Narrow Results

When to apply a new technology in an organization is a critical decision for every software development organization. Earlier work defines a set of methods that the research community uses when a new technology is developed. This chapter presents a discussion of the set of methods that industrial organizations use before adopting a new technology. First there is a brief definition of the earlier research methods and then a definition of the set of industrial methods. A survey taken by experts from both the research and industrial communities provides insights into how these communities differ in their approach toward technology innovation and technology transfer.

Radio frequency identification (RFID) and wireless sensor networks (WSN) are two important wireless technologies that have a wide variety of applications and provide limitless future potentials. RFID facilitates detection and identification of objects that are not easily detectable or distinguishable by using current sensor technologies. However, it does not provide information about the condition of the objects it detects. Sensors, on the other hand, provide information about the condition of the objects as well as the environment. Hence, integration of these technologies will expand their overall functionality and capacity. This chapter first presents a brief introduction on RFID and then investigates recent research works, new patents, academic products and applications that integrate RFID with sensor networks. Four types of integration are discussed: (1) integrating tags with sensors; (2) integrating tags with wireless sensor nodes and wireless devices; (3) integrating readers with wireless sensor nodes and wireless devices; and (4) mix of RFID and wireless sensor networks. New challenges and future works are discussed at the end.

Acupuncture has been a popular alternative medicine in the United States for several decades. Its therapeutic effects on pain have been validated by both basic and clinical researches, and it is currently emerging as a unique non-pharmaceutical choice for pain against opioid crisis. However, the full spectrum of acupuncture indications remains unexplored. In this study, we conducted a cross-sectional survey among 419 acupuncturists nation-wide to investigate the top 10 and top 99 acupuncture indications in private clinics in the United States. We found the top 10 indications to be: lower back pain, depression, anxiety, headache, arthritis, allergies, general pain, female infertility, insomnia, neck pain and frozen shoulder. Among the top 99 indications, pain represents the largest category; and mental health management, especially for mood disorders, is in greatest demand. The following popular groups are: immune system dysfunctions, gastrointestinal diseases, gynecology and neurology. In addition, specialty index, commonality index, and the potential to become medical specialties were estimated for each indication. Demographic analysis suggests that China trained acupuncturists tend to have broader indication spectrums, but the top conditions treated are primarily decided by local needs. Also, gender, resident states, age and clinical experience all affect indication distributions. Our data for the first time outlines the profile of acupuncture treatable conditions in the US and is valuable for strategic planning in acupuncture training, healthcare administration and public education.

Hybrid entrepreneurs (HEs) represent a considerable share of all entrepreneurial activity. Yet little is known about the phenomenon. In this study we examine the differences between transitory HEs, who expect to make the transition to full-time entrepreneurship, and persistent HEs, who view their part-time status as permanent. With data collected from 848 academic HEs we find that only a small minority considers full self-employment likely in the near future and that self-fulfillment is the most significant motive for entrepreneurial activities. The results suggest that persistent hybrid entrepreneurship should be viewed as a form of entrepreneurship in its own right, and that even partial entrepreneurship has the potential to lengthen careers and improve wellbeing at work. Hybrid entrepreneurship offers the entrepreneurially inclined employees the best of both worlds.

Service firms need to continuously innovate their service offerings in order to remain competitive in constantly changing market conditions. Successful innovators utilise current information technology (IT) to access service innovation capacity and knowledge which can be located internal or external to their organisation. In this paper, we develop and test a theoretical framework that explains how IT can contribute to service innovation performance, and finally, to service provision performance. Drawing on dynamic capability theory, we differentiate between sensing (SN), seizing (SZ), and transformation (TF) as the key abilities in service innovation. With our theoretical model, we can explain almost 40% of the variance in service innovation performance (SIP) (R² = 0.3955) and provide evidence for the multi-faceted and significant effects of IT. In particular, we are able to show a significant influence of inter-organisational IT support on SN and SZ abilities while organisational IT support has a significant impact on TF abilities.

