Modelling the impact of port-centric logistics cluster on inter-firm competition

Port-centric logistics clusters are considered as intermodal gateways of international trade, which connect national economies with global production networks. Globalization and the resultant interdependency between producers and the markets they serve have thus increased the vitality of sophisticated global seaport hubs and networks. These clusters are a spatial aggregation of logistics-related interconnected and interdependent companies that assist in smooth port operations. Singapore, Rotterdam, and Dubai ports are world-class models of port-centric logistics clusters. The formation of these clusters is a derivative of regional growth and offer a conducive business environment within a geographically contained area. Despite the increasing popularity of cluster theory, there seems a lack of unified theoretical framework, and identification methods of the cluster because of the divergent approaches by which it has been proposed including but not limited to agglomeration economies, industrial districts, knowledge spillover, regional development, innovation system, and network, etc. There has been insufficient evidence to empirically evaluate the prevalence of port-centric logistics cluster, assess the functionality and characteristics of port-centric logistics clusters. There is also disagreement on three key questions: what industry types port-centric logistics clusters constitute, what methods are appropriate to delineate the boundary of port-centric logistics cluster and do the logistics firms within-cluster demonstrate higher inter-firm competition through competitive rivalry near port than away from port vicinity. In this study, a spatial approach is adopted to geographically delineate the spatial congregation of port-centric logistics employment using Melbourne as a case. Using the Census data from Australian Bureau of Statistics, this study aims to identify the industrial sectors that characterize port-centric logistics cluster followed by delineating the geographic boundary of cluster around Melbourne port that represents the area from where the seaport draws its workers in different port-related industries.  Using the information about where people live and work, and what industry they work in, the total workforce employed in port-related industries within the close vicinity of Port of Melbourne is estimated. Areas, where port-related employment is above nation's logistics employment average and spatially adjacent, are categorized as part of the port-centric logistics cluster. The employment gradient mapped in GIS illustrates the territorial representation of port-centric logistics cluster. The establishment of Melbourne port-centric logistics cluster could mean the opportunities for organizations to achieve agglomeration economies, increase rivalry among organizations to promote competition, closer proximity between customers and supplier, shared resources, increased inter-firm interactions, and knowledge sharing. Further, this study adopts a quantitative approach to model the relationship between the cluster and inter-firm competition. An online and paper-based survey was administered to 379 logistics firms within and outside the cluster. The constructs were adopted from previous studies and validated items were used. A pilot test was conducted to assess constructs reliability and validity. The measurement and structural models were tested using structural equation modelling. The results show a significant impact of clustering of logistics firms on inter-firm competition through competitive rivalry among firms. A multi-group analysis reveals a significant difference between two groups in relation to the impact of clustering around port geography and away from port on inter-firm competition. The study found that the logistics firms demonstrate accentuated competition near the port in a clustered environment than away from port geography. This shows the impact of land use consolidation by the State Government in its effort to boost transport and warehousing employment closer to the port. The contributions this study offer includes validated conceptual framework to evaluate the impact of clustering of logistics firms on inter-firm competition. From managerial perspective this study offers on the opportunity for the manager on how to make a locational decision to provide the services. The decision is based on considering the potential benefits of collocating into the clustered environment to enhance their capabilities through spill over effect which is inherited within the cluster. Moreover, the knowledge created through this study can be utilized to draft policies regarding transportation planning and urban land use to support the geographical area around the port which may, in turn, stimulate the logistics firms to work in the designated zone. The major limitation of the study is using the data only from Melbourne. A future study may consider comparing the data from two different cities or country to validate the results of this study of the positive impact of clustering on the competitive rivalry

Multi-sensor data fusion in mobile devices for the identification of Activities of Daily Living

