytics affordances: A revelatory case study
واقعی سازی هزینه های تحلیلی داده های بزرگ: یک مطالعه موردی الهامی-2020
Drawing on a revelatory case study, we identify four big data analytics (BDA) actualization mechanisms: (1) enhancing, (2) constructing, (3) coordinating, and (4) integrating, which manifest in actions on three sociotechnical system levels, i.e., the structure, actor, and technology levels. We investigate the actualization of four BDA affordances at an automotive manufacturing company, i.e., establishing customer-centric marketing, provisioning vehicle-data-driven services, data-driven vehicle developing, and optimizing production processes. This study introduces a theoretical perspective to BDA research that explains how organizational actions contribute to actualizing BDA affordances. We further provide practical implications that can help guide practitioners in BDA adoption.
Keywords: Big data analytics | Affordance theory | Socio-technical approach | Organizational transformation | Organizational benefits | Affordance actualization
Factors influencing effective use of big data: A research framework
عوامل مؤثر بر استفاده مؤثر از داده های بزرگ: چارچوب تحقیقی-2020
Information systems (IS) research has explored “effective use” in a variety of contexts. However, it is yet to specifically consider it in the context of the unique characteristics of big data. Yet, organizations have a high appetite for big data, and there is growing evidence that investments in big data solutions do not always lead to the derivation of intended value. Accordingly, there is a need for rigorous academic guidance on what factors enable effective use of big data. With this paper, we aim to guide IS researchers such that the expansion of the body of knowledge on the effective use of big data can proceed in a structured and systematic manner and can subsequently lead to empirically driven guidance for organizations. Namely, with this paper, we cast a wide net to understand and consolidate from literature the potential factors that can influence the effective use of big data, so they may be further studied. To do so, we first conduct a systematic literature review. Our review identifies 41 factors, which we categorize into 7 themes, namely data quality; data privacy and security and governance; perceived organizational benefit; process management; people aspects; systems, tools, and technologies; and organizational aspects. To explore the existence of these themes in practice, we then analyze 45 published case studies that document insights into how specific companies use big data successfully. Finally, we propose a framework for the study of effective use of big data as a basis for future research. Our contributions aim to guide researchers in establishing the relevance and relationships within the identified themes and factors and are a step toward developing a deeper understanding of effective use of big data.
Keywords: Big data | Effective use | Factors | Framework
Leveraging internet of things and big data analytics initiatives in European and American firms: Is data quality a way to extract business value?
اعمال اینترنت اشیاء و ابتکارات تحلیلی داده های بزرگ در شرکت های اروپایی و آمریکایی: آیا کیفیت داده راهی برای استخراج ارزش تجارت است؟-2020
Big data analytics (BDA) and the Internet of Things (IoT) tools are considered crucial investments for firms to distinguish themselves among competitors. Drawing on a strategic management perspective, this study proposes that BDA and IoT capabilities can create significant value in business processes if supported by a good level of data quality, which will lead to a better competitive advantage. Responses are collected from 618 European and American firms that use IoT and BDA applications. Partial least squares results reveal that better data quality is needed to unlock the value of IoT and BDA capabilities.
Keywords: Big data analytics | Internet of things | Strategic management | Knowledge-based theory | Dynamics capability theory
Business value of big data analytics: A systems-theoretic approach and empirical test
ارزش تجاری تجزیه و تحلیل داده های بزرگ: یک رویکرد سیستم-تئوری و آزمون تجربی-2020
Although big data analytics have been widely considered a key driver of marketing and innovation processes, whether and how big data analytics create business value has not been fully understood and empirically validated at a large scale. Taking social media analytics as an example, this paper is among the first attempts to theoretically explain and empirically test the market performance impact of big data analytics. Drawing on the systems theory, we explain how and why social media analytics create super-additive value through the synergies in functional complementarity between social media diversity for gathering big data from diverse social media channels and big data analytics for analyzing the gathered big data. Furthermore, we deepen our theorizing by considering the difference between small and medium enterprises (SMEs) and large firms in the required integration effort that enables the synergies of social media diversity and big data analytics. In line with this theorizing, we empirically test the synergistic effect of social media diversity and big data analytics by using a recent large-scale survey data set from 18,816 firms in Italy. We find that social media diversity and big data analytics have a positive interaction effect on market performance, which is more salient for SMEs than for large firms.
Keywords: Big data analytics | Social media analytics | Synergies | Business value of information technology | Market performance | Digital innovation
Manufacturing big data ecosystem: A systematic literature review
ساخت اکوسیستم داده های بزرگ: مروری بر ادبیات سیستماتیک-2020
Advanced manufacturing is one of the core national strategies in the US (AMP), Germany (Industry 4.0) and China (Made-in China 2025). The emergence of the concept of Cyber Physical System (CPS) and big data imperatively enable manufacturing to become smarter and more competitive among nations. Many researchers have proposed new solutions with big data enabling tools for manufacturing applications in three directions: product, production and business. Big data has been a fast-changing research area with many new opportunities for applications in manufacturing. This paper presents a systematic literature review of the state-of-the-art of big data in manufacturing. Six key drivers of big data applications in manufacturing have been identified. The key drivers are system integration, data, prediction, sustainability, resource sharing and hardware. Based on the requirements of manufacturing, nine essential components of big data ecosystem are captured. They are data ingestion, storage, computing, analytics, visualization, management, workflow, infrastructure and security. Several research domains are identified that are driven by available capabilities of big data ecosystem. Five future directions of big data applications in manufacturing are presented from modelling and simulation to realtime big data analytics and cybersecurity.