Enterprise Application Integration (EAI) is an important factor in the success of the enterprise application as standalone applications cannot contribute to the success of the increasing business demand of today. There has been a steady growth in the number of research conducted in this field; as such, there is a need to review the research in this field. This paper conducts an extensive review on 44 journal publications in the field of EAI from 1992 to 2015 available in Science Citation Index (SCI) and Social Science Citation Index (SSCI) database. The aim of this paper is to present scholars and practitioners with a detailed overview of the available research in the field of EAI. The selected papers have been grouped into 13 categories. The contents of papers in each categories are summarized and future research direction for each category is outlined. This overview indicates that the research in EAI that has spanned from late 90’s till date, requires more effort in developing new methodologies and framework to integrate enterprise applications that are the need of today’s growing business that venture into new technologies like cloud computing and Internet of Things (IoT).

Recent shifts in technology have created a data-rich environment and made it necessary for firms to develop new capabilities to cope with these changes. To address this challenge, this study introduces marketing-enabled data analytics capability, a specific type of information technology (IT) capability that enables firms to better understand customer needs and achieve a competitive advantage in the digital era. Using empirical results collected via online survey, we argue that marketing-enabled data analytics capability, which comprises data analytics infrastructure, marketing-oriented analytics expertise, and IT–marketing social capital, positively influences a firm’s organisational agility and innovation success. Moreover, the results show that organisational agility partially mediates the relationship between marketing-enabled data analytics capability and innovation success. By developing the construct of marketing-enabled data analytics capability, this paper lays a foundation for future research on this new type of IT capability, which is critical in the digitization process.

Since the first appearance of ‘open innovation’ as a theoretical concept in 2003, the debates on its essence still continue among academics, while its interpretations within the business community also seem to differ from one company to another. Using a survey of 251 companies operating in Europe, in this research, we compare the perceptions of open innovation that exist within both the academic and business worlds, to assist in the conceptual development of the phenomenon. Our research reveals a mismatch in these perceptions, as only a few activities counted as ‘open’ by innovation scholars appear to affect companies’ self-reported state of open innovation implementation. Moreover, our research has shown differences in the interpretation of open innovation among companies of different sizes. Only free revealing, acknowledged by scholars as one of the open innovation practices, has been recognised as such a practice by all the studied firms. This paper contributes to conceptualisation of ‘open innovation’ and shares practical insights on bridging academia and business perceptions of it.

The relationship between budget deficits and macroeconomic variables (such as growth, interest rates, trade deficit, exchange rate, among others) represents one of the most widely debated topics among economists and policy makers in both developed and developing countries. However, the purpose of this paper is to review the extensive literature to such a relationship, concentrating on theoretical debates and empirical studies, in order to derive substantive conclusions, which can be beneficial in the macroeconomics area; policy analysis; or in terms of constructing or developing a macroeconomic model for analyzing the impact of budget deficits on macroeconomic variables. The majority of these studies regress a macroeconomic variable on the deficit variable. These studies are cross-country and utilize time series data. In general the key outcomes from the studies presented in this paper indicated that both the method of financing and the components of government expenditures could have different effects. Therefore, it is crucial for the government to distinguish between consumption and investment expenditures especially when the government is in the process of evaluating the impact of fiscal policy on private investment and output growth or in the process of cutting expenditures to reduce the fiscal imbalances in the country. Even though the overall results from the empirical literature with respect to the impact of public investment on private investment and growth are ambiguous, the bulk of the empirical studies find a significantly negative effect of public consumption expenditure on growth, while the effects of public investment expenditure (such as on education, healthcare) are found to be positive although less robust. The key findings from these studies is important in particular for developing countries to be aware of the importance of government investment expenditures in the area of education, healthcare, infrastructure to long-term economic growth and the benefits from which are an important contributor to welfare and well-being. The key outcome from all of the studies presented in this paper while investigating the relationship between the budget deficit and current account deficit showed strong evidence in both developed and developing countries towards supporting the Keynesian proposition (conventional view) which suggests that an increase in the budget deficit would induce domestic absorption and, hence import expansion, causing a current account deficit. The key findings from the empirical studies investigating the relationship between the budget deficit and interest rates indicated strong evidence towards supporting the Keynesian model of a significant and positive relationship between budget deficits and interest rates. The major outcomes from the empirical studies examining the relationship between budget deficits and inflation showed strong evidence that the budget deficit financed through monetization and a rising money supply could lead to inflation.

