Beyond Utility: An inductive investigation into non-utility factors influencing consumer adoption and use of ICT

This study explores the adoption and use of Information and Communications Technologies (ICTs) in a context marked by ubiquitous connectivity and intense social interaction. Research in the field has predominantly explored the topic within closed and private contexts, such as work and education environments. Resulting theories tend to lose predictive strength when transferred to open and social contexts. Specifically, theories often assume that behaviour is shaped exclusively by the utility derived from technological functions – an occurrence more common in closed and private settings. Other influencing factors, whilst acknowledged, tend to be sidelined or treated as exceptions. Further complexities arise as theorists misread and mistreat user perceptions and intentions. The study combines an inductive strategy with a Skinnerian radical behaviourist philosophical worldview. Individual accounts and group discussion about online social networking and smartphone ownership were captured in a natural social setting. A total of 35 technology users from Malta aged between 18 and 40 years participated in face-to-face interviews and focus group discussions. In contrast to other studies, verbal accounts and group interaction were treated and analysed as social behaviour and not as cognitive decision processes. Findings show that a more holistic understanding emerges if the social and internal dimensions are considered alongside environmental consequences. Results indicate that beyond utilitarian benefits, users also seek pleasure and social status whilst averting risk and minimising cost and disruption. The study shows that consumer ICTs are different from other technologies, such as cars and refrigerators, since these are tools specifically designed for application within verbal behaviour. ICTs can be applied as tools to communicate information, share past experiences, provide feedback to others, and confer social status on others. ICT applications elicit feedback from listeners and observers rather than cause measurable changes in the environment. The study builds on this insight by proposing a conceptual framework as an interpretative tool for practitioners and as a theoretic proposition for future inquiry

A Case Study on Ubiquitous Social Networking: Fusion Mobile

This thesis deals with the challenges and opportunities inherent in building a prototype capable of supporting ubiquitous social networking applications on mobile devices. The challenges in the design of mobile application are investigated through a case study that develops a prototype, Fusion Mobile, which is an application for mobile social networking and media sharing. This research is carried out within the context of an ongoing research project P2P-FUSION, which addresses current difficulties in regards to legal, creative reuse of audiovisual media on the internet. The project develops a software system called Fusion, which allows anyone to publish audiovisual content only to the audience they want within a P2P network, as well as through a mobile application. In developing this prototype application (Fusion Mobile), and the user interface in particular, I investigate the design process to find general keys to success in social networking application development for mobile devices. I develop a design framework and use a co-design approach to guide the process. By analyzing the design process, I provide insight to mobile application developers regarding the pros and cons of 1) using a pre-defined design framework to guide application development and 2) involving users and external mobile developers int he design process as co-design partners

Analytics of human presence and movement behaviour within specific environments

The vast amounts of detailed information, generated by Wi-Fi and other mobile communication technologies, provide an invaluable opportunity to study different aspects of presence and movement behaviours of people within a given environment; for example, a university campus, an organisation office complex, or a city centre. Utilising such data, this thesis studies three main aspects of the human presence and movement behaviours: spatio-temporal movement (where and when do people move), user identification (how to uniquely identify people from their presence and movement historical records), and social grouping (how do people interact). Previous research works have predominantly studied two out of these three aspects, at most. Conversely, we investigate all three aspects in order to develop a coherent view of the human presence and movement behaviour within selected environments. More specifically, we create stochastic models for movement prediction and user identification. We also devise a set of clustering models for the detection of the social groups within a given environment. The thesis makes the following contributions: 1. Proposes a family of predictive models that allows for inference of locations though a collaborative mechanism which does not require the profiling of individual users. These prediction models utilise suffix trees as their core underlying data structure, where predictions about a specific individual are computed over an aggregate model incorporating the collective record of observed behaviours of multiple users. 2. Defines a mobility fingerprint as a profile constructed from the users historical mobility traces. The proposed method for constructing such a profile is a principled and scalable implementation of a variable length Markov model based on n-grams. 3. Proposes density-based clustering methods that discover social groups by analysing activity traces of mobile users as they move around, from one location to another, within an observed environment. We utilise two large collections of mobility traces: a GPS data set from Nokia and an Eduroam network log from Birkbeck, University of London, for the evaluation of the proposed models reported herein

