171 research outputs found
Implementing Robotic Process Automation in Sales Support
Digitalization has been shaping the ways how we work and live for a considerable length of time. Businesses’ competitiveness is partially determined by their capability to adopt and leverage new technologies. One of the latest trends in digitalization is the automation of repetitive, low-cognitive human tasks in white-collar jobs. A tool that was created to automate low-cognitive hu-man tasks, Robotic Process Automation (further only RPA) utilizes software robots to address this topic. RPA gains attraction because it is easily scalable and implemented at a rather low cost and the use of it doesn’t require prior programming skills. The implementation of RPA has been studied to some extent, however, the studies of implementation in sales and sales support are lacking. Notably, the automation of sales tasks is lagging far behind other business functions, even though a great deal of sales tasks could be automated. To address the limited understanding of automation in sales this study’s objective was to investigate the impact special features on sales might have with automation on a practical level, and the influence of human factors in RPA implementation and addressing employees’ commitment factors to ensure the use of RPA.
Reaching the targets of the study was ensured by answering the following research questions: 1) What are the prerequisites for the automation of sales support processes, 2) How to ensure employees’ commitment to RPA, 3) What kind of resources are needed from the organization in the RPA implementation, and 4) How to prioritize the tasks to be automated with RPA. The study was conducted as a single case study at a Finnish technology company. The primary data was gathered through semi-structured interviews and the interviewees were all employees of the case company with a relevant role to the studied issue. Multi-sourced secondary data was used to ensure data triangulation and broaden the insights of the results provided. The data was analysed through a thematic analysis.
To understand the main empirical findings some prevailing facts must be known. First, RPA is to be utilised by the Sales Support team for the first time, but RPA is not new in the company. However, the Sales Engineers (further only SE) have been provided with RPA training before this study took place. Second, the RPA process at the case company relies on the individual users and their motivations as it is not mandatory for SEs to use RPA.
It was discovered that SEs’ lack of motivation to use RPA is the main reason hindering the automation process in Sales Support. This could be addressed by increasing SEs’ knowledge of RPA by improving the provided training courses and by naming a key user or users to support SEs with the automation design. The importance of the key user should diminish when the use of RPA stabilises. It is also suggested to make the use of RPA temporarily mandatory through KPIs because the voluntariness of use has not led to the adoption of RPA as intended. Lastly, the first tasks to be automated should be prioritized based on task simplicity, as it will support the learning of the individuals and minimize the risk of systems operations being compromised.
This study contributes to the literature by increasing the understanding of the factors affecting the new technology implementation within sales and confirming some prior findings in the rather new field of study. In practice, the findings of the study advise managers on how to deal with and support already overloaded salespeople in RPA adoption. The study also investigated the voluntary use of technology at work which has been previously associated with private life only in the literature but could be further studied in the future. The study despite aiming for generalisability covers only a niche area of sales and thus a general study of RPA possibilities in sales could be of interest
Design Dynamics. Navigating the new Complex Landscape of Omnichannel Fashion Retail
The fashion industry is entering the dynamic global competitive market, promoting various actions prioritising design, creativity, sustainability, and technological advancement as pivotal factors. At the same time, it is reimagining its business models to adapt to the changing landscape. The rise of pervasive connectivity, intuitive interfaces and innovative interaction channels has triggered a revolution in fashion retail, reshaping customer behaviour and expectations. The traditional retail framework has evolved into a fully interconnected omnichannel system. This transformation is characterised by the proliferation of physical and virtual channels and touch points and by the adoption of a more flexible and integrated approach.
In this dynamic context, design plays a central role, possessing the ability to impart meaning to the production and distribution system. Design-led innovation represents an incremental form of innovation that injects a nuanced range of meaning into the marketplace, extending beyond tangible objects, including discourses, expressions, narratives, visual images, symbols, metaphors, and spaces.
