•A bibliometric analysis on Big Data and Business Intelligence from 1990 to 2016.•Big Data papers grow much faster than Business Intelligence papers•Computer Science and information systems are two ...core disciplines.•Most influential papers are identified and a research framework is proposed.
Business Intelligence that applies data analytics to generate key information to support business decision making, has been an important area for more than two decades. In the last five years, the trend of “Big Data” has emerged and become a core element of Business Intelligence research. In this article, we review academic literature associated with “Big Data” and “Business Intelligence” to explore the development and research trends. We use bibliometric methods to analyze publications from 1990 to 2017 in journals indexed in Science Citation Index Expanded (SCIE), Social Science Citation Index (SSCI) and Arts & Humanities Citation Index (AHCI). We map the time trend, disciplinary distribution, high-frequency keywords to show emerging topics. The findings indicate that Computer Science and management information systems are two core disciplines that drive research associated with Big Data and Business Intelligence. “Data mining”, “social media” and “information system” are high frequency keywords, but “cloud computing”, “data warehouse” and “knowledge management” are more emphasized after 2016.
The increased popularity of social networking sites, such as Linkedln, Facebook, and Twitter, has opened opportunities for new business models for electronic commerce, often referred to as social ...commerce. Social commerce involves using Web 2.0 social media technologies and infrastructure to support online interactions and user contributions to assist in the acquisition of products and services. Social media technologies not only provide a new platform for entrepreneurs to innovate but also raise a variety of new issues for e-commerce researchers that require the development of new theories. This could become one of the most challenging research arenas in the coming decade. The purpose of this introduction is to present a framework that integrates several elements in social commerce research and to summarize the papers included in this special issue. The framework includes six key elements for classifying social commerce research: research theme, social media, commercial activities, underlying theories, outcomes, and research methods. The proposed framework is valuable in defining the scope and identifying potential research issues in social commerce. We also explain how the papers included in this special issue fit into the proposed research framework.
Despite the publicity regarding big data and analytics (BDA), the success rate of these projects and strategic value created from them are unclear. Most literature on BDA focuses on how it can be ...used to enhance tactical organizational capabilities, but very few studies examine its impact on organizational value. Further, we see limited framing of how BDA can create strategic value for the organization. After all, the ultimate success of any BDA project lies in realizing strategic business value, which gives firms a competitive advantage. In this study, we describe the value proposition of BDA by delineating its components. We offer a framing of BDA value by extending existing frameworks of information technology value, then illustrate the framework through BDA applications in practice. The framework is then discussed in terms of its ability to study constructs and relationships that focus on BDA value creation and realization. We also present a problem-oriented view of the framework-where problems in BDA components can give rise to targeted research questions and areas for future study. The framing in this study could help develop a significant research agenda for BDA that can better target research and practice based on effective use of data resources.
•Direct phosphorus recovery from municipal wastewater.•Sequesters phosphorus at the beginning of wastewater treatment.•Obviates the need for biological phosphorus removal.•Phosphorus recovery without ...addition of Mg2+ and Ca2+.•Potential complete phosphorus recovery from municipal wastewater.
This work reports, for the first time, a new approach to direct phosphorus recovery from municipal wastewater via an osmotic membrane bioreactor (OMBR). In the OMBR, organic matter and NH4+ were removed by biological activities. PO43−, Ca2+, Mg2+ and unconverted NH4+ were rejected by the forward osmosis (FO) membrane and enriched within the bioreactor. The resultant phosphorus-rich supernatant was then used for phosphorus recovery. By adjusting the pH to 8.0–9.5, PO43− was recovered via precipitation with Ca2+, Mg2+ and NH4+. The OMBR showed up to 98% overall removal of TOC and NH4+-N. At pH 9.0, more than 95% PO43−-P was recovered without addition of magnesium and calcium. The precipitates were predominantly amorphous calcium phosphate (ACP) with phosphorus content >11.0%. In principal, this process can recover almost all the phosphorus, apart from the portion assimilated by bacteria. The global phosphorus recovery efficiency was shown to be 50% over 84days.
Protein corona formation is critical for the design of ideal and safe nanoparticles (NPs) for nanomedicine, biosensing, organ targeting, and other applications, but methods to quantitatively predict ...the formation of the protein corona, especially for functional compositions, remain unavailable. The traditional linear regression model performs poorly for the protein corona, as measured by R² (less than 0.40). Here, the performance with R² over 0.75 in the prediction of the protein corona was achieved by integrating a machine learning model and meta-analysis. NPs without modification and surface modification were identified as the two most important factors determining protein corona formation. According to experimental verification, the functional protein compositions (e.g., immune proteins, complement proteins, and apolipoproteins) in complex coronas were precisely predicted with good R² (most over 0.80). Moreover, the method successfully predicted the cellular recognition (e.g., cellular uptake by macrophages and cytokine release) mediated by functional corona proteins. This workflow provides a method to accurately and quantitatively predict the functional composition of the protein corona that determines cellular recognition and nanotoxicity to guide the synthesis and applications of a wide range of NPs by overcoming limitations and uncertainty.