Following the recent advances in technology and the growing use of mobile devices such as smartphones, several solutions may be developed to improve the quality of life of users in the context of Ambient Assisted Living (AAL). Mobile devices have different available sensors, e.g., accelerometer, gyroscope, magnetometer, microphone and Global Positioning System (GPS) receiver, which allow the acquisition of physical and physiological parameters for the recognition of different Activities of Daily Living (ADL) and the environments in which they are performed. The definition of ADL includes a well-known set of tasks, which include basic selfcare tasks, based on the types of skills that people usually learn in early childhood, including feeding, bathing, dressing, grooming, walking, running, jumping, climbing stairs, sleeping, watching TV, working, listening to music, cooking, eating and others. On the context of AAL, some individuals (henceforth called user or users) need particular assistance, either because the user has some sort of impairment, or because the user is old, or simply because users need/want to monitor their lifestyle. The research and development of systems that provide a particular assistance to people is increasing in many areas of application. In particular, in the future, the recognition of ADL will be an important element for the development of a personal digital life coach, providing assistance to different types of users. To support the recognition of ADL, the surrounding environments should be also recognized to increase the reliability of these systems. The main focus of this Thesis is the research on methods for the fusion and classification of the data acquired by the sensors available in off-the-shelf mobile devices in order to recognize ADL in almost real-time, taking into account the large diversity of the capabilities and characteristics of the mobile devices available in the market. In order to achieve this objective, this Thesis started with the review of the existing methods and technologies to define the architecture and modules of the method for the identification of ADL. With this review and based on the knowledge acquired about the sensors available in off-the-shelf mobile devices, a set of tasks that may be reliably identified was defined as a basis for the remaining research and development to be carried out in this Thesis. This review also identified the main stages for the development of a new method for the identification of the ADL using the sensors available in off-the-shelf mobile devices; these stages are data acquisition, data processing, data cleaning, data imputation, feature extraction, data fusion and artificial intelligence. One of the challenges is related to the different types of data acquired from the different sensors, but other challenges were found, including the presence of environmental noise, the positioning of the mobile device during the daily activities, the limited capabilities of the mobile devices and others. Based on the acquired data, the processing was performed, implementing data cleaning and feature extraction methods, in order to define a new framework for the recognition of ADL. The data imputation methods were not applied, because at this stage of the research their implementation does not have influence in the results of the identification of the ADL and environments, as the features are extracted from a set of data acquired during a defined time interval and there are no missing values during this stage. The joint selection of the set of usable sensors and the identifiable set of tasks will then allow the development of a framework that, considering multi-sensor data fusion technologies and context awareness, in coordination with other information available from the user context, such as his/her agenda and the time of the day, will allow to establish a profile of the tasks that the user performs in a regular activity day. The classification method and the algorithm for the fusion of the features for the recognition of ADL and its environments needs to be deployed in a machine with some computational power, while the mobile device that will use the created framework, can perform the identification of the ADL using a much less computational power. Based on the results reported in the literature, the method chosen for the recognition of the ADL is composed by three variants of Artificial Neural Networks (ANN), including simple Multilayer Perceptron (MLP) networks, Feedforward Neural Networks (FNN) with Backpropagation, and Deep Neural Networks (DNN). Data acquisition can be performed with standard methods. After the acquisition, the data must be processed at the data processing stage, which includes data cleaning and feature extraction methods. The data cleaning method used for motion and magnetic sensors is the low pass filter, in order to reduce the noise acquired; but for the acoustic data, the Fast Fourier Transform (FFT) was applied to extract the different frequencies. When the data is clean, several features are then extracted based on the types of sensors used, including the mean, standard deviation, variance, maximum value, minimum value and median of raw data acquired from the motion and magnetic sensors; the mean, standard deviation, variance and median of the maximum peaks calculated with the raw data acquired from the motion and magnetic sensors; the five greatest distances between the maximum peaks calculated with the raw data acquired from the motion and magnetic sensors; the mean, standard deviation, variance, median and 26 Mel- Frequency Cepstral Coefficients (MFCC) of the frequencies obtained with FFT based on the raw data acquired from the microphone data; and the distance travelled calculated with the data acquired from the GPS receiver. After the extraction of the features, these will be grouped in different datasets for the application