The book analyses the multifaceted nature of the fashion retail experience through the lens of the design discipline, aiming to contextualise the evolution of retail within increasingly complex processes, networks and interconnections, both theoretically and practically. The focus is on retail design, delving into the new skills required and the valuable tools needed to apply them in inherently multidisciplinary contexts. Ultimately, the aim is to navigate the intricate terrain of retail evolution and shed light on the evolving role of design in this multifaceted sector
Designing an AI-enabled bundling generator in an automotive case study
Procurement and marketing are the main boundary-spanning functions of an organization. Some studies highlight that procurement is less likely to benefit from artificial intelligence emphasizing its potential in other functions, i.e., in marketing. A case study in the automotive industry of the bundling problem utilizing the design science approach is conducted from the perspective of the buying organization contributing to theory and practice. We rely on information processing theory to create a practical tool that is augmenting the skills of expert buyers through a recommendation engine to make better decisions in a novel way to further save costs. Thereby, we are adding to the literature on spend analysis that has mainly been looking backward using historical data of purchasing orders and invoices to infer saving potentials in the future – our study supplements this approach with forward-looking planning data with inherent challenges of precision and information-richness
Changing Priorities. 3rd VIBRArch
In order to warrant a good present and future for people around the planet and to safe the care of the planet itself, research in architecture has to release all its potential. Therefore, the aims of the 3rd Valencia International Biennial of Research in Architecture are:
- To focus on the most relevant needs of humanity and the planet and what architectural research can do for solving them.
- To assess the evolution of architectural research in traditionally matters of interest and the current state of these popular and widespread topics.
- To deepen in the current state and findings of architectural research on subjects akin to post-capitalism and frequently related to equal opportunities and the universal right to personal development and happiness.
- To showcase all kinds of research related to the new and holistic concept of sustainability and to climate emergency.
- To place in the spotlight those ongoing works or available proposals developed by architectural researchers in order to combat the effects of the COVID-19 pandemic.
- To underline the capacity of architectural research to develop resiliency and abilities to adapt itself to changing priorities.
- To highlight architecture's multidisciplinarity as a melting pot of multiple approaches, points of view and expertise.
- To open new perspectives for architectural research by promoting the development of multidisciplinary and inter-university networks and research groups.
For all that, the 3rd Valencia International Biennial of Research in Architecture is open not only to architects, but also for any academic, practitioner, professional or student with a determination to develop research in architecture or neighboring fields.Cabrera Fausto, I. (2023). Changing Priorities. 3rd VIBRArch. Editorial Universitat Politècnica de València. https://doi.org/10.4995/VIBRArch2022.2022.1686
Designing an AI-enabled Bundling Generator in an Automotive Case Study
Procurement and marketing are the main boundary-spanning functions of an organization. Some studies highlight that procurement is less likely to benefit from artificial intelligence emphasizing its potential in other functions, i.e., in marketing. A case study in the automotive industry of the bundling problem utilizing the design science approach is conducted from the perspective of the buying organization contributing to theory and practice. We rely on information processing theory to create a practical tool that is augmenting the skills of expert buyers through a recommendation engine to make better decisions in a novel way to further save costs. Thereby, we are adding to the literature on spend analysis that has mainly been looking backward using historical data of purchasing orders and invoices to infer saving potentials in the future – our study supplements this approach with forward-looking planning data with inherent challenges of precision and information-richness
Development of a co-creation model that streamlines the fashion process from the consumer to the manufacture
Nowadays, co-creation, a relatively new concept in the fashion field, is managing to
approach consumers directly and thus get a detailed understanding of their needs and
desires. Some brands already use this concept as the basis of their business, which is an
added value for the product and consequently for its suitability.
However, the adoption of this concept has been addressed in the literature in an isolated
way concerning consumers or brands, and its impact on the supply chain is still
unknown, so its applicability also needs research. There is also no clear idea of the role
of the fashion designer in co-creation fashion brands, and its role has also not been taken
into account in the literature.
In a real business context, the management of the processes of the different brands is
done autonomously. There is no pre-defined model for the structuration or application
that can serve as a basis for brands that want to adopt this concept. Thus, this research
focused on creating a co-creation model that can simplify the process from consumer to
production, focusing on fashion design, the product and the fashion designer, and the
supply chain, with the aim of creating a co-creation model, so that brands that want to
adopt the co-creation concept can apply it in real life.
