Browsing by Author "Suvivuo, Sampsa"

Now showing 1 - 7 of 7
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    21st Century Cottage Industry - A cross-case synthesis of freelancer intermediary platforms
    (2018) Suvivuo, Sampsa; Tuunainen, Virpi; Tieto- ja palvelujohtamisen laitos; Kauppakorkeakoulu; School of Business
    The purpose of this study was to identify possible archetypes of freelancer intermediary platforms. Though there is growing interest towards platforms, classification of platforms stops when it is classified as a transaction, innovation, integrated or some other platform. However, this approach doesn’t account for the variation within these categories. Given the young population's interest towards freelancing and the estimated size of the platform economy as a whole ($4300 Bn.) and the number of freelancer intermediaries (250-300), attempting to identify the subtypes of freelancer intermediary platforms was deemed a worthy endeavor. Finding these subtypes of intermediary platforms or archetypes of freelancer intermediaries has both academic and practical implications. For academics, these archetypes will contribute to the growing body of platform literature by giving it new units of analysis and by creating reasonable categorization. For people interested in utilizing a freelancer intermediary platform either as a seller or a buyer, this thesis offers solid knowledge of the intermediary platforms functions and features as well as what to expect when joining one. The research design is built on principles of embedded and flexible multiple-case study and cross-case synthesis. When describing a contemporary phenomenon, a multiple-case study produces more robust results when the weight of one case decreases. The cross-case synthesis was one of the few viable options given the study’s lack of dependent and independent variables. These variables were unavailable because no beforehand information on what the archetypes could be was available. For this reason, this study adapted analytical methods of grounded theory. The study identified four archetypes of freelancer intermediary platforms: the locals, two for the price of one, the middle child and the global juggernauts. Locals focus on physical services that are dependent on freelancers’ location. Two for the price of one are small platforms that charge only one side be it, seller or buyer. The middle child is very similar to global juggernauts in other aspects but the size and is a necessary phase in the platform’s maturation. Global juggernauts are the biggest platforms and the industry leaders that have significant network and trust management systems in place. Archetypes form a solid foundation on which future research on freelancer intermediaries can be based on.
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    Building and Sustaining Communities in Online Labour Platforms
    (2023-05-11) Suvivuo, Sampsa; Rinta-Kahila, Tapani; Tuunainen, Virpi Kristiina; Department of Information and Service Management; University of Queensland
    Online labour platforms enable the trading of services by connecting buyers who need services with providers willing to deliver those services. One crucial element to a platform’s survival and success lies in creating and fostering a thriving community of users – otherwise, the platform is likely to perish. However, we know very little about how such communities can be built. Extant studies have overlooked different stakeholder groups’ roles in community building and how primarily online platforms utilize offline activities. In this paper, we study the community-building efforts of six online labour platforms that have succeeded to thrive longer than an average platform. Beyond the traditional stakeholder roles of providing a marketplace (platform), delivering services (providers) and purchasing services (buyers), we find novel unique and shared roles that foster a well-functioning community. We identify five levers of community building: facilitating trade, encouraging community participation, involving users, empowering providers, and empowering buyers.
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    Challenges and Solutions in Qualitative Big Data Research: A Methodological Literature Review
    (Association for Information Systems, 2024-07-31) Suvivuo, Sampsa; Tuunainen, Virpi; Department of Information and Service Management; School Common, BIZ
    The digitalization of our daily lives has considerably increased the amount of digital (trace) data on people’s behaviors that are available to researchers. However, qualitative methods that require manually perusing each document struggle with the width and breadth of such data. Although quantitative and qualitative big data share many challenges, we identified the practical challenges encountered by researchers, specifically with qualitative big data, and how these challenges were addressed. We reviewed 169 studies that used qualitative big data and identified three main categories of intertwined challenges: locating relevant data, addressing noise in the data, and preserving data richness. We found that the greater the amount of data and the richer they are, the greater the variety of types and sources of noise. While the volume of the data necessitates the use of algorithms, doing so entails the treatment of data in ways that decrease the richness of qualitative data. Furthermore, simultaneously ensuring high richness and veracity might be difficult because the algorithms are probabilistic, thus compelling researchers to balance the desired levels of volume, variety, and veracity. Although the identified solutions cannot completely solve this tripartite balancing, they can still be used to alleviate different aspects of such a challenge.