of the ANN methods and to discover the method and dataset that reports better results. The classification stage was incrementally developed, starting with the identification of the most common ADL (i.e., walking, running, going upstairs, going downstairs and standing activities) with motion and magnetic sensors. Next, the environments were identified with acoustic data, i.e., bedroom, bar, classroom, gym, kitchen, living room, hall, street and library. After the environments are recognized, and based on the different sets of sensors commonly available in the mobile devices, the data acquired from the motion and magnetic sensors were combined with the recognized environment in order to differentiate some activities without motion, i.e., sleeping and watching TV. The number of recognized activities in this stage was increased with the use of the distance travelled, extracted from the GPS receiver data, allowing also to recognize the driving activity. After the implementation of the three classification methods with different numbers of iterations, datasets and remaining configurations in a machine with high processing capabilities, the reported results proved that the best method for the recognition of the most common ADL and activities without motion is the DNN method, but the best method for the recognition of environments is the FNN method with Backpropagation. Depending on the number of sensors used, this implementation reports a mean accuracy between 85.89% and 89.51% for the recognition of the most common ADL, equals to 86.50% for the recognition of environments, and equals to 100% for the recognition of activities without motion, reporting an overall accuracy between 85.89% and 92.00%. The last stage of this research work was the implementation of the structured framework for the mobile devices, verifying that the FNN method requires a high processing power for the recognition of environments and the results reported with the mobile application are lower than the results reported with the machine with high processing capabilities used. Thus, the DNN method was also implemented for the recognition of the environments with the mobile devices. Finally, the results reported with the mobile devices show an accuracy between 86.39% and 89.15% for the recognition of the most common ADL, equal to 45.68% for the recognition of environments, and equal to 100% for the recognition of activities without motion, reporting an overall accuracy between 58.02% and 89.15%. Compared with the literature, the results returned by the implemented framework show only a residual improvement. However, the results reported in this research work comprehend the identification of more ADL than the ones described in other studies. The improvement in the recognition of ADL based on the mean of the accuracies is equal to 2.93%, but the maximum number of ADL and environments previously recognized was 13, while the number of ADL and environments recognized with the framework resulting from this research is 16. In conclusion, the framework developed has a mean improvement of 2.93% in the accuracy of the recognition for a larger number of ADL and environments than previously reported. In the future, the achievements reported by this PhD research may be considered as a start point of the development of a personal digital life coach, but the number of ADL and environments recognized by the framework should be increased and the experiments should be performed with different types of devices (i.e., smartphones and smartwatches), and the data imputation and other machine learning methods should be explored in order to attempt to increase the reliability of the framework for the recognition of ADL and its environments.Após os recentes avanços tecnológicos e o crescente uso dos dispositivos móveis, como por exemplo os smartphones, várias soluções podem ser desenvolvidas para melhorar a qualidade de vida dos utilizadores no contexto de Ambientes de Vida Assistida (AVA) ou Ambient Assisted Living (AAL). Os dispositivos móveis integram vários sensores, tais como acelerómetro, giroscópio, magnetómetro, microfone e recetor de Sistema de Posicionamento Global (GPS), que permitem a aquisição de vários parâmetros físicos e fisiológicos para o reconhecimento de diferentes Atividades da Vida Diária (AVD) e os seus ambientes. A definição de AVD inclui um conjunto bem conhecido de tarefas que são tarefas básicas de autocuidado, baseadas nos tipos de habilidades que as pessoas geralmente aprendem na infância. Essas tarefas incluem alimentar-se, tomar banho, vestir-se, fazer os cuidados pessoais, caminhar, correr, pular, subir escadas, dormir, ver televisão, trabalhar, ouvir música, cozinhar, comer, entre outras. No contexto de AVA, alguns indivíduos (comumente chamados de utilizadores) precisam de assistência particular, seja porque o utilizador tem algum tipo de deficiência, seja porque é idoso, ou simplesmente porque o utilizador precisa/quer monitorizar e treinar o seu estilo de vida. A investigação e desenvolvimento de sistemas que fornecem algum tipo de assistência particular está em crescente em muitas áreas de aplicação. Em particular, no futuro, o reconhecimento das AVD é uma parte importante para o desenvolvimento de um assistente pessoal digital, fornecendo uma assistência pessoal de baixo custo aos diferentes tipos de pessoas. pessoas. Para ajudar no reconhecimento das AVD, os ambientes em que estas se desenrolam devem ser reconhecidos para aumentar a fiabilidade destes sistemas. O foco principal desta Tese é o desenvolvimento de métodos