Keywords: Smart manufacturing | Big data | Cloud computing | Cloud manufacturing | Internet of things | NoSQL
Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues
تولید هوشمند جفت محور دیجیتال : هوشمند سازی ، مدل مرجع ، برنامه ها و موضوعات تحقیق-2020
This paper reviews the recent development of Digital Twin technologies in manufacturing systems and processes, to analyze the connotation, application scenarios, and research issues of Digital Twin-driven smart manufacturing in the context of Industry 4.0. To understand Digital Twin and its future potential in manufacturing, we summarized the definition and state-of-the-art development outcomes of Digital Twin. Existing technologies for developing a Digital Twin for smart manufacturing are reviewed under a Digital Twin reference model to systematize the development methodology for Digital Twin. Representative applications are reviewed with a focus on the alignment with the proposed reference model. Outstanding research issues of developing Digital Twins for smart manufacturing are identified at the end of the paper.
Keywords: Smart manufacturing | Digital Twin | Industry 4.0 | Cyber-physical System | Big Data | Standard
Does citizen coproduction lead to better urban services in smart cities projects? An empirical study on e-participation in a mobile big data platform
آیا مشارکت شهروندان منجر به خدمات بهتر شهری در پروژه های شهرهای هوشمند می شود؟ یک مطالعه تجربی در مورد مشارکت الکترونیکی در یک بستر بزرگ داده همراه-2020
With contemporary development of digital technology and smart cities initiatives, citizen co-production has created a new government-citizen interface. However, it remains inconclusive whether such citizen-government collaboration has achieved the fundamental goal of improving service quality for citizens. In this research, we tested the relationship between e-participation as a form of co-production and service performance, using multiple large longitudinal datasets from a smart city mobile platform. The results of the analysis show that citizen e-participation, in providing service feedback, is positively associated with the clearance rate of urban service requests in subdistrict service units, after controlling for various factors. We also found that the effect size of e-participation on service performance varies between different types of city services. E-participation has a stronger relative influence on complex problems that may involve multiple agencies, than with simple routine services
Keywords: Citizen-sourcing | Smart cities | E-government | Urban service | Open government | Big data | Digital transformation
Technological frames in public administration: What do public managers think of big data?
چارچوب های فن آوری در مدیریت عمومی: مدیران دولتی درباره داده های بزرگ چه فکر می کنند؟-2020
Being among the largest creators and gatherers of data in many countries, public administrations are looking for ways to harness big data technology. However, the de facto uses of big data in the public sector remain very limited. Despite numerous studies aiming to clarify the term big data, for many public managers, it remains unclear what this technology does and does not offer public administration. Using the concept of technological frames, we explore the assumptions, expectations, and understandings that public managers possess in order to interpret and make sense of big data. We identify nine big data frames, ranging from inward-oriented technoenthusiasts to outward-oriented techno-skeptics, each of which characterizes public managers specific viewpoints relating to the introduction of big data in public administrations. Our findings highlight inconsistencies between different perceptions and reveal widespread skepticism among public managers, helping better understand why the de facto uses of big data in the public sector remain very limited.
Keywords: Big data | Technological frame | Public manager | Public administration | Q methodology
Adaptive data and verified message disjoint security routing for gathering big data in energy harvesting networks
داده تطبیقی و تایید امنیت پیام متلاشی شدن مسیریابی برای جمع آوری داده های بزرگ در شبکه های برداشت انرژی-2020
To improve the data arrival ratio and the transmission delay and considering that the capacity for determining malicious nodes and energy are limited, a security disjoint routing-based verified message (SDRVM) scheme is proposed. The main contributions of SDRVM are as follows: (a) two connected dominating sets (a data CDS and a v-message CDS) are created for disseminating data and verified messages (v-messages), respectively, based on the remaining energy of nodes. (b) Nodes record the ID information in data packets with a specified probability, namely, the marking probability, which is adjusted according to the remaining energy of the nodes. (c) The duty cycle of the nodes is adjusted, and the energy of the nodes is divided into three levels. In the data CDS, the duty cycle of the sensor nodes is the longest and the duty cycle of the nodes that do not belong to either of the CDSs is the shortest. (d) If the energy of the sensor nodes is sufficient, data packets are transmitted several times and the v-messages that are stored in the nodes are transmitted to the destination nodes. The proposed scheme has been evaluated using different parameters where the results obtained prove its effectiveness in comparison to the existing solutions.
Keywords: Energy harvesting networks | Security | Disjoint routing | Marking probability | Network lifetime
DQPFS: Distributed quadratic programming based feature selection for big data
DQPFS: انتخاب ویژگی های مبتنی بر برنامه نویسی درجه دوم برای داده های بزرگ-2020
With the advent of the Big data, the scalability of the machine learning algorithms has become more crucial than ever before. Furthermore, Feature selection as an essential preprocessing technique can improve the performance of the learning algorithms in confront with large-scale dataset by removing the irrelevant and redundant features. Owing to the lack of scalability, most of the classical feature selection algorithms are not so proper to deal with the voluminous data in the Big Data era. QPFS is a traditional feature weighting algorithm that has been used in lots of feature selection applications. By inspiring the classical QPFS, in this paper, a scalable algorithm called DQPFS is proposed based on the novel Apache Spark cluster computing model. The experimental study is performed on three big datasets that have a large number of instances and features at the same time. Then some assessment criteria such as accuracy, execution time, speed-up and scale-out are figured. Moreover, to study more deeply, the results of the proposed algorithm are compared with the classical version QPFS and the DiRelief, a distributed feature selection algorithm proposed recently. The empirical results illustrate that proposed method has (a) better scale-out than DiRelief, (b) significantly lower execution time than DiRelief, (c) lower execution time than QPFS, (d) better accuracy of the Naïve Bayes classifier in two of three datasets than DiRelief.
Keywords: Big data | Apache Spark | Feature selection | Feature ranking | Quadratic programming