Analytics of human presence and movement behaviour within specific environments

The vast amounts of detailed information, generated by Wi-Fi and other mobile communication technologies, provide an invaluable opportunity to study different aspects of presence and movement behaviours of people within a given environment; for example, a university campus, an organisation office complex, or a city centre. Utilising such data, this thesis studies three main aspects of the human presence and movement behaviours: spatio-temporal movement (where and when do people move), user identification (how to uniquely identify people from their presence and movement historical records), and social grouping (how do people interact). Previous research works have predominantly studied two out of these three aspects, at most. Conversely, we investigate all three aspects in order to develop a coherent view of the human presence and movement behaviour within selected environments. More specifically, we create stochastic models for movement prediction and user identification. We also devise a set of clustering models for the detection of the social groups within a given environment. The thesis makes the following contributions: 1. Proposes a family of predictive models that allows for inference of locations though a collaborative mechanism which does not require the profiling of individual users. These prediction models utilise suffix trees as their core underlying data structure, where predictions about a specific individual are computed over an aggregate model incorporating the collective record of observed behaviours of multiple users. 2. Defines a mobility fingerprint as a profile constructed from the users historical mobility traces. The proposed method for constructing such a profile is a principled and scalable implementation of a variable length Markov model based on n-grams. 3. Proposes density-based clustering methods that discover social groups by analysing activity traces of mobile users as they move around, from one location to another, within an observed environment. We utilise two large collections of mobility traces: a GPS data set from Nokia and an Eduroam network log from Birkbeck, University of London, for the evaluation of the proposed models reported herein

Effects of Gatekeeping on the Diffusion of Information

This study proposes a theoretical model of information diffusion using the conceptual framework of Gatekeeping Theory (Shoemaker & Vos, 2009). Diffusion is a process by which elements are distributed through a social system (Rogers, 2003; Kadushin, 2012). This model builds on previous diffusion research and incorporates constructs of authority and vivid information, novel to the domain. To test the fit of the model, Twitter data derived using data mining techniques are utilized. Specifically, messages posted to Twitter relating to the 2013 Consumer Electronics (CES) conference are mined. Essentially, this study focuses on the diffusion of technology information through a popular social medium, Twitter. From these messages, the network was be visualized and diffusion paths were determined using network analysis. A test of the model was conducted to determine fit using structural equation modeling

Identity projects of design professionals - Identity construction using social media

Objective of the study The aim of this study is to research how a group of design professionals construct and evaluate their identity projects and what kinds of processes are involved in their identity work. This study also looks at how possible identity conflicts are resolved and what kind of roles does social media play as a part of these processes. Research method This research is qualitative and interpretive in nature. Transcripts of 8 semi-structured interviews were used as data for this research. All interviewees were currently working or had worked within design. The interview transcripts were analysed using hermeneutic logic starting from categorizing emerging overall themes, and moving on to careful analysis of parts of the text. Through a series of part-to-whole iterations, a deep understanding of the text and it themes was sought. As a result of my analysis I found two major identity conflict themes that I used as a central structure for my findings. Findings Although identity projects can emerge in a variety of ways there are similarities in what kinds of meanings are attached to being a design professional. The identity seems to be socially constructed as most informants narrated it as belonging or alternatively distancing themselves from a social group. Being a design professional seems to be a very conflicted identity project with a constant struggle of staying creative but simultaneously succumbing and fulfilling the very different busi-ness needs. This showed as integrity and efficacy concerns. Social media and other digital sources help designers speed up the exploration phase of design and enable them to find inspiration faster or even store it in the forms of pictures and texts on their social media profiles. The other major conflict had to do with social categorisation and belonging to the design commu-nity, but distancing yourself enough as to stay individual and maintain your own unique perspec-tive. This entailed using different types of social media services for different parts of the identity such as separating your professional and personal identities with the use of different social media profiles. This also meant constant editing of the profiles so that they stay appropriate to their audi-ence

Identifying Entrepreneurial Opportunities: Cognition and Categorization in Nascent Entrepreneurs

Scholars, practitioners, and policy-makers share a common interest in understanding entrepreneurship. However, while research on entrepreneurship has burgeoned in recent years, our understanding of how people identify opportunities – a critical first step in the entrepreneurial process – remains relatively limited (Shane, 2012). Indeed, extant research lacks consensus about the basic nature and definition of opportunities, rendering the literature on opportunity identification both theoretically fragmented and empirically underdeveloped. To address this problem, my dissertation uses an exploratory sequential mixed method design (Creswell, 2013) to develop a detailed understanding of the opportunity identification process. In the first phase of research, I conducted interviews with nascent entrepreneurs in an inductive, qualitative study. These interviews yielded two important findings. First, entrepreneurs tend to view opportunities as new technology-market combinations. This view is consistent with previous research suggesting that opportunities emerge when entrepreneurs perceive ‘matches’ between new means of supply and markets where those means of supply can be introduced (Gregoire & Shepherd, 2012). Second, my interviewees described three cognitive processes through which such opportunities are identified: analogistic thinking, recombination, and distinction-making. Of these, distinction-making was the most prevalent process reported, and it appears to be closely related to opportunity identification among both nascent entrepreneurs and managers in existing firms. In the second phase of research, I theorized that distinction-making – the process of creating and refining new categories of information, objects and events – facilitates opportunity identification by enabling people to identify potential ‘fit’ between technologies and markets. I conducted a series of three experimental studies to more closely examine the relationship between distinction-making and opportunity identification. Results indicate that distinction-making is positively related to the number of opportunities people identify for generating new technologies, as well as the number of opportunities they identify for applying existing technologies to new markets. Distinction-making is also positively related to the innovativeness of those opportunities, where innovativeness is judged by other relevant actors in the entrepreneurial process. Moreover, the data indicate that distinction-making facilitates opportunity identification by enabling higher levels of domain-specific information processing in the domain in which the opportunities lie.PhDBusiness AdministrationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116674/1/karlesky_1.pd