•Salt accumulation caused significant succession of bacterial community.•High rejection of the FO membrane resulted in significant pollutant accumulation.•Significant succession occurred among ...species of Nitromonas.•Nitrospira was not evidently affected while Nitrobacter was washed out.•Denitrifying bacteria shifted from α- to γ-Proteobacteria members.
An osmotic membrane bioreactor was developed for wastewater treatment. The effects of salt accumulation on system performance and microbial community dynamics were investigated. Evident deterioration of biological activity, especially nitrification, was observed, which resulted in significant accumulation of organic matter and NH4+–N within the bioreactor. Arising from the elevation of salinity, almost all the dominant species was taken over by high salt-tolerant species. Significant succession among different species of Nitromonas was observed for ammonia-oxidizing bacteria. For nitrite-oxidizing bacteria, Nitrospira was not evidently affected, whereas Nitrobacter was eliminated from the system. Salt accumulation also caused significant shifts in denitrifying bacterial community from α- to γ-Proteobacteria members. Overall, the microbial community adapted to the elevated salinity conditions and brought about a rapid recovery of the biological activity. Membrane fouling occurred but was insignificant. Biofouling and inorganic scaling coexisted, with magnesium/calcium phosphate/carbonate compounds identified as the inorganic foulants.
Social commerce is emerging as an important platform in e-commerce, primarily due to the increased popularity of social networking sites such as Facebook, Linkedln, and Twitter. To understand the ...user's social sharing and social shopping intention in social networking Web sites, we conducted an empirical study on a popular microblog to investigate how social factors such as social support and relationship quality affect the user's intention of future participation in social commerce. The results indicate that both factors play a critical role. Social support and Web site quality positively influence the user's intention to use social commerce and to continue using a social networking site. These effects are found to be mediated by the quality of the relationship between the user and the social networking Web site. Our findings not only help researchers interpret why social commerce has become popular, but also assist practitioners in developing better social commerce strategy.
Given strong influences of online customer reviews on consumer purchase decisions, identifying helpful reviews has received broad attention from practitioners and researchers. The elaboration ...likelihood model (ELM) has been adopted to explain the review feature–helpfulness link. However, when analyzing reviews from websites, existing studies tend to ignore that quality indicators such as length and readability are merely cues and have not circumvented endogeneity induced by unseen argument quality. Hence, we propose an extended ELM application to observational data on review helpfulness. We develop a research model that integrates relevant quality indicators and sentiment features based on a circumplex model of affect. To test our hypotheses, we use publically available review datasets from three platforms (Amazon.com, Drugs.com, and Yelp.com) and adopt an instrument-free method that allows for arbitrary correlations between unseen argument quality and multiple endogenous indicators. Our analysis shows that ignoring endogeneity would result in invalid effect size and hypothesis-testing. In addition to identifying effects of endogenous quality indicators on review helpfulness, we find asymmetric effects of positive and negative valence contingent on low or high arousal. By articulating conceptual pitfalls and illustrating empirical remedies, our study aims to be a prototypical example of performing ELM-grounded analyses of online customer reviews.
•An extended elaboration likelihood model for online review helpfulness.•Empirically test the model using data sets from Amazon.com, Yelp.com, and Drugs.com.•An instrument-free approach to tackle endogeneity induced by unseen argument quality.•Show persistent effects of review quality indicators across samples and periods.•Show differential effects of review emotions in four valence-arousal mixes of a circumplex.
•Barriers to big data development in medical institutions were perceived.•A framework of medical big data barriers was constructed.•Solid suggestions toward the removal of barriers to big data ...implementation.
The computerized healthcare information system has undergone tremendous advancements in the previous two decades. Medical institutions are paying further attention to the replacement of traditional approaches that can no longer handle the increasing amount of patient data. In recent years, the healthcare information system based on big data has been growing rapidly and is being adapted to medical information to derive important health trends and support timely preventive care. This research aims to evaluate organization-driven barriers in implementing a healthcare information system based on big data. It adopts the analytic network process approach to determine the aspect weight and applies VlseKriterijumska Optimizacija I Kzompromisno Resenje (VIKOR) to conclude a highly appropriate strategy for overcoming such barriers. The proposed model can provide hospital managers with forecasts and implications that facilitate the withdrawal of organizational barriers when adopting the healthcare information system based on big data into their healthcare service system. Results can provide benefits for increasing the effectiveness and quality of the healthcare information system based on big data in the healthcare industry. Therefore, by understanding the sequence of the importance of resistance factors, managers can formulate efficient strategies to solve problems with appropriate priorities.
Blockchain technology has the promise of transforming security and trust in digital transactions. However, concerns about technical complexity and the benefits of deployment have blunted its ...adoption. We examine factors that influence managerial intention to adopt blockchain technology. We extend the fit-viability model (FVM) and develop a value-based technology adoption model through an empirical study of 242 managers mostly in medical and financial industries. Managers in such organizations are likely to consider fit and viability in adopting blockchain technology to store and protect data. Drawing upon Fit-Viability and Task-Technology Fit models, and the Unified Theory of Acceptance and Use of Technology (UTAUT), we test a model with Partial Least Squares (PLS) to assess managers' intention to adopt blockchain technology. Our findings indicate that functional and symbolic benefits have positive impact on managers' assessment of task-technology fit. Furthermore, viability is an important criterion in adopting blockchain technology.
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