Structural and Temporal Topic Models of Feedbacks on Service Quality – A Path to Theory Development?

There is growing interest in applying computational methods in analysing large amount of data without sacrificing rigour in Information Systems research. In this paper, we demonstrate how the use of structural and temporal topic modelling can be employed to produce insights of both theoretical and practical importance from the analysis of textual comments on the quality of services in hospitals. As a first step, we revealed the thematic structures in the comments as topics which were aligned with the SERVQUAL dimensions. Following this, we established the temporal precedence among SERVQUAL factors based on the evolution of the topics over time. Theoretically, our findings were consistent with the emerging consensus on the nature of SERVQUAL dimensions from extant quantitative research and offer new propositions on the relationships among these dimensions. From the practice perspective, we produced quantified measures of factors associated with healthcare service experienc

All I do is win, win, win no matter what? Pre-game anxiety and experience predict athletic performance in the NBA

In this study, we examine the relationship between anxiety and athletic performance, measuring pre-game anxiety in a corpus of 12,228 tweets of 81 National Basketball Association (NBA) players using an anxiety inference algorithm, and match this data with certified NBA individual player game performance data. We found a positive relationship between pre-game anxiety and athletic performance, which was moderated by both player experience and minutes played on the court. This paper serves to demonstrate the use case for using machine learning to label publicly available micro-blogs of players which can be used to form important discrete emotions, such as pre-game anxiety, which in turn can predict athletic performance in elite sports. Based on the results, we discuss these findings and outline recommendations for athletes, teams, team leaders, coaches, and managers.info:eu-repo/semantics/publishedVersio

Developing and Harnessing Software Technology in the South: The Roles of China, India, Brazil, and South Africa

Software technology is gaining prominence in national information technology (IT) strategies due to its huge potential for socioeconomic development, particularly through the support it provides in the productive sectors of the economy, delivery of public services and engagement of citizens. In growing numbers of developing countries, software technology is also being leveraged for income generation from digital services and products. For instance, in recent years, India, Chile, the Philippines, Brazil, China, and Indonesia have emerged as important global players in the offshore software services industry, with India and China standing out as leaders. Cooperation between developing countries (south-south) in the area of software technology has also been growing; particularly in the application of software technology to agriculture, public administration and governance (e-governance), transportation and the society (knowledge society). The paper presents the current state of software technology in the south and specifically, the maturity of the software industries in China, India, Brazil, and South Africa (CIBS). It establishes profiles of different regions based on the level of education, quality of research and availability of e-infrastructure and e-applications for determining the potential of these regions in terms of growth and competitiveness in the global software industry. Further complementary analysis of country profiles produced country clusters, helping to identify potential collaboration scenarios for advancing software capacity in the south. Finally, the paper discusses how CIBS can pivot regional or inter-regional cooperation in software technology in the south.software technology, software industry, south-south cooperation, China, Brazil, India, South Africa

Open Data Capability Architecture - An Interpretive Structural Modeling Approach

Despite of increasing availability of open data as a vital organizational resource, large numbers of start-ups and organizations fail when it comes to utilizing open data effectively. This shortcoming is attributable to the poor understanding of what types of capabilities are required to successfully conduct data related activities. At the same time, research on open data capabilities and how they relate to one another remains sparse. Guided by extant literature, interviews of these organizations, and drawn from Interpretive Structural Modeling (ISM) approach which are pair comparison methods to evolve hierarchical relationships among a set of elements to convert unclear and unstructured mental models of systems into well-articulated models that act as base for conceptualization and theory building, this study explores open data capabilities and the relationships and the structure of the dependencies among these areas. Findings from this study reveal hitherto unknown knowledge regarding how the capability areas relate one another in these organizations. From the practical standpoint, the resulting architecture has the potential to transform capability management practices in open data organizations towards greater competitiveness through more flexibility and increased value generation. From the research point of you, this paper motivates theory development in this discipline

Qualitative Structural Model for Capabilities in Open Data Organizations

Open data is increasingly becoming an essential asset for many organizations. However, large numbers of organizations fall short when it comes to utilizing open data effectively to fully leverage the potential of it. There are ample evidences that this shortcoming is attributable to the poor understanding of what types of capabilities are required to successfully conduct data related activities. At the same time, research on open data capabilities and how they relate to one another remains sparse. Based on the theoretical foundation constructed from the integration of Capability-based Theory and Dynamic Capability Theory and, extant literature and interviews of leadership of open data organizations, we attempt to address this knowledge gap by investigating open data capabilities and relationships between them. Findings help validate the two theories in the open data organizations and reveal unknown knowledge about open data capability areas and how they affect one another

DESIGNING NEXT GENERATION SMART CITY INITIATIVES - HARNESSING FINDINGS AND LESSONS FROM A STUDY OF TEN SMART CITY PROGRAMS

The proliferation of Smart Cities initiatives around the world is part of the strategic response by governments to the challenges and opportunities of increasing urbanization and the rise of cities as the nexus of societal development. As a framework for urban transformation, Smart City initiatives aim to harness Information and Communication Technologies and Knowledge Infrastructures for economic regeneration, social cohesion, better city administration and infrastructure management. However, experiences from earlier Smart City initiatives have revealed several technical, management and governance challenges arising from the inherent nature of a Smart City as a complex Socio-technical System of Systems . While these early lessons are informing modest objectives for planned Smart Cities programs, no rigorous developed framework based on careful analysis of existing initiatives is available to guide policymakers, practitioners, and other Smart City stakeholders. In response to this need, this paper presents a Smart City Initiative Design (SCID) Framework grounded in the findings from the analysis of ten major Smart Cities programs from Netherlands, Sweden, Malta, United Arab Emirates, Portugal, Singapore, Brazil, South Korea, China and Japan. The findings provide a design space for the objectives, implementation options, strategies, and the enabling institutional and governance mechanisms for Smart City initiatives