An initial literature review revealed problems in the co-creation process that need a
solution and helped to identify the research problems. Subsequently, a comprehensive
literature review was carried out on co-creation, other concepts complementary to cocreation,
fashion design and the fashion business, consumers and the supply chain.
Secondly, a cases study focused on fashion brands already working with co-creation was
done to better understand how it works. Finally, a survey to consumers and interviews
with fashion designers and supply chain experts were conducted to understand each
party's point of view. These contributed to the revelation of important information,
subsequently implemented in the new co-creation model that allowed the simplification
of the process and its simultaneous suitability to consumers, brands and supply chain.
The results obtained allowed answering the research questions, the proposed objectives
and structuring a new co-creation model.Atualmente, a co-criação, um conceito relativamente novo no campo da moda, está a
conseguir abordar os consumidores diretamente, conseguindo desta forma perceber com
detalhe as suas necessidades e desejos. Algumas marcas já utilizam este conceito como
base do seu negócio, o que é um valor acrescentado para o produto e para a sua
consequente adequação.
No entanto, a adoção deste conceito tem sido abordada na literatura de forma isolada
relativamente aos consumidores ou marcas, e o seu impacto na cadeia de abastecimento
é ainda desconhecido, pelo que a sua aplicabilidade também necessita de investigação.
Também não há uma ideia clara do papel do designer de moda em marcas de moda de
co-criação, e o seu papel também não foi explorado na literatura.
Num contexto empresarial real, a gestão dos processos das diferentes marcas é feita de
forma autónoma. Não existe um modelo pré-definido para a estruturação ou aplicação
que possa servir de base para as marcas que queiram adotar este conceito. Assim, esta
investigação centrou-se na criação de um modelo de co-criação que pudesse simplificar
o processo desde o consumidor até à produção, concentrando-se no design de moda, no
produto, no designer de moda e na cadeia de abastecimento, com o objetivo de criar um
modelo de co-criação para que as marcas que queiram adotar o conceito de co-criação o
possam aplicar na vida real.
A revisão bibliográfica prévia revelou problemas no processo de co-criação que
necessitam de uma solução que ajudaram a identificar os problemas de investigação.
Posteriormente foi feita uma revisão bibliográfica completa sobre a co-criação, outros
conceitos complementares à co-criação, design de moda e o negócio da moda,
consumidores e a cadeia de abastecimento. Segundo, foi feito um estudo de casos
centrado em marcas de moda que já trabalham com a co-criação para melhor
compreender o seu funcionamento. Finalmente, foi feito um questionário aos
consumidores e entrevistas a designers de moda e especialistas da cadeia de
abastecimento para compreender o ponto de vista de cada parte. Estes contribuíram para
a revelação de informações importantes, posteriormente implementadas no novo
modelo de co-criação que permitiram a simplificação do processo e a sua adequação
simultânea aos consumidores, marcas e cadeia de abastecimento. Os resultados obtidos permitiram responder às questões de investigação, aos objetivos
propostos e estruturar um novo modelo de co-criação
The Use of Games and Crowdsourcing for the Fabrication-aware Design of Residential Buildings
State-of-the-art participatory design acknowledges the true, ill-defined nature of design problems, taking into account stakeholders' values and preferences. However, it overburdens the architect, who has to synthesize far more constraints into a one-of-a-kind design. Generative Design promises to equip architects with great power to standardize and systemize the design process. However, the common trap of generative design is trying to treat architecture simply as a tame problem. In this work, I investigate the use of games and crowdsourcing in architecture through two sets of explorative questions. First, if everyone can participate in the network-enabled creation of the built environment, what role will they play? And what tools will they need to enable them? And second, if anyone can use digital fabrication to build any building, how will we design it? What design paradigms will govern this process? I present a map of design paradigms that lie at the intersections of Participatory Design, Generative Design, Game Design, and Crowd Wisdom. In four case studies, I explore techniques to employ the practices from the four fields in the service of architecture. Generative Design can lower the difficulty of the challenge to design by automating a large portion of the work. A newly formulated, unified taxonomy of generative design across the disciplines of architecture, computer science, and computer games