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    Foreword IRIS46: Reflecting on the Nordic Approach to IS Research
    (2023) Suvivuo, Sampsa; Hettich, Alexandra; Department of Information and Service Management; Copenhagen Business School
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    Landscape of mobile-assisted language learning applications (MALL)
    (2023) Jaatela, Janne; Tuunainen, Virpi; Suvivuo, Sampsa; Tieto- ja palvelujohtamisen laitos; Kauppakorkeakoulu; School of Business
    This thesis was done to increase knowledge of the competitive aspects of a new and unstudied MLLA market. A multiple case study was chosen as the method to investigate certain case companies that have proven themselves in the market over a lengthy period. Information for the study was gathered from public sources including news articles and websites related to the case companies. The findings from individual cases were presented in the strategic analysis tool Ansoff matrix. Individual cases were included in meta-analysis to determine factors that seem to be trending within the market. The result was a MALL focused version of Ansoff Matrix that combines the used strategies within a single matrix framework. Findings from the meta-analysis suggest that there are certain key differences and similarities in the strategic routes the companies have taken but most of the decisions seem to be made in isolation from the competitors.
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    Qualitative Big Data’s Challenges and Solutions
    (2021-01-05) Suvivuo, Sampsa; Department of Information and Service Management
    Digitalization of everyday lives has tremendously increased the amount of digital (trace) data of people’s behaviour available for researchers. However, traditional qualitative research methods struggle with the width and breadth of the data. This paper reviewed 61 recent studies that had utilized qualitative big data for the practical challenges they had encountered and how they were addressed. While quantitative and qualitative big data share many common issues, the review points at that lack of qualitative methods and dataset reduction required by algorithms in big data research decreases the richness of the qualitative data. Locating relevant data and reducing noise are further challenges. Currently, these challenges can be only partially addressed with a combination of human and computer pattern recognition and crowdsourcing. The review describes many “tricks of the trade” but abduction research and pragmatist philosophy seem promising starting places for a more pervasive framework.
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    Use of artificial intelligence in anti-money laundering: expected benefits and challenges faced by banks
    (2021) Meriläinen, Ville; Tuunainen, Virpi; Suvivuo, Sampsa; Tieto- ja palvelujohtamisen laitos; Kauppakorkeakoulu; School of Business
    Money laundering continues to negatively impact societies and economies globally. Governments and regulators have tightened anti-money laundering regulations in recent years, leading to banks having had to invest significantly in their anti-money laundering units and controls to remain compliant. Despite this development and the additional cost taken on by the banks, only a miniscule amount of laundered money gets caught by law enforcement. New, more efficient methods for fighting financial crime are needed to catch money launderers. The purpose of this thesis was to study if using artificial intelligence to identify and prevent financial crime could be a way to both decrease the cost of compliance for banks and financial institutions and increase the effectiveness of their AML controls – ultimately leading to catching more of the illicit funds being laundered. A literature review was conducted to understand and identify what artificial intelligence solutions are available for banks to use in anti-money laundering, what challenges are related to implementing them and what benefits they provide. The research was conducted by having qualitative semi-structured interviews with senior leaders and experts within the anti-money laundering. The interview data was analyzed to identify similarities, differences, and relationships between interviewees’ answers. The findings from the interviews were compared to what was learned from the literature review, to see how the findings might differ from existing knowledge literature. The findings suggest that various artificial intelligence based solutions are available to be used by banks in anti-money laundering, including machine learning powered transaction monitoring and social network analysis for identifying networks of criminals. AI is expected to provide great benefits in terms of reduced cost and increased quality of the banks’ AML controls. However, banks face data quality and availability challenges due to their often old and complex IT system landscapes. Investment is needed into creating a data foundation to build on before AI can be successfully implemented.