Actualizing big data analytics affordances: A revelatory case study

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 socio-technical 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.     

The impact of big data analytics on firms’ high value business performance

Big Data Analytics (BDA) is an emerging phenomenon with the reported potential to transform how firms manage and enhance high value businesses performance. The purpose of our study is to investigate the impact of BDA on operations management in the manufacturing sector, which is an acknowledged infrequently researched context. Using an interpretive qualitative approach, this empirical study leverages a comparative case study of three manufacturing companies with varying levels of BDA usage (experimental, moderate and heavy). The information technology (IT) business value literature and a resource based view informed the development of our research propositions and the conceptual framework that illuminated the relationships between BDA capability and organizational readiness and design. Our findings indicate that BDA capability (in terms of data sourcing, access, integration, and delivery, analytical capabilities, and people’s expertise) along with organizational readiness and design factors (such as BDA strategy, top management support, financial resources, and employee engagement) facilitated better utilization of BDA in manufacturing decision making, and thus enhanced high value business performance. Our results also highlight important managerial implications related to the impact of BDA on empowerment of employees, and how BDA can be integrated into organizations to augment rather than replace management capabilities. Our research will be of benefit to academics and practitioners in further aiding our understanding of BDA utilization in transforming operations and production management. It adds to the body of limited empirically based knowledge by highlighting the real business value resulting from applying BDA in manufacturing firms and thus encouraging beneficial economic societal changes.     

System 1 and System 2 cognition in the decision to adopt and use a new technology

Most models of technology adoption and use assume a rational decision maker engaged in thoughtful deliberate consideration of the new technology. However, recent research in psychology concludes that such deliberate, rational, conscious decision-making (termed System 2 cognition) has less influence on behavior than originally thought; nonconscious automatic cognition (termed System 1 cognition), which is often influenced by personality characteristics and pattern matching based on past experience, also plays a key role in most decisions. As users adopt and use new technologies time and time again, a set of general expectations about new technology adoption begins to emerge. A user’s personality combined with this pattern of positive and negative experiences creates System 1 heuristics that are triggered when a user faces a similar decision in the future. The focus of this paper is to examine the extent to which the predispositions produced by System 1 automatic cognition – both enabling and inhibiting – versus the deliberate technology assessment produced by System 2 cognition influence technology adoption and use. We found that enabling predispositions influences the formation of intentions to use a new technology, and both enabling and inhibiting predispositions influence an individual’s ultimate follow through in acting on his or her intentions and actually using new technologies. Our research suggests that concepts previously seen as “determinants” of technology adoption and use (e.g., performance expectancy, effort expectancy, social influence, and facilitating conditions) are not really determinants but rather are important partial mediators in a larger nomological network that includes both automatic System 1 cognition and deliberate System 2 cognition.     

Big Data Analytics in Telecommunications: Literature Review and Architecture Recommendations

This paper focuses on facilitating state-of-the-art applications of big data analytics(BDA) architectures and infrastructures to telecommunications(telecom) industrial sector.Telecom companies are dealing with terabytes to petabytes of data on a daily basis. Io T applications in telecom are further contributing to this data deluge. Recent advances in BDA have exposed new opportunities to get actionable insights from telecom big data. These benefits and the fast-changing BDA technology landscape make it important to investigate existing BDA applications to telecom sector. For this, we initially determine published research on BDA applications to telecom through a systematic literature review through which we filter 38 articles and categorize them in frameworks, use cases, literature reviews, white papers and experimental validations. We also discuss the benefits and challenges mentioned in these articles. We find that experiments are all proof of concepts(POC) on a severely limited BDA technology stack(as compared to the available technology stack), i.e.,we did not find any work focusing on full-fledged BDA implementation in an operational telecom environment. To facilitate these applications at research-level, we propose a state-of-the-art lambda architecture for BDA pipeline implementation(called Lambda Tel) based completely on open source BDA technologies and the standard Python language, along with relevant guidelines.We discovered only one research paper which presented a relatively-limited lambda architecture using the proprietary AWS cloud infrastructure. We believe Lambda Tel presents a clear roadmap for telecom industry practitioners to implement and enhance BDA applications in their enterprises.     

Big data analytics in E-commerce: a systematic review and agenda for future research

There has been an increasing emphasis on big data analytics (BDA) in e-commerce in recent years. However, it remains poorly-explored as a concept, which obstructs its theoretical and practical development. This position paper explores BDA in e-commerce by drawing on a systematic review of the literature. The paper presents an interpretive framework that explores the definitional aspects, distinctive characteristics, types, business value and challenges of BDA in the e-commerce landscape. The paper also triggers broader discussions regarding future research challenges and opportunities in theory and practice. Overall, the findings of the study synthesize diverse BDA concepts (e.g., definition of big data, types, nature, business value and relevant theories) that provide deeper insights along the cross-cutting analytics applications in e-commerce.     