Computational Algorithm for Finding Outcasts from Social Networks

Syrjäytyminen yhteiskunnasta ja sosiaalisesta kanssakäymisestä on puhuttanut mediassa ja valtion ylimmässä johdossa. Sitä on pidetty ongelmallisena ja suurena huolenaiheena koskien pääosin nuoria. Syrjäytyminen ei rajoitu pelkästään nuorisoon, vaan sitä esiintyy jo pienillä lapsilla yksinäisyyden ja ryhmään kuulumattomuuden muodoissa. Sitä esiintyy myös aikuisilla esimerkiksi työttömyyden tai masentuneisuuden myötä. Sosiaalisista suhteista syrjäytymistä kuvataan vieraantumisella. Tässä työssä esittelen uuden algoritmin syrjäytyneiden löytämiseksi sosiaalisista verkostoista käyttäen verkkomallinnuksen periaatteita. Algoritmi käyttää tietoa ryhmittymien muodostumisesta sekä yksittäisten solmujen lasketusta vaikutusvallasta. Algoritmin teoria pohjautuu paikallisten maksimien löytämiseen, verkkotopologian yksityiskohtaiseen läpikäymiseen sekä matemaattisiin malleihin, joiden avulla vaikutusvaltaa mitataan. Vaikutusta mitataan laskemalla solmujen ja linkkien painoarvoja, laskemalla levinneisyyttä yhdestä solmusta kaikkiin ja kaikista yhteen. Algoritmin eri komponentteja voidaan käyttää moneen tarkoitukseen, joista syrjäytyneiden etsiminen on yksi sovelluskohde. Algoritmin laskennallinen tehokkuus ja sen luotettavuus realistisissa tapauksissa osoitetaan. Algoritmin tuottamia tuloksia on tarkasteltu ja niistä on huomattu loogisia yhtäläisyyksiä verkon topologiaan verrattuna. Algoritmi sopii tilanteisiin, joissa verkon topologia voidaan mallintaa reaalimaailmasta. Luokkahuoneen topologia voidaan mallintaa esimerkiksi pyytämällä lapsia nimeämään kavereitaan kyseiseltä luokalta. Algoritmin tarkoituksena on tunnistaa syrjäytyviä yksilöitä ajoissa ja täten ehkäistä syrjäytymisestä ja yksinäisyydestä aiheutuvia haittavaikutuksia tunnetuissa sosiaalisissa ympäristöissä. Syrjäytymisen ehkäisemiseksi yhteiskunnallisessa mielessä algoritmi auttaa havaitsemaan yksilöt, joilla ei ole laajaa verkostoa. Työ- tai opiskelupaikka olisi mahdollista saada helpommin kontaktien avulla.Social isolation from the society and from social interactions has been a topic in the media, including the government’s highest leaders. It has been considered problematic and major concern for mostly young people. However, the exclusion is not limited only towards young people, but it is already present in lives of small children in forms of loneliness and non-belonging. It is also present in adult lives, for example, due to unemployment or depression. The social isolation is described by the term alienation. In this research, I present a new algorithm for finding excluded people in social networks using the principles of network modeling. The algorithm uses information about the formation of communities and the computed influence of individual nodes. The theory of the algorithm is based on locating the local maxima, going through the detailed topology of the network and mathematical models measuring influence. Influence is measured by computing the weights of nodes and links, by computing spreading probabilities from a single node to everywhere and from all nodes to the selected node. Different modules of the algorithm can be used for many purposes, from which searching of outcasts is one application. The efficiency of the algorithm and its reliability in realistic cases will be demonstrated. The results of the algorithm have been studied and logical similarities over the network topology have been found. The algorithm is suitable for situations where network topology can be modeled from the real world. Classroom topology can be modeled for example by asking children to name their friends in the class. The purpose of the algorithm is to find the isolated persons to prevent the negative effects of exclusion and loneliness in well-known social environments. To prevent social exclusion from the view of society, the algorithm helps to detect individuals without a large network. It would be easier to get a job or study place through contacts