para a fusão e classificação dos dados adquiridos a partir dos sensores disponíveis nos dispositivos móveis, para o reconhecimento quase em tempo real das AVD, tendo em consideração a grande diversidade das características dos dispositivos móveis disponíveis no mercado. Para atingir este objetivo, esta Tese iniciou-se com a revisão dos métodos e tecnologias existentes para definir a arquitetura e os módulos do novo método de identificação das AVD. Com esta revisão da literatura e com base no conhecimento adquirido sobre os sensores disponíveis nos dispositivos móveis disponíveis no mercado, um conjunto de tarefas que podem ser identificadas foi definido para as pesquisas e desenvolvimentos desta Tese. Esta revisão também identifica os principais conceitos para o desenvolvimento do novo método de identificação das AVD, utilizando os sensores, são eles: aquisição de dados, processamento de dados, correção de dados, imputação de dados, extração de características, fusão de dados e extração de resultados recorrendo a métodos de inteligência artificial. Um dos desafios está relacionado aos diferentes tipos de dados adquiridos pelos diferentes sensores, mas outros desafios foram encontrados, sendo os mais relevantes o ruído ambiental, o posicionamento do dispositivo durante a realização das atividades diárias, as capacidades limitadas dos dispositivos móveis. As diferentes características das pessoas podem igualmente influenciar a criação dos métodos, escolhendo pessoas com diferentes estilos de vida e características físicas para a aquisição e identificação dos dados adquiridos a partir de sensores. Com base nos dados adquiridos, realizou-se o processamento dos dados, implementando-se métodos de correção dos dados e a extração de características, para iniciar a criação do novo método para o reconhecimento das AVD. Os métodos de imputação de dados foram excluídos da implementação, pois não iriam influenciar os resultados da identificação das AVD e dos ambientes, na medida em que são utilizadas as características extraídas de um conjunto de dados adquiridos durante um intervalo de tempo definido. A seleção dos sensores utilizáveis, bem como das AVD identificáveis, permitirá o desenvolvimento de um método que, considerando o uso de tecnologias para a fusão de dados adquiridos com múltiplos sensores em coordenação com outras informações relativas ao contexto do utilizador, tais como a agenda do utilizador, permitindo estabelecer um perfil de tarefas que o utilizador realiza diariamente. Com base nos resultados obtidos na literatura, o método escolhido para o reconhecimento das AVD são as diferentes variantes das Redes Neuronais Artificiais (RNA), incluindo Multilayer Perceptron (MLP), Feedforward Neural Networks (FNN) with Backpropagation and Deep Neural Networks (DNN). No final, após a criação dos métodos para cada fase do método para o reconhecimento das AVD e ambientes, a implementação sequencial dos diferentes métodos foi realizada num dispositivo móvel para testes adicionais. Após a definição da estrutura do método para o reconhecimento de AVD e ambientes usando dispositivos móveis, verificou-se que a aquisição de dados pode ser realizada com os métodos comuns. Após a aquisição de dados, os mesmos devem ser processados no módulo de processamento de dados, que inclui os métodos de correção de dados e de extração de características. O método de correção de dados utilizado para sensores de movimento e magnéticos é o filtro passa-baixo de modo a reduzir o ruído, mas para os dados acústicos, a Transformada Rápida de Fourier (FFT) foi aplicada para extrair as diferentes frequências. Após a correção dos dados, as diferentes características foram extraídas com base nos tipos de sensores usados, sendo a média, desvio padrão, variância, valor máximo, valor mínimo e mediana de dados adquiridos pelos sensores magnéticos e de movimento, a média, desvio padrão, variância e mediana dos picos máximos calculados com base nos dados adquiridos pelos sensores magnéticos e de movimento, as cinco maiores distâncias entre os picos máximos calculados com os dados adquiridos dos sensores de movimento e magnéticos, a média, desvio padrão, variância e 26 Mel-Frequency Cepstral Coefficients (MFCC) das frequências obtidas com FFT com base nos dados obtidos a partir do microfone, e a distância calculada com os dados adquiridos pelo recetor de GPS. Após a extração das características, as mesmas são agrupadas em diferentes conjuntos de dados para a aplicação dos métodos de RNA de modo a descobrir o método e o conjunto de características que reporta melhores resultados. O módulo de classificação de dados foi incrementalmente desenvolvido, começando com a identificação das AVD comuns com sensores magnéticos e de movimento, i.e., andar, correr, subir escadas, descer escadas e parado. Em seguida, os ambientes são identificados com dados de sensores acústicos, i.e., quarto, bar, sala de aula, ginásio, cozinha, sala de estar, hall, rua e biblioteca. Com base nos ambientes reconhecidos e os restantes sensores disponíveis nos dispositivos móveis, os dados adquiridos dos sensores magnéticos e de movimento foram combinados com o ambiente reconhecido para diferenciar algumas atividades sem movimento (i.e., dormir e ver televisão), onde o número de atividades reconhecidas nesta fase aumenta com a fusão da distância percorrida, extraída a partir dos dados do recetor GPS, permitindo também reconhecer a atividade de conduzir. Após a implementação dos três métodos de classificação com diferentes números de iterações, conjuntos de dados e configurações numa