Runtime adaptive systems are able to dynamically transform their internal structure, and hence their behavior, in response to internal or external changes. Such transformations provide the basis for new functionalities or improvements of the non-functional properties that match operational requirements and standards. Software Product Line Engineering (SPLE) has introduced several models and mechanisms for variability modeling and management. Dynamic software product lines (DSPL) engineering exploits the knowledge acquired in SPLE to develop systems that can be context-aware, post-deployment reconfigurable, or runtime adaptive. This paper focuses on DSPL engineering approaches for developing runtime adaptive systems and proposes a framework for classifying and comparing these approaches from two distinct perspectives: adaptation properties and adaptation realization. These two perspectives are linked together by a series of guidelines that help to select a suitable adaptation realization approach based on desired adaptation types.

In the era of rapid growth of technologies, image matting plays a key role in image and video editing along with image composition. In many significant real-world applications such as film production, it has been widely used for visual effects, virtual zoom, image translation, image editing and video editing. With recent advancements in digital cameras, both professionals and consumers have become increasingly involved in matting techniques to facilitate image editing activities. Image matting plays an important role to estimate alpha matte in the unknown region to distinguish foreground from the background region of an image using an input image and the corresponding trimap of an image which represents a foreground and unknown region. Numerous image matting techniques have been proposed recently to extract high-quality matte from image and video sequences. This paper illustrates a systematic overview of the current image and video matting techniques mostly emphasis on the current and advanced algorithms proposed recently. In general, image matting techniques have been categorized according to their underlying approaches, namely, sampling-based, propagation-based, combination of sampling and propagation-based and deep learning-based algorithms. The traditional image matting algorithms depend primarily on color information to predict alpha matte such as sampling-based, propagation-based or combination of sampling and propagation-based algorithms. However, these techniques mostly use low-level features and suffer from high-level background which tends to produce unwanted artifacts when color is same or semi-transparent in the foreground object. Image matting techniques based on deep learning have recently introduced to address the shortcomings of traditional algorithms. Rather than simply depending on the color information, it uses deep learning mechanism to estimate the alpha matte using an input image and the trimap of an image. A comprehensive survey on recent image matting algorithms and in-depth comparative analysis of these algorithms has been thoroughly discussed in this paper.

The information-based prediction models using machine learning techniques have gained massive popularity during the last few decades. Such models have been applied in a number of domains such as medical diagnosis, crime prediction, movies rating, etc. Similar is the trend in telecom industry where prediction models have been applied to predict the dissatisfied customers who are likely to change the service provider. Due to immense financial cost of customer churn in telecom, the companies from all over the world have analyzed various factors (such as call cost, call quality, customer service response time, etc.) using several learners such as decision trees, support vector machines, neural networks, probabilistic models such as Bayes, etc. This paper presents a detailed survey of models from 2000 to 2015 describing the datasets used in churn prediction, impacting features in those datasets and classifiers that are used to implement prediction model. A total of 48 studies related to churn prediction in telecom industry are discussed using 23 datasets (3 public and 20 private). Our survey aims to highlight the evolution of techniques from simple features/learners to more complex learners and feature engineering or sampling techniques. We also give an overview of the current challenges in churn prediction and suggest solutions to resolve them. This paper will allow researchers such as data analysts in general and telecom operators in particular to choose best suited techniques and features to prepare their churn prediction models.