Distinguishing the adoption of business intelligence systems from their implementation: the role of managers’ personality profiles

For decades, the marketing guidelines of enterprise software providers have focused on those managers who are likely to be more innovative in adopting new information systems. The current study argues that this approach demands improvements for two reasons: (1) this tactic may be biased, since past studies have only examined the single trait of innovativeness and its impact on an individual adoption intention and (2) the organisational implementation intention might be more important than the individual adoption intention, but the former has been largely ignored in the existing literature. Based on the case of business intelligence (BI) systems and data from 62 senior managers, this study is a pioneer in that it empirically reveals that managers’ individual adoption intention is distinct from their organisational implementation intention. Further, while managers’ innovativeness may be a significant determinant of their individual adoption intention towards BI systems, the issue of whether managers actually implement BI systems in their organisations is dominated by their involvement characteristics. Fruitful suggestions are proposed for practitioners and scholars.     

Big Data Analytics in Healthcare — A Systematic Literature Review and Roadmap for Practical Implementation

The advent of healthcare information management systems (HIMSs) continues to produce large volumes of healthcare data for patient care and compliance and regulatory requirements at a global scale. Analysis of this big data allows for boundless potential outcomes for discovering knowledge. Big data analytics (BDA) in healthcare can, for instance, help determine causes of diseases, generate effective diagnoses, enhance QoS guarantees by increasing efficiency of the healthcare delivery and effectiveness and viability of treatments, generate accurate predictions of readmissions, enhance clinical care, and pinpoint opportunities for cost savings. However, BDA implementations in any domain are generally complicated and resource-intensive with a high failure rate and no roadmap or success strategies to guide the practitioners. In this paper, we present a comprehensive roadmap to derive insights from BDA in the healthcare (patient care) domain, based on the results of a systematic literature review. We initially determine big data characteristics for healthcare and then review BDA applications to healthcare in academic research focusing particularly on NoSQL databases. We also identify the limitations and challenges of these applications and justify the potential of NoSQL databases to address these challenges and further enhance BDA healthcare research. We then propose and describe a state-of-the-art BDA architecture called Med-BDA for healthcare domain which solves all current BDA challenges and is based on the latest zeta big data paradigm. We also present success strategies to ensure the working of Med-BDA along with outlining the major benefits of BDA applications to healthcare. Finally, we compare our work with other related literature reviews across twelve hallmark features to justify the novelty and importance of our work. The aforementioned contributions of our work are collectively unique and clearly present a roadmap for clinical administrators, practitioners and professionals to successfully implement BDA initiatives in their organizations.      

Leveraging internet of things and big data analytics initiatives in European and American firms: Is data quality a way to extract business value?

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.     

Big data analytics capabilities: a systematic literature review and research agenda

With big data growing rapidly in importance over the past few years, academics and practitioners have been considering the means through which they can incorporate the shifts these technologies bring into their competitive strategies. To date, emphasis has been on the technical aspects of big data, with limited attention paid to the organizational changes they entail and how they should be leveraged strategically. As with any novel technology, it is important to understand the mechanisms and processes through which big data can add business value to companies, and to have a clear picture of the different elements and their interdependencies. To this end, the present paper aims to provide a systematic literature review that can help to explain the mechanisms through which big data analytics (BDA) lead to competitive performance gains. The research framework is grounded on past empirical work on IT business value research, and builds on the resource-based view and dynamic capabilities view of the firm. By identifying the main areas of focus for BDA and explaining the mechanisms through which they should be leveraged, this paper attempts to add to literature on how big data should be examined as a source of competitive advantage. To this end, we identify gaps in the extant literature and propose six future research themes.     

A preliminary study of ecommerce adoption in developing countries

What factors underlie the adoption dynamics of ecommerce technologies among users in developing countries? Even though the internet promised to be the great equalizer, the nuanced variety of conditions and contingencies that shape user adoption of ecommerce technologies has received little scrutiny. Building on previous research on technology adoption, the paper proposes a global information technology (IT) adoption model. The model includes antecedents of performance expectancy, social influence, and technology opportunism and investigates the crucial influence of facilitating conditions. The proposed model is tested using data from 172 technology users from 37 countries, collected over a 1‐year period. The findings suggest that in developing countries, facilitating conditions play a critical moderating role in understanding actual ecommerce adoption, especially when in tandem with technological opportunism. Altogether, the paper offers a preliminary scrutiny of the mechanics of ecommerce adoption in developing countries.     