máquina com alta capacidade de processamento, os resultados relatados provaram que o melhor método para o reconhecimento das atividades comuns de AVD e atividades sem movimento é o método DNN, mas o melhor método para o reconhecimento de ambientes é o método FNN with Backpropagation. Dependendo do número de sensores utilizados, esta implementação reporta uma exatidão média entre 85,89% e 89,51% para o reconhecimento das AVD comuns, igual a 86,50% para o reconhecimento de ambientes, e igual a 100% para o reconhecimento de atividades sem movimento, reportando uma exatidão global entre 85,89% e 92,00%. A última etapa desta Tese foi a implementação do método nos dispositivos móveis, verificando que o método FNN requer um alto poder de processamento para o reconhecimento de ambientes e os resultados reportados com estes dispositivos são inferiores aos resultados reportados com a máquina com alta capacidade de processamento utilizada no desenvolvimento do método. Assim, o método DNN foi igualmente implementado para o reconhecimento dos ambientes com os dispositivos móveis. Finalmente, os resultados relatados com os dispositivos móveis reportam uma exatidão entre 86,39% e 89,15% para o reconhecimento das AVD comuns, igual a 45,68% para o reconhecimento de ambientes, e igual a 100% para o reconhecimento de atividades sem movimento, reportando uma exatidão geral entre 58,02% e 89,15%. Com base nos resultados relatados na literatura, os resultados do método desenvolvido mostram uma melhoria residual, mas os resultados desta Tese identificam mais AVD que os demais estudos disponíveis na literatura. A melhoria no reconhecimento das AVD com base na média das exatidões é igual a 2,93%, mas o número máximo de AVD e ambientes reconhecidos pelos estudos disponíveis na literatura é 13, enquanto o número de AVD e ambientes reconhecidos com o método implementado é 16. Assim, o método desenvolvido tem uma melhoria de 2,93% na exatidão do reconhecimento num maior número de AVD e ambientes. Como trabalho futuro, os resultados reportados nesta Tese podem ser considerados um ponto de partida para o desenvolvimento de um assistente digital pessoal, mas o número de ADL e ambientes reconhecidos pelo método deve ser aumentado e as experiências devem ser repetidas com diferentes tipos de dispositivos móveis (i.e., smartphones e smartwatches), e os métodos de imputação e outros métodos de classificação de dados devem ser explorados de modo a tentar aumentar a confiabilidade do método para o reconhecimento das AVD e ambientes

DESIGN AND OPTIMIZATION OF SIMULTANEOUS WIRELESS INFORMATION AND POWER TRANSFER SYSTEMS

The recent trends in the domain of wireless communications indicate severe upcoming challenges, both in terms of infrastructure as well as design of novel techniques. On the other hand, the world population keeps witnessing or hearing about new generations of mobile/wireless technologies within every half to one decade. It is certain the wireless communication systems have enabled the exchange of information without any physical cable(s), however, the dependence of the mobile devices on the power cables still persist. Each passing year unveils several critical challenges related to the increasing capacity and performance needs, power optimization at complex hardware circuitries, mobility of the users, and demand for even better energy efficiency algorithms at the wireless devices. Moreover, an additional issue is raised in the form of continuous battery drainage at these limited-power devices for sufficing their assertive demands. In this regard, optimal performance at any device is heavily constrained by either wired, or an inductive based wireless recharging of the equipment on a continuous basis. This process is very inconvenient and such a problem is foreseen to persist in future, irrespective of the wireless communication method used. Recently, a promising idea for simultaneous wireless radio-frequency (RF) transmission of information and energy came into spotlight during the last decade. This technique does not only guarantee a more flexible recharging alternative, but also ensures its co-existence with any of the existing (RF-based) or alternatively proposed methods of wireless communications, such as visible light communications (VLC) (e.g., Light Fidelity (Li-Fi)), optical communications (e.g., LASER-equipped communication systems), and far-envisioned quantum-based communication systems. In addition, this scheme is expected to cater to the needs of many current and future technologies like wearable devices, sensors used in hazardous areas, 5G and beyond, etc. This Thesis presents a detailed investigation of several interesting scenarios in this direction, specifically concerning design and optimization of such RF-based power transfer systems. The first chapter of this Thesis provides a detailed overview of the considered topic, which serves as the foundation step. The details include the highlights about its main contributions, discussion about the adopted mathematical (optimization) tools, and further refined minutiae about its organization. Following this, a detailed survey on the wireless power transmission (WPT) techniques is provided, which includes the discussion about historical developments of WPT comprising its present forms, consideration of WPT with wireless communications, and its compatibility with the existing techniques. Moreover, a review on various types of RF energy harvesting (EH) modules is incorporated, along with a brief and general overview on the system modeling, the modeling assumptions, and