Studies on the enantioselectivity of bacterial lactonases

Owing to their biological significance, increased efforts are being directed towards the production of lactones in optically pure form. One method of synthesis is via a microbial equivalent of the Baeyer-Villiger reaction. This reaction is common in microorganisms possessing a monooxygenase enzyme which is induced as part of the catabolic machinery responsible for the assimilation of cyclic alcohols or ketones. The stoichiometric requirement for NADPH as cofactor, however, makes this enzyme uneconomical for implementation on an industrial scale. Microorganisms in which the monooxygenase enzyme have been induced also possess a lactone hydrolase (lactonase) enzyme which is responsible for the hydrolysis of formed lactones to hydroxy acids. It is the aim of this project to investigate the potential for the use of these lactonases for the optical resolution of racemic lactones in a bid to produce the latter in optically pure form. Three inducible lactonases; 8-valerolactone hydrolase from Pseudomonas NCIMB 9872 grown on cyclopentanol, e-caprolactone hydolase from Acinetobacter NCIMB 9871 grown on cyclohexanol and s-caprolactone hydrolase from Rhodococcus coprophilus WT1 grown on cyclohexanol were selected for study. Growth studies, conducted in order to optimise lactonase production in each of the bacteria, revealed that in all three microorganisms, lactonase activity was induced throughout growth. High speed supernatants bearing the enzyme of interest were tested with a range of y- (5-membered ring), 8- (6-membered ring) and s- (7-membered ring) lactones. These lactonases only showed activity towards (8- and s-) lactones. Enantioselectivity studies using 8-decanolactone as substrate showed 8-valerolactone hydrolase from Pseudomonas NCIMB 9872 to have the greatest enantioselectivity with a %enantiomeric excess after 60% transformation for residual lactone of greater than 98%. The lactonases from Acinetobacter NCIMB 9871 and R. coprophilus WT1 only showed modest enantioselectivity towards this substrate with 6% and 15% enantiomeric excess after 60% transformation for residual substrate respectively. 8-Valerolactone hydrolase was purified from high speed supernatant fractions of Pseudomonas NCIMB 9872 using a two step purification procedure involving anion exchange chromatography and hydrophobic interaction chromatography. This enzyme is a monomer with molecular weight of approximately 28kda and an isoelectric point of about 4.5. Kinetic studies indicate that this enzyme obeys classical Michaelis-Menten kinetics, has a pH optimum of about 7.5 and a temperature optimum between 28 and 30°C. In addition the first 15 amino acid residues starting at the NH2 terminus are reported. Inhibitor studies suggest that this enzyme does not depend on sulfhydryl groups for its activity. However, there is a metal ion dependency since EDTA and citrate inhibited lactonase activity

Using Isolation Forest and Alternative Data Products to Overcome Ground Truth Data Scarcity for Improved Deep Learning-based Agricultural Land Use Classification Models

High-quality labelled datasets represent a cornerstone in the development of deep learning models for land use classification. The high cost of data collection, the inherent errors introduced during data mapping efforts, the lack of local knowledge, and the spatial variability of the data hinder the development of accurate and spatially-transferable deep learning models in the context of agriculture. In this paper, we investigate the use of Isolation Forest (IF), an anomaly detection algorithm, to reduce noise in a large-scale, low-resolution alternative ground truth dataset used to train land use deep learning models. We use a modest-size, high-resolution and high-fidelity manually collected ground-truth dataset to calibrate Isolation Forest parameters and evaluate our approach, highlighting the relatively low cost of the methodology. Our data-centric methodology demonstrates the efficacy of deep learning methods coupled with IF to create mid-resolution land-use models and map products for agriculture using an alternative ground-truth dataset. Moreover, we compare our deep learning approach with a traditional algorithm used in remote sensing and evaluate the spatial transferability of the created models. Finally, we reflect upon the lessons learnt and future work

Applying Design Patterns in URI Strategies - Naming in Linked Geospatial Data Infrastructure

The centrality of Uniform Resource Identifiers (URI) as names in Linked Data initiatives has led to the development of guidelines and best practices by World Wide Web Consortium (W3C) and other experts groups on how to design "good" URIs in general and for the government domain in particular. However, these URI design guidelines have had limited pragmatic value for several reasons including the under specified nature of the rules, weak elaboration on nature of problems addressed and consequences of prescribed design decisions. With no conceptual or rigorous underpinning for existing design rules, checking for internal consistency or coverage when developing URI strategies is difficult. We tackle these problems in this paper by: 1) consolidating existing URI design rules, 2) distilling core URI design aspects or facets from these rules and 3) abstracting the rules into a set of consistent URI Design Patterns specifications. This process resulted in 8 Design Patterns from an initial set of 37 URI design rules. Following this, we show how the design patterns could be employed in developing a URI strategy to support the realization of a Linked Spatial Data Infrastructure. We conclude with an evaluation of the URI design patterns and implications of our work

VR-Participation: On the feasibility of next-gen Virtual Reality technologies as Participation channel

While progress in the development of e-Participation platforms has been significant and the emergence of new Social Media-driven platforms appears to bring significant (by quantity), citizen engagement, little attention has been paid by researchers to the limitations of the pervasive textual communication for political participation. In this paper, we describe the major sociotechnical challenges of classic e-Participation solutions and how the emerging next-gen Virtual Reality (VR) technologies can be leveraged to alleviate some of the issues identified