Orchestrating big data analytics capability for sustainability: A study of air pollution management in China

Under rapid urbanization, cities are facing many societal challenges that impede sustainability. Big data analytics (BDA) gives cities unprecedented potential to address these issues. As BDA is still a new concept, there is limited knowledge on how to apply BDA in a sustainability context. Thus, this study investigates a case using BDA for sustainability, adopting the resource orchestration perspective. A process model is generated, which provides novel insights into three aspects: data resource orchestration, BDA capability development, and big data value creation. This study benefits both researchers and practitioners by contributing to theoretical developments as well as by providing practical insights.     

Critical factors of WAP services adoption: an empirical study

Mobile commerce is becoming increasingly important in business. Wireless application protocol (WAP) is one of the most widespread technical standards for mobile commerce. Following continuous technical evolution, WAP has now included various new features. However, WAP services continue to struggle for market share. Hence, understanding the adoption of WAP services is increasingly important for enterprises interested in developing mobile commerce. This study aims to: (i) identify the critical factors of WAP services adoption; (ii) explore the relative importance of each factor between WAP adopters and non-adopters; and (iii) examine the causal relationships among variables on WAP services adoption behavior. This study reports empirical test of the adoption of WAP services in Taiwan, based on the theory of planned behavior and innovation diffusion theory. The results indicate that the critical factors influencing the adoption of WAP services include connection speed, service costs, user satisfaction, personal innovativeness, ease of use, peer influence, and facilitating conditions. Some suggestions for subsequent researchers and practitioners seeking to understand WAP services adoption behavior are also provided.     

基于置信规则库推理的装备战损等级评定

针对复杂战场环境下战损数据的多源性和不确定性, 本文根据战损等级评定的非线性特点, 提出了一种基于置信规则库(belief rule base, BRB)和证据推理(evidential reasoning, ER)的装备战损等级评定方法. 首先, 在战损等级评定影响因素分析的基础上, 建立了一种新的融合多种特征信息的BRB-ER战损等级评定模型; 其次, 为解决传统专家系统中初始BRB参数不准确的问题, 利用局部粒子群算法对模型初始参数进行优化, 从而提高战损等级评定的准确性; 最后, 以某战损试验为例, 对基于置信规则库推理的装备战损等级评定方法进行了验证和对比. 结果表明, 该方法用于战时装备战损等级评定具有较高的准确度和可靠性, 从而为装备保障指挥员进行战场维修决策提供辅助支持.     

A survey study of the current IS usage in the Chinese manufacturing industry

The status of information systems (IS) usage is an important measure of the level of IS adoption. This paper presents the results of a study investigating the current usage of IS and the attitudes toward their use in the Chinese manufacturing industry. Using the survey results obtained from a sample of 78 firms, we provide findings in the current status of IS applications, orientation of IS applications, human factors, and future IS plans. This effort also demonstrates an underlying relationship between critical IS factors (especially the human factors) and business performance in the Chinese environment. The paper concludes with a discussion of IS issues and their implications for practitioners.     

Exploring factors influencing mobile users' intention to adopt multimedia messaging service

While short messaging service (SMS) is discussed often in recent literature, multimedia messaging service (MMS), a media rich successor of SMS, is seldom heard or understood by mobile users in Taiwan. The adoption rates of MMS are far from satisfactory, implying that there might be some factors keeping the potential users away from using MMS. This research integrates a qualitative method, the Zaltman Metaphor Elicitation Technique, with the quantitative questionnaire survey to elicit and validate underlying factors which influence potential users' attitude and intention toward the adoption of MMS. A research model together with eight hypotheses was formulated, and a questionnaire based survey was administrated to mobile users knowledgeable about mobile messaging services for empirically validating the research model and testing the hypotheses. Our research findings show that relative advantage and ease of use are important factors significantly influencing mobile users' adoption of MMS but the other two antecedents, facilitating conditions and previous experience, do not have significant and direct impacts on mobile users' intention to use MMS. These study results can be referenced by service providers for designing and developing successful business applications to catch the valuable opportunity and benefit of MMS.     

Understanding the Factors Affecting the Organizational Adoption of Big Data

Big data is rapidly becoming a major driver for firms seeking to gain a competitive advantage. Grounded in the Diffusion of Innovation theory (DOI), the institutional theory, and the Tech-nology–Organization–Environment (TOE) framework, this study applies the results of a content analysis to develop a framework to identify the main factors affecting the organizational adoption of big data. The content analysis is based on the retrieval and review of relevant papers in the business intelligence & analytics (BI&A) literature published during the period 2009–2015. The 26 factors identified by this review are then integrated into a TOE framework. The findings of this research enrich the current big data literature and enhance practitioners’ understanding of the decision-making processes involved in a firm’s adoption of big data.     