recent industrial considerations. Furthermore, this Thesis work has been divided into three main research topics, as follows. Firstly, the notion of simultaneous wireless information and power transmission (SWIPT) is investigated in conjunction with the cooperative systems framework consisting of single source, multiple relays and multiple users. In this context, several interesting aspects like relay selection, multi-carrier, and resource allocation are considered, along with problem formulations dealing with either maximization of throughput, maximization of harvested energy, or both. Secondly, this Thesis builds up on the idea of transmit precoder design for wireless multigroup multicasting systems in conjunction with SWIPT. Herein, the advantages of adopting separate multicasting and energy precoder designs are illustrated, where we investigate the benefits of multiple antenna transmitters by exploiting the similarities between broadcasting information and wirelessly transferring power. The proposed design does not only facilitates the SWIPT mechanism, but may also serve as a potential candidate to complement the separate waveform designing mechanism with exclusive RF signals meant for information and power transmissions, respectively. Lastly, a novel mechanism is developed to establish a relationship between the SWIPT and cache-enabled cooperative systems. In this direction, benefits of adopting the SWIPT-caching framework are illustrated, with special emphasis on an enhanced rate-energy (R-E) trade-off in contrast to the traditional SWIPT systems. The common notion in the context of SWIPT revolves around the transmission of information, and storage of power. In this vein, the proposed work investigates the system wherein both information and power can be transmitted and stored. The Thesis finally concludes with insights on the future directions and open research challenges associated with the considered framework

Robots, Cyborgs, and Humans. A Model of Consumer Behavior in Services: A Study in the Healthcare Services Sector

La present tesi es basa en una investigació que proposa un ús futurista de l'robot i el cyborg com cirurgians oculars. El model desenvolupat investiga la intenció de l'consumidor per elegir cada cirurgià (és a dir: cirurgià robot, cirurgià cyborg o cirurgià humà). Les dades es van analitzar utilitzant la tècnica PLS-SEM. Els resultats de la investigació mostren que l'expectativa d'esforç, l'expectativa de rendiment, el risc percebut i la influència social van mostrar un impacte significatiu en la intenció d'utilitzar els serveis de l'robot cirurgià. Els resultats de el model per al cyborg cirurgià van confirmar l'impacte significatiu de l'expectativa d'esforç, l'excitació, l'expectativa de rendiment i la influència social en la intenció d'utilitzar els seus serveis. L'expectativa d'esforç i la influència social van confirmar un impacte significatiu en la intenció d'utilitzar els serveis de l'cirurgià humà. Els resultats mostren que en els tres models les variables influència social i expectativa d'esforç afecten significativament a la intenció d'utilitzar aquests serveis de cirurgia i que amb diferent intensitat entre els models per expectativa de esforç-. L'impacte de la influència social dóna una idea general sobre la naturalesa de el sector de la salut a Jordània, on una part de la societat presta més atenció a les recomanacions dels altres a l'elegir els seus cirurgians. A més, l'impacte de l'expectativa d'esforç contribueix a les expectatives per la simplicitat de l'servei dels pacients, en termes d'ús i interacció amb els cirurgians proposats. L'anàlisi multigrup va confirmar que les variables dels models estan afectant de la mateixa manera a l'comparar la intenció d'usar cyborgs i humans, i a l'comparar cyborgs i robots. No obstant això, sí que hi ha diferències significatives a l'comparar l'elecció entre robots i humans en l'impacte de l'expectativa d'esforç per utilitzar els serveis de cirurgia. D'altra banda, els participants van mostrar la seva preferència pel cirurgià humà sobre els cirurgians cyborg i robot, respectivament. Com a resultat, l'acceptació de les tecnologies de robot i cyborg per part de la societat podria donar una idea sobre la lluita esperada en el futur entre el desenvolupament de robots i la millora de les capacitats humanes.La presente tesis se basa en una investigación que propone un uso futurista del robot y el cyborg como cirujanos oculares. El modelo desarrollado investiga la intención del consumidor para elegir a cada cirujano (es decir: cirujano robot, cirujano cyborg o cirujano humano). Los datos se analizaron utilizando la técnica PLS-SEM. Los resultados de la investigación muestran que la expectativa de esfuerzo, la expectativa de rendimiento, el riesgo percibido y la influencia social mostraron un impacto significativo en la intención de utilizar los servicios del robot cirujano. Los resultados del modelo para el cyborg cirujano confirmaron el impacto significativo de la expectativa de esfuerzo, la excitación, la expectativa de rendimiento y la influencia social en la intención de usar sus servicios. La expectativa de esfuerzo y la influencia social confirmaron un impacto significativo en la intención de utilizar los servicios del cirujano humano. Los resultados muestran que en los tres modelos las variables influencia social y expectativa de esfuerzo afectan significativamente a la intención de usar esos servicios de cirugía –aunque con distinta intensidad entre los modelos para expectativa de esfuerzo-. El impacto de la influencia