863 research outputs found
Unlocking service provider excellence : expanding the touchpoints, context, qualities framework
Customer reviews offer scope for better understanding the customer experience (CX), which may be leveraged to improve firms' CX performance. We extend the Touchpoints, Context, Qualities (TCQ) nomenclature by integrating it with the ARC value-creation elements and the multiple dimensions of CX. Our extended TCQ framework comprises nine building blocks to delineate dynamic what we term CX performance trajectories. We test our framework by collecting verbatim text-based reviews, and transforming them into two robust data sets (weekly, and monthly), which we examine using a dynamic Hidden Markov Model. We identify three levels of CX performance states and the migrations paths between them. We find that the building blocks coherently express mechanisms that are effective at the weekly and monthly levels for helping firms improve, and prevent deterioration of, CX performance. This research enriches the CX and TCQ literature. In particular, we derive actionable guidance for managers to facilitate the dynamic management of their firm’s CX performance
NEMISA Digital Skills Conference (Colloquium) 2023
The purpose of the colloquium and events centred around the central role that data plays
today as a desirable commodity that must become an important part of massifying digital
skilling efforts. Governments amass even more critical data that, if leveraged, could
change the way public services are delivered, and even change the social and economic
fortunes of any country. Therefore, smart governments and organisations increasingly
require data skills to gain insights and foresight, to secure themselves, and for improved
decision making and efficiency. However, data skills are scarce, and even more
challenging is the inconsistency of the associated training programs with most curated for
the Science, Technology, Engineering, and Mathematics (STEM) disciplines.
Nonetheless, the interdisciplinary yet agnostic nature of data means that there is
opportunity to expand data skills into the non-STEM disciplines as well.College of Engineering, Science and Technolog
Detecting Team Conflict From Multiparty Dialogue
The emergence of online collaboration platforms has dramatically changed the dynamics of human teamwork, creating a veritable army of virtual teams composed of workers in different physical locations. The global world requires a tremendous amount of collaborative problem solving, primarily virtual, making it an excellent domain for computer scientists and team cognition researchers who seek to understand the dynamics involved in collaborative tasks to provide a solution that can support effective collaboration. Mining and analyzing data from collaborative dialogues can yield insights into virtual teams\u27 thought processes and help develop virtual agents to support collaboration. Good communication is indubitably the foundation of effective collaboration. Over time teams develop their own communication styles and often exhibit entrainment, a conversational phenomenon in which humans synchronize their linguistic choices. This dissertation presents several technical innovations in the usage of machine learning towards analyzing, monitoring, and predicting collaboration success from multiparty dialogue by successfully handling the problems of resource scarcity and natural distribution shifts. First, we examine the problem of predicting team performance from embeddings learned from multiparty dialogues such that teams with similar conflict scores lie close to one another in vector space. We extract the embeddings from three types of features: 1) dialogue acts 2) sentiment polarity 3) syntactic entrainment. Although all of these features can be used to predict team performance effectively, their utility varies by the teamwork phase. We separate the dialogues of players playing a cooperative game into stages: 1) early (knowledge building), 2) middle (problem-solving), and 3) late (culmination). Unlike syntactic entrainment, both dialogue act and sentiment embeddings effectively classify team performance, even during the initial phase. Second, we address the problem of learning generalizable models of collaboration. Machine learning models often suffer domain shifts; one advantage of encoding the semantic features is their adaptability across multiple domains. We evaluate the generalizability of different embeddings to other goal-oriented teamwork dialogues. Finally, in addition to identifying the features predictive of successful collaboration, we propose multi-feature embedding (MFeEmb) to improve the generalizability of collaborative task success prediction models under natural distribution shifts and resource scarcity. MFeEmb leverages the strengths of semantic, structural, and textual features of the dialogues by incorporating the most meaningful information