builds the base for the use of algorithms in the case studies. The work introduces Playable Voxel-Shape Grammars, a new type of generative technique. It enables Game Design to guide participants through a series of challenges, effectively increasing their skills by helping them understand the underlying principles of the design task at hand. The use of crowdsourcing in architecture can mean thousands of architects creating content for a generative design system, to expand and open up its design space. Crowdsourcing can also be about millions of people online creating designs that an architect or a homeowner can refer to increase their understanding of the complex issues at hand in a given design project and for better decision making. At the same time, game design in architecture helps find the balance between algorithmically exploring pre-defined design alternatives and open-ended, free creativity. The research reveals a layered structure of entry points for crowd-contributed content as well as the granular nature of authorship among four different roles: non-expert stakeholders, architects, the crowd, and the tool-makers
Understanding Customer Switching Behaviour in the Retail Banking Sector: The Case of Nigeria and the Gambia
This thesis examines customer switching behaviour in Nigeria and Gambia, focusing on the retail banking sector. The study’s key objective is to provide new knowledge on customer banking behaviour in the retail banking sector. The study is grounded in Bansal et al.’s (2005) push-pull-mooring model. A qualitative method was employed in the data collection, incorporating a triangulation approach, whereby direct observations were combined with thematic interviews and focus group discussions. The intention behind this method was to increase the validity of the research results. Ultimately, the study findings indicate significant factors and subfactors influencing customer switching behaviour in the retail banking sector. The results are categorised as push, pull, or mooring factors. It identifies seven push factors with thirteen subfactors, four pull factors with ten subfactors, and six mooring factors with three subfactors. The study’s significant contribution to existing knowledge of services marketing is the identification of new and emerging constructs, thus extending the existing knowledge in the literature. The study’s findings support numerous results of prior relevant research, while some findings disagree with those of previous research.
Furthermore, the new constructs that emerge from this research are highly relevant to today’s consumers. For example, factors like banking products, perceived knowledge of banking products, perceived relative security of banking products, satisfaction with the current bank, emotions (e.g., regret or anger), liquidity challenges, bank staff career development prospects, and ethical banking issues are the study’s unique contributions to the push factors and subfactors. In addition, the emerging pull factors and subfactors include technological advancement, coronavirus pandemic-induced switching, a bank’s physical appearance, positive banking expectations, a bank’s relative proximity, expected switching benefits, perceived usefulness of a bank’s digital platforms, perceived ease of banking transactions, personalised banking offerings, and repositioning banking business models. Lastly, the new mooring factors and subfactors identified in this study are inertia, changes in customer needs or tastes, involuntary switching, and bank responsiveness. Consequently, the author has developed a framework/model based on the findings of this study. The new framework/model presented comprehensive results with practical implications and a valuable contribution to the current knowledge of customer switching behaviour
Democratizing machine learning
Modelle des maschinellen Lernens sind zunehmend in der Gesellschaft verankert, oft in Form von automatisierten Entscheidungsprozessen. Ein wesentlicher Grund dafür ist die verbesserte Zugänglichkeit von Daten, aber auch von Toolkits für maschinelles Lernen, die den Zugang zu Methoden des maschinellen Lernens für Nicht-Experten ermöglichen.
Diese Arbeit umfasst mehrere Beiträge zur Demokratisierung des Zugangs zum maschinellem Lernen, mit dem Ziel, einem breiterem Publikum Zugang zu diesen Technologien zu er- möglichen. Die Beiträge in diesem Manuskript stammen aus mehreren Bereichen innerhalb dieses weiten Gebiets. Ein großer Teil ist dem Bereich des automatisierten maschinellen Lernens (AutoML) und der Hyperparameter-Optimierung gewidmet, mit dem Ziel, die oft mühsame Aufgabe, ein optimales Vorhersagemodell für einen gegebenen Datensatz zu finden, zu vereinfachen. Dieser Prozess besteht meist darin ein für vom Benutzer vorgegebene Leistungsmetrik(en) optimales Modell zu finden. Oft kann dieser Prozess durch Lernen aus vorhergehenden Experimenten verbessert oder beschleunigt werden.