The aim of this paper is to conduct an empirical study to explore the influence of size category on innovation incorporation in Irish SMEs. Changes in markets and in large organisation strategies have resulted in the need for SMEs to re-examine and modify their competitive strategies to increase innovation. Although there is some literature on innovation incorporation in SMEs, there is a lack of direct studies on this issue, with a reliance on related but indirect studies such as Reengineering and New product development in SMEs. Furthermore, there is a paucity of studies and data on the innovation incorporation in different organisational size categories within SMEs.

This study uses a grounded SME innovation model developed from earlier studies as the basis of a questionnaire survey of 2086 SMEs in Northern Ireland. The results indicate that SME organisational size categories has a significant effect on innovation incorporation in relation to leadership, people and culture, TQM/CI, product and process and Knowledge and information management.

Enterprise Architecture (EA) is principles, methods and models that are used in the design and realization of an enterprise’s organizational structure, business processes, information systems and IT infrastructure. There has been a steady growth in the number of research conducted in this field, however there is a need to consolidate the focus of the research in this field. This paper conducts an extensive review on 177 journal publications in the field of EA from 1974 to 2016 available in Science Citation Index (SCI) and Social Science Citation Index (SSCI) database. The aim of this paper is to present scholars and practitioners with a detailed overview on the available research in the field of EA. The selected papers have been grouped into 27 categories. The contents of papers in each category are summarized and future research direction for each categories are outlined. This overview indicates that the research in EA that has spanned from late 90’s till date, requires more concentrated effort in terms of developing new methodologies and framework to integrate enterprise applications that are the need of today’s growing business that venture into new technologies like SOA, frameworks, modeling and healthcare.

An effective Facility Management (FM) is essential to maintain old buildings so ensure that they remain sturcturally sound, and able to support the funcationality to the inhabitants using them. However, to date, there is comprehensive study on the subject, this paper aims to review the previous papers on facility management performance evaluation and construct evaluation framework as a first step of strategic management by identifying critical success factors (CSF) and key performance indicators (KPI). The evaluation framework could be employed for cornerstone of strategic management on FM. Moreover, the identified CSF and KPI are surveyed in order to offer an empirical approach for strategic management in FM domain. The survey results indicate that reliability of service, timely responsiveness to emergency, tenants’ safety, customer satisfaction and work execution control are the most critical in successful FM performance. In addition, training is essentential to strengthen the monitoring; training for emergency situation, education and training for service mindset, regular meeting with tenants and safe inspection and patrol are the most important factors in enhancing the level of performance in the office building FM practice. Finally, the survey results on CSF and KPI on Korean office buildings are addressed in strategic perspective in FM, thus suggesting future research.

The redundancy is a widely spread technology of building computing systems that continue to operate satisfactorily in the presence of faults occurring in hardware and software components. The principle objective of applying redundancy is achieve reliability goals subject to techno-economic constraints. Due to a plenty of applications arising virtually in both industrial and military organizations especially in embedded fault tolerance systems including telecommunication, distributed computer systems, automated manufacturing systems, etc., the reliability and its dependability measures of redundant computer-based systems have become attractive features for the systems designers and production engineers. However, even with the best design of redundant computer-based systems, software and hardware failures may still occur due to many failure mechanisms leading to serious consequences such as huge economic losses, risk to human life, etc. The objective of present survey article is to discuss various key aspects, failure consequences, methodologies of redundant systems along with software and hardware redundancy techniques which have been developed at the reliability engineering level. The methodological aspects which depict the required steps to build a block diagram composed of components in different configurations as well as Markov and non-Markov state transition diagram representing the structural system has been elaborated. Furthermore, we describe the reliability of a specific redundant system and its comparison with a non redundant system to demonstrate the tractability of proposed models and its performance analysis.