from dialogue acts (DAs), sentiment polarities, and vocabulary of the dialogues. To further enhance the performance of MFeEmb under a resource-scarce scenario, we employ synthetic data generation and few-shot learning. We use the method proposed by Bailey and Chopra (2018) for few-shot learning from the FsText python library. We replaced the universal embedding with our proposed multi-feature embedding to compare the performance of the two. For data augmentation, we propose using synonym replacement from collaborative dialogue vocabulary instead of synonym replacement from WordNet. The research was conducted on several multiparty dialogue datasets, including ASIST, SwDA, Hate Speech, Diplomacy, Military, SAMSum, AMI, and GitHub. Results show that the proposed multi-feature embedding is an excellent choice for the meta-training stage of the few-shot learning, even if it learns from a small train set of size as small as 62 samples. Also, our proposed data augmentation method showed significant performance improvement. Our research has potential ramifications for the development of conversational agents that facilitate teaming as well as towards the creation of more effective social coding platforms to better support teamwork between software engineers
Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges and Future Directions
Sixth-generation (6G) networks anticipate intelligently supporting a wide
range of smart services and innovative applications. Such a context urges a
heavy usage of Machine Learning (ML) techniques, particularly Deep Learning
(DL), to foster innovation and ease the deployment of intelligent network
functions/operations, which are able to fulfill the various requirements of the
envisioned 6G services. Specifically, collaborative ML/DL consists of deploying
a set of distributed agents that collaboratively train learning models without
sharing their data, thus improving data privacy and reducing the
time/communication overhead. This work provides a comprehensive study on how
collaborative learning can be effectively deployed over 6G wireless networks.
In particular, our study focuses on Split Federated Learning (SFL), a technique
recently emerged promising better performance compared with existing
collaborative learning approaches. We first provide an overview of three
emerging collaborative learning paradigms, including federated learning, split
learning, and split federated learning, as well as of 6G networks along with
their main vision and timeline of key developments. We then highlight the need
for split federated learning towards the upcoming 6G networks in every aspect,
including 6G technologies (e.g., intelligent physical layer, intelligent edge
computing, zero-touch network management, intelligent resource management) and
6G use cases (e.g., smart grid 2.0, Industry 5.0, connected and autonomous
systems). Furthermore, we review existing datasets along with frameworks that
can help in implementing SFL for 6G networks. We finally identify key technical
challenges, open issues, and future research directions related to SFL-enabled
6G networks
Measuring the impact of COVID-19 on hospital care pathways
Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted
Book of cases on public and non-profit marketing: trends and responsible approaches in tourism
Debates around the negative impacts on natural and social environments are increasingly gaining relevance
in marketing strategies in different sectors, requiring the development of responsible approaches. Under
the theme “Trends and Responsible Approaches in Tourism”, the International Association of Public and
Non-Profit Marketing (AIMPN / IAPNM), together with the Faculty of Economics and the Research Centre in
Tourism, Sustainability and Well-being (CinTurs), University of Algarve, Portugal, organized the XIV
International Congress on Teaching Cases Related to Public and Non-profit Marketing.
The Congress took place on December 16, 2022, at the Faculty of Economics, University of Algarve, Portugal,
virtually. The objective of this annual Congress is to disseminate case studies referring to activities of non-
profit organizations, public institutions or companies.
This book presents 53 cases peer-reviewed by a scientific committee and selected from the presentations
performed by over 100 participants from diverse nationalities during this event. The Congress aims to
disseminate best practices referring to activities of non-profit organizations, public institutions and
companies and is addressed to students, teachers and professionals. Based on topics around non-profit,
social and public marketing, examples of good practices carried out by third-sector organizations,
companies and public organizations emerge. This approach, which is aligned with the United Nations’
Sustainable Development Goals, offers discussions supporting future marketing strategies that can
contribute to a better society.