In dieser Arbeit werden drei solcher Methoden vorgestellt, die entweder darauf abzielen, eine feste Menge möglicher Hyperparameterkonfigurationen zu erhalten, die wahrscheinlich gute Lösungen für jeden neuen Datensatz enthalten, oder Eigenschaften der Datensätze zu nutzen, um neue Konfigurationen vorzuschlagen.
Darüber hinaus wird eine Sammlung solcher erforderlichen Metadaten zu den Experimenten vorgestellt, und es wird gezeigt, wie solche Metadaten für die Entwicklung und als Testumgebung für neue Hyperparameter- Optimierungsmethoden verwendet werden können. Die weite Verbreitung von ML-Modellen in vielen Bereichen der Gesellschaft erfordert gleichzeitig eine genauere Untersuchung der Art und Weise, wie aus Modellen abgeleitete automatisierte Entscheidungen die Gesellschaft formen, und ob sie möglicherweise Individuen oder einzelne Bevölkerungsgruppen benachteiligen. In dieser Arbeit wird daher ein AutoML-Tool vorgestellt, das es ermöglicht, solche Überlegungen in die Suche nach einem optimalen Modell miteinzubeziehen. Diese Forderung nach Fairness wirft gleichzeitig die Frage auf, ob die Fairness eines Modells zuverlässig geschätzt werden kann, was in einem weiteren Beitrag in dieser Arbeit untersucht wird. Da der Zugang zu Methoden des maschinellen Lernens auch stark vom Zugang zu Software und Toolboxen abhängt, sind mehrere Beiträge in Form von Software Teil dieser Arbeit. Das R-Paket mlr3pipelines ermöglicht die Einbettung von Modellen in sogenan- nte Machine Learning Pipelines, die Vor- und Nachverarbeitungsschritte enthalten, die im maschinellen Lernen und AutoML häufig benötigt werden. Das mlr3fairness R-Paket hingegen ermöglicht es dem Benutzer, Modelle auf potentielle Benachteiligung hin zu über- prüfen und diese durch verschiedene Techniken zu reduzieren. Eine dieser Techniken, multi-calibration wurde darüberhinaus als seperate Software veröffentlicht.Machine learning artifacts are increasingly embedded in society, often in the form of automated decision-making processes. One major reason for this, along with methodological improvements, is the increasing accessibility of data but also machine learning toolkits that enable access to machine learning methodology for non-experts. The core focus of this thesis is exactly this – democratizing access to machine learning in order to enable a wider audience to benefit from its potential.
Contributions in this manuscript stem from several different areas within this broader area. A major section is dedicated to the field of automated machine learning (AutoML) with the goal to abstract away the tedious task of obtaining an optimal predictive model for a given dataset. This process mostly consists of finding said optimal model, often through hyperparameter optimization, while the user in turn only selects the appropriate performance metric(s) and validates the resulting models. This process can be improved or sped up by learning from previous experiments.
Three such methods one with the goal to obtain a fixed set of possible hyperparameter configurations that likely contain good solutions for any new dataset and two using dataset characteristics to propose new configurations are presented in this thesis.
It furthermore presents a collection of required experiment metadata and how such meta-data can be used for the development and as a test bed for new hyperparameter optimization methods. The pervasion of models derived from ML in many aspects of society simultaneously calls for increased scrutiny with respect to how such models shape society and the eventual biases they exhibit. Therefore, this thesis presents an AutoML tool that allows incorporating fairness considerations into the search for an optimal model. This requirement for fairness simultaneously poses the question of whether we can reliably estimate a model’s fairness, which is studied in a further contribution in this thesis. Since access to machine learning methods also heavily depends on access to software and toolboxes, several contributions in the form of software are part of this thesis. The mlr3pipelines R package allows for embedding models in so-called machine learning pipelines that include pre- and postprocessing steps often required in machine learning and AutoML. The mlr3fairness R package on the other hand enables users to audit models for potential biases as well as reduce those biases through different debiasing techniques. One such technique, multi-calibration is published as a separate software package, mcboost
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