social da una idea general sobre la naturaleza del sector de la salud en Jordania, donde una parte de la sociedad presta más atención a las recomendaciones de los demás al elegir a sus cirujanos. Además, el impacto de la expectativa de esfuerzo contribuye a las expectativas por la simplicidad del servicio de los pacientes, en términos de uso e interacción con los cirujanos propuestos. El análisis multigrupo confirmó que las variables de los modelos están afectando de la misma manera al comparar la intención de usar cyborgs y humanos, y al comparar cyborgs y robots. Sin embargo, sí que existen diferencias significativas al comparar la elección entre robots y humanos en el impacto de la expectativa de esfuerzo para utilizar los servicios de cirugía. Por otro lado, los participantes mostraron su preferencia por el cirujano humano sobre los cirujanos cyborg y robot, respectivamente. Como resultado, la aceptación de las tecnologías de robot y cyborg por parte de la sociedad podría dar una idea sobre la lucha esperada en el futuro entre el desarrollo de robots y la mejora de las capacidades humanThe research proposes a futuristic use of robot and cyborg as surgeons in an eye surgery. Thereafter, the developed model has been applied to investigate the intention to use each surgeon (i.e. robot surgeon, cyborg surgeon, and human surgeon). The data was analyzed using the PLS-SEM technique. According to the research results, effort expectancy, performance expectancy, perceived risk, and social influence showed a significant impact on intention to use robot services. However, the results of the cyborg service model confirmed the significant impact of effort expectancy, arousal, performance expectancy, and social influence on the intention to use cyborg services. Furthermore, effort expectancy and social influence confirmed their significant impact on the intention to use human services. The results of the three models showed that the variables social influence and effort expectancy significantly affected the intention to use these surgical services, with a different intensity between the models for effort expectancy. The social influence impact gives a general idea about the nature of the healthcare sector in Jordan, where a part of society gives more attention to the recommendation from others while choosing their surgeons. Also, the effort expectancy impact contributes to patients' expectations of simplicity, in terms of use and interaction with the proposed surgeons. The multigroup analysis confirmed that the models' variables are affecting the intention to use cyborg and human service, and cyborg and robots in the same way. However, the differences were confirmed between robot and human cyborgs in terms of the impact of effort expectancy on the intention to use these services. On the other side, the participants showed their preference of the human surgeon over the cyborg and robot surgeons, respectively. As a result, the acceptance of the robot and cyborg technologies by a part of the society could give an idea about the expected struggle in the future among developing robots and enhancing human capabilities

Smart Wireless Sensor Networks

The recent development of communication and sensor technology results in the growth of a new attractive and challenging area - wireless sensor networks (WSNs). A wireless sensor network which consists of a large number of sensor nodes is deployed in environmental fields to serve various applications. Facilitated with the ability of wireless communication and intelligent computation, these nodes become smart sensors which do not only perceive ambient physical parameters but also be able to process information, cooperate with each other and self-organize into the network. These new features assist the sensor nodes as well as the network to operate more efficiently in terms of both data acquisition and energy consumption. Special purposes of the applications require design and operation of WSNs different from conventional networks such as the internet. The network design must take into account of the objectives of specific applications. The nature of deployed environment must be considered. The limited of sensor nodes� resources such as memory, computational ability, communication bandwidth and energy source are the challenges in network design. A smart wireless sensor network must be able to deal with these constraints as well as to guarantee the connectivity, coverage, reliability and security of network's operation for a maximized lifetime. This book discusses various aspects of designing such smart wireless sensor networks. Main topics includes: design methodologies, network protocols and algorithms, quality of service management, coverage optimization, time synchronization and security techniques for sensor networks

Of Incentive, Bias, and Behaviour: An Empirical Economic Investigation into Project Delivery Constructs Influencing the Adoption of Building Information Modelling