The cases have been organized into seven main areas: 1) senior cases, 2) green marketing, 3) well-being,
marketing and tourism, 4) public and non-profit marketing, 5) responsible consumer behaviour trends and
tourism management, 6) social responsibility and sustainability, and 7) social marketing.info:eu-repo/semantics/publishedVersio
Multi-Asset Factor Investing Strategies and Controversy Screening using Natural Language Processing
Factor investing strategies have revolutionized the landscape of equity investing, and continues to be heavily researched by academics and practitioners, leading to the documentation of more than 450 factors. However, from a practical investment perspective, much of the factor evidence documented by academics may be more apparent than real. The performance of many factors has found to be dependent on the inclusion of small- and micro-cap stocks in academic studies, although such stocks would likely be excluded from the real investment universe due to illiquidity and transaction costs. We take the perspective of an institutional investor and navigate this zoo of factors by focusing on the evidence relevant to the practicalities of factor-based investment strategies. Establishing a sound theoretical rationale is key to identifying “true” factors, and we emphasize the need to recognize data-mining concerns that may cast doubt on the relevance of many factors. Nevertheless, a parsimonious set of factors emerges in equities and other asset classes, including currencies, fixed income and commodities. Since these factors can serve as meaningful ingredients to factor-based portfolio construction, we build currency factor strategies using the G10 currencies. We show that parametric portfolio policies can help guide an optimal currency strategy when tilting towards cross-sectional factor characteristics. While currency carry serves as the main return generator in this tilting strategy, momentum and value are implicit diversifiers to potentially balance the downside of carry investing in flight-to-quality shifts of foreign exchange investors. Drawing insights from a currency timing strategy, according to time series predictors, we further examine the parametric portfolio policy’s ability to mitigate the downside of the carry trade by incorporating an explicit currency factor timing element. This integrated approach to currency factor investing outperforms a naive equally weighted benchmark as well as univariate and multivariate parametric portfolio policies. Whilst factor investing continues to grow in popularity, investors have expressed interest in aligning their investments with social values in order to maximize positive social impact. Hence, for any company, involvement in socially unethical practices not only leads to reputational damage but also financial consequences, anecdotally. To quantify the consequence of such controversial behaviour, we investigate the price impact of involvement in social controversies and find that the returns drop, on an average, by over 200 basis in the days around the outbreak of news on social violations. We identify companies following socially unethical practices from news headlines with the help of state-of-the-art language modelling approaches. Using a large sample of 1 million news headlines, we further train and fine-tune a DistilRoBERTa model to identify reports of controversial incidents in daily news feed. We map the price reaction of such controversial events using an event study approach and document negative price impact for companies with poor social practices measured via increased controversial behaviour, largely driven by small to medium market capitalization companies. Amongst the eight different social dimensions we examine, controversies surrounding violations of product safety standards, online scams and data privacy breaches significantly impact firm returns. Dissecting this result by geographies, the U.S, Australia, Europe and Emerging Market react very negatively to social controversies
Metaverse. Old urban issues in new virtual cities
Recent years have seen the arise of some early attempts to build virtual cities,
utopias or affective dystopias in an embodied Internet, which in some respects appear to
be the ultimate expression of the neoliberal city paradigma (even if virtual). Although
there is an extensive disciplinary literature on the relationship between planning and
virtual or augmented reality linked mainly to the gaming industry, this often avoids design
and value issues. The observation of some of these early experiences - Decentraland,
Minecraft, Liberland Metaverse, to name a few - poses important questions and problems
that are gradually becoming inescapable for designers and urban planners, and allows
us to make some partial considerations on the risks and potentialities of these early virtual
cities
Use social media knowledge for exploring the portuguese wine industry: following talks and perceptions?
This work presents an exploratory study that retrieves, processes, and analyses Twitter data to gain insights about the relevance and perceptions of the wine industry in the Douro Portuguese region (including Porto and Douro wines), as well as other regions in the country. The main techniques and algorithms used in our work belong to the families of natural language processing and machine learning, and the practical relevance of the proposed methodology has been proven in the analysis of 1.2 million unique messages from more than 764,000 distinct users retrieved from the Twitter platform. Derived results from this study are valuable to provide insights that can be further used in the context of Business Informatics to promote better and more efficient marketing campaigns, for example, centering the topic on the most interested people or communicating with the most appropriate words.Fundação para a Ciência e a Tecnologia | Ref. UID/QUI/50006/2020Fundação para a Ciência e a Tecnologia | Ref. UIDB/04469/2020Fundação para a Ciência e a Tecnologia | Ref. SFRH/BD/145497/2019Xunta de Galicia | Ref. ED431C2018/55-GRCXunta de Galicia | Ref. ED431G2019/0
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