We tested mediation models using structural equation modeling (SEM) to better understand the pathways that lead to negative heat-health outcomes for socially vulnerable households and individuals in Phoenix, AZ. Extreme heat presents a significant risk to public health, and current theories of social vulnerability cannot explain the mechanistic pathways that lead to negative health outcomes. A better understanding of these pathways can be used to target more effective public health interventions. We find that self-reported indoor thermal discomfort was a strong and significant predictor of indoor heat illness outcomes (Std $\mathrm{\text{B}}=0.72$, $p<0.01$), suggesting that heat risk messaging could encourage the public to trust their personal sense of being too warm. Thermal discomfort (Std $\mathrm{\text{B}}=-0.28$, $p<0.01$) and self-reported indoor heat illness (Std $\mathrm{\text{B}}=-0.47$, $p<0.01$) were only predicted by socioeconomic status (SES); age, gender, and racialized group did not relate to heat illness or thermal discomfort based on our sample. However, specified mediators did not fully explain the relationship between SES and thermal discomfort or heat illness, suggesting that we do not fully understand how social vulnerability manifests as negative heat-health outcomes. In addition, results show that passive cooling techniques, like taking a cold shower, did not have a protective effect on heat-health outcomes, and these passive cooling behaviors were used in greater proportion by socially vulnerable individuals. This study concludes with 11 evidence-based hypotheses about how social vulnerability may produce negative heat-health outcomes.

The computational power requirements of real-world optimization problems begin to exceed the general performance of the Central Processing Unit (CPU). The modeling of such problems is in constant evolution and requires more computational power. Solving them is expensive in computation time and even metaheuristics, well known for their eficiency, begin to be unsuitable for the increasing amount of data. Recently, thanks to the advent of languages such as CUDA, the development of parallel metaheuristics on Graphic Processing Unit (GPU) platform to solve combinatorial problems such as the Quadratic Assignment Problem (QAP) has received a growing interest. It is one of the most studied NP-hard problems and it is known for its high computational cost. In this paper, we survey several of the most important metaheuristics approaches for the QAP and we focus our survey on parallel metaheuristics using the GPU.

The development of vision-based human activity recognition and analysis systems has been a matter of great interest to both the research community and practitioners during the last 20 years. Traditional methods that require a human operator watching raw video streams are nowadays deemed as at least ineffective and expensive. New, smart solutions in automatic surveillance and monitoring have emerged, propelled by significant technological advances in the fields of image processing, artificial intelligence, electronics and optics, embedded computing and networking, molding the future of several applications that can benefit from them, like security and healthcare. The main motivation behind it is to exploit the highly informative visual data captured by cameras and perform high-level inference in an automatic, ubiquitous and unobtrusive manner, so as to aid human operators, or even replace them. This survey attempts to comprehensively review the current research and development on vision-based human activity recognition. Synopses from various methodologies are presented in an effort to garner the advantages and shortcomings of the most recent state-of-the-art technologies. Also a first-level self-evaluation of methodologies is also proposed, which incorporates a set of significant features that best describe the most important aspects of each methodology in terms of operation, performance and others and weighted by their importance. The purpose of this study is to serve as a reference for further research and evaluation to raise thoughts and discussions for future improvements of each methodology towards maturity and usefulness.

Measuring organisational trust gives stakeholders many indicators of their organisations. These indicators are performance, leadership, employees' satisfaction, job satisfaction, and knowledge sharing culture. In addition to that, the highest levels of organisations' trust can critically reduce conflict charges and transaction costs.

This paper is the first stage of a research study that discusses the importance of trust to knowledge sharing. This paper shows to what extent culture influences organisational trust compared with the global trust index. This research adopts a model of trust with five dimensions and they are relating to knowledge sharing in an organisation; concern for employees, openness and honesty, identification, reliability, and competence. The adopted model was used to develop the global trust index. A survey of 26 different items was conducted on 135 profitable organisations in Jordan in different industries. A total of 397 questionnaires were returned in a form eligible for the analysis.

The results show that the overall organisational trust index in Jordan compared with the global index is low and the difference is significant. Furthermore, all trust dimensions in Jordan have lower values than the world's trust dimensions. Locally, the openness and honesty dimension has the highest value, and the competency dimension has the lowest value. Therefore, Jordanian culture significantly affects the trust index in the country and lowers it compared with the global trust index.