Technology adoption in employee recruitment: The case of social media in Central and Eastern Europe

Social technologies are increasingly used in several organizational functions, including human resource management. The focus of this study is on social media adoption in employee recruitment and selection in Central and Eastern Europe (CEE). The theoretical lens used in this study is the Unified Theory of Acceptance and Use of Technology (UTAUT), extended to include the impact of the recruiters' managerial position and educational level on technology adoption. Results obtained from partial least squares path modeling, using cross-sectional data collected from 224 recruiters, showed that the core hypotheses of the UTAUT were supported, namely the positive impact of performance expectancy, effort expectancy and social influence on behavioral intention, as well as the positive impact of facilitating conditions and behavioral intention on usage behavior. As expected, the recruiters' characteristics triggered many interaction effects, but none of them impacted facilitating conditions. Managerial implications are discussed and future research suggestions are provided.     

An investigation of enablers and inhibitors of crowdfunding adoption: Empirical evidence from startups in China

In recent years, crowdfunding has emerged as a new fundraising technique for startup ventures. Crowdfunding allows small businesses and startups to seek funding from a large number of private investors, the so‐called “crowdfunders.” Crowdfunding is beneficial for startup ventures by offering a variety of financial and nonfinancial benefits such as helping to solve funding difficulties, providing value‐added involvement, facilitating access to further funding, and providing publicity. In contrast, diffusion processes of new business innovations have become increasingly complicatedly and multifaceted in recent years. Potential users today are exposed to a wide range of influences that include word‐of‐mouth communications, network externalities, and social signals. Despite the advantages associated with crowdfunding, the crowdfunding industry faces slow rates of diffusion in the Chinese market because crowdfunding ventures often fail to translate into adoption behavior. This study investigates the enablers and inhibitors of crowdfunding adoption intention from the perspective of startups by employing the two‐factor theory, status quo bias theory, and innovation diffusion theory. These theories allow crowdfunding researchers and platform managers to test the relative influence of both reasons for (enablers) and reasons against (inhibitors) crowdfunding adoption in a single framework. In conclusion, considering the phenomenon of startups in China, the results of this study will fill the paucity of empirical research in the innovation resistance area, shedding light on the motivation of entrepreneurs to avoid crowdfunding as a financing course as well as providing crowdfunding managers and policymakers with strategic recommendations.     

Exploring factors influencing consumers’ behavioral intention to adopt broadband in Malaysia

The purpose of this paper is to examine various normative, attitudinal and control factors influencing consumers’ intention to adopt broadband internet in a developing country such as Malaysia. This study is based on empirical data collected using a self-administered questionnaire relating to the normative, attitudinal and control variables. Structural equation modeling analysis is conducted to test the role of numerous variables on consumers’ behavioral intentions to adopt broadband internet in Malaysia. Results revealed that Primary Influences (PI), Relative Advantage (RA), Hedonic Outcomes (HO), Facilitating Conditions Resources (FCR), and Self-Efficacy (SE) are positively associated with consumers’ Behavioral Intention (BI) to adopt broadband in Malaysia. The significance of this research study is twofold. Practically, this research study provide some useful guidelines to industry players, be it the internet service providers (ISPs) or the policy makers, to understand which are the factors (primary influences, self-efficacy, relative advantage, hedonic outcomes and facilitating conditions resources) that can have an influence on consumers’ intention to adopt broadband technology. It was found that primary influences, self-efficacy and relative advantage are the key determinants that influences consumers’ broadband adoption. From here, practitioners could take into consideration our findings when revising and restructuring their marketing strategy. Theoretically, the research framework used in this study is an extension from the past research models used (i.e. TPB, DOI and MATH). With the newly integrated framework, a greater level of comprehension can be attained with regards to the broadband acceptance among the Malaysian consumers.     

The promising future of healthcare services: When big data analytics meets wearable technology

As policy-makers and business practitioners across the globe expend extraordinary effort toward the field of e-health, the thriving development of healthcare-wearable technology is creating great opportunities and posing a remarkable future for healthcare services. This paper employs a game theory model to investigate the dynamics of wearable device market. We extend the two-dimensional product differentiation model by incorporating consumer diversity, consumer density, and firms’ big data analytics (BDA) investment strategy. Our model reveals that with differentiated consumer densities firms are more likely to engage in quality competition and the firm that invests in BDA can achieve higher profits. Furthermore, the overall quality of biomedical and healthcare services can be improved under various market conditions. Our findings provide practical guidance to wearable device manufacturers on optimizing competition strategies and offer insights to social planners on potential policy-making to promote better healthcare services.