Building Information Modelling (BIM) is a collaborative construction platform allowing for digital databases, real-time change management, and a high degree of information reuse catalysing increased quality of work, enhanced productivity, and lower costs. Yet, overall adoption rates within industry remain vexingly low. Integrated Project Delivery (IPD) is currently the only contractual incentive vehicle available for BIM, and indeed the full potential of both are only realised when employed together; even so, uptake rates of IPD exist even lower. In response, this research evaluates hitherto ill-explored factors influencing the adoption of BIM by empirically testing hypotheses related to the impacts of three compounding theories upon the BIM decision calculus. Specifically, the incentive theory, the theory of acceptance and use of technology (UTAUT), and the status quo bias model. The research approaches BIM adoption holistically at the organizational, individual, transactional, and behavioural levels through a mixed design combining five quantitative, cross-sectional, questionnaire-based studies and one interview-based pre-test/post-test case study with sample populations including a Fortune 100 contractor, internationally renowned trade groups, and arguably the most progressive municipal construction client in the world. Data was collected using purposive sampling and analysed quantitatively through Structural Equation Modelling (SEM) and qualitatively with Directed Content Analysis (DCA). Primary conclusions are that BIM decisions are hierarchical; BIM adoption involves a general higher-level decision-making requiring stakeholders’ consensus; BIM utilization involves a specific lower-level decision-making with managerial discretion; economic incentives and competitive pressure influence higher-level decisions; non-economic factors influence lower-level decisions but are moderated by organizations’ type and size; organizations’ size and the degree of managerial discretion are inversely related; strength of the effects vary across and within the three theory-based factors that influence BIM adoption; and the effects of leadership and organizational culture remain unaccounted for and require investigation

Towards an understanding of big data analytics as a weapon for competitive performance

Master's thesis Information Systems IS501 - University of Agder 2017Context: Big data has in the recent years been an area of interest among innovative organizations and has started to become a major priority for organizations in general, either through their own big data departments or by purchasing big data analyses from suppliers. Big data analytics means more knowledge from more data sources and is by many prophesied to be a contributing source of big change in how organizations receive their intelligence. Purpose: This thesis investigates the connection between big data and competitive performance. This connection could be explained through the following two paths; 1) how big data analytics contribute to making an organization more agile/dynamic and 2) how big data analytics improves daily operations. To measure this, we looked at big data analytics capabilities, dynamic capabilities and operational capabilities in addition to competitive performance. Methods: The methods that were used in this research was mainly of a quantitative type in addition to a qualitative case study and a two-phased literature review. We had to establish how to define and measure big data analytics capabilities. To do this we had to collect and review existing literature on big data analytical capabilities. We then had to do the same process with dynamic capabilities, operational capabilities and competitive performance. Even if there were little to no examples of previous literature on the whole scope of our research area, there were jigsaw bits that contained important knowledge on the different parts of the research area. With the help of previous literature and our case study, a survey was created. This was sent to big organizations in Nordic countries, mainly from the Kapital 500 list of the biggest organizations in Norway and Forbes Global 2000 list, where we focused on the biggest organizations in the Nordic countries. Extensive work was put into sorting away organizations that did not use big data, and to get respondents that did. A total of 135 respondents completed the survey and 107 of those used big data solutions. We developed a model with four hypotheses to investigate the relationship between big data analytic capabilities and competitive performance through the mediating concept of dynamic capabilities and operational capabilities. We analysed the responses using structural equation modelling. Specifically, we used partial least square path modelling (PLS-SEM). The tool used to distribute the survey was SurveyGizmo and we used SmartPLS to analyse the data. Results: Our analyses validated our first hypothesis which points to the positive correlation between big data analytics capabilities and dynamic capabilities to be significant. Further, the second hypothesis that stipulates the path from dynamic capabilities to competitive performance was significant. We also found significance on our third hypothesis which suggest a positive correlation between big data analytics capabilities and operational capabilities. We failed to find any significance on the fourth hypothesis which proposed that there is a positive correlation between operational capabilities and competitive performance. Also, we did not VI find any significance on environmental factors moderating effect on the second and fourth hypothesis. Conclusion: Overall, our results shows that the concept of big data analytics capability is transformed into competitive performance through the path of dynamic capability which can be seen as a mediating factor. This study contributes to better understand how big data analytics investments are turned into competitive actions and will be particularly valuable for companies using big data. Key words: big data analytics; big data analytics capabilities; dynamic capabilities; operational capabilities; competitive performanc