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Product returns are not only costly for e-tailers, but the unnecessary transports also impact the environment. Consequently, online retailers have started to formulate policies to reduce the number of returns. Determining when and how to act is, however, a delicate matter, since a too harsh approach may lead to not only the order being cancelled, but also the customer leaving the business. Being able to accurately predict which orders that will lead to a return would be a strong tool, guiding which actions to be taken. This paper addresses the problem of data-driven product return prediction, by conducting a case study using a large real-world data set. The main results are that well-calibrated probabilistic predictors are essential for providing predictions with high precision and reasonable recall. This implies that utilizing calibrated models to predict some instances, while rejecting to predict others can be recommended. In practice, this would make it possible for a decision-maker to only act upon a subset of all predicted returns, where the risk of a return is very high., ©2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This research is a part of the industrial graduate research school in digital retailing (INSiDR) at the University of Borås, funded by The Swedish Knowledge Foundation, grants nr. 20160035, 20170215., INSiDR

The industrial transition to Industrie 4.0 and subsequently Industrie 5.0 requires robots to be able to share physical and social space with humans in such a way that interaction and coexistence are positively experienced by the humans and where it is possible for the human and the robot to mutually perceive, interpret and act on each other's actions and intentions. To achieve this, strategies for humanrobot interaction are needed that are adapted to operators' needs and characteristics in an industrial context, i.e., Operator 5.0. This paper presents a research design for the development of a framework for human-robot interaction strategies based on ANEMONE, which is an evaluation framework based on activity theory, the seven stages of action model, and user experience (UX) evaluation methodology. At two companies, ANEMONE is applied in two concrete use cases, collaborative kitting and mobile robot platforms for chemical laboratory assignments. The proposed research approach consists of 1) evaluations of existing demonstrators, 2) development of preliminary strategies that are implemented, 3) re-evaluations and 4) cross-analysis of results to produce an interaction strategy framework. The theoretically and empirically underpinned framework-to-be is expected to, in the long run, contribute to a sustainable work environment for Operator 5.0.

The industrial evolutions require robots to be able to share physical and social space with humans in such a way that interaction and coexistence are positively experienced by human workers. A prerequisite is the possibility for the human and the robot to mutually perceive, interpret and act on each other's actions and intentions. To achieve this, strategies for human-robot interaction are needed that are adapted to operators' needs and characteristics in the industrial contexts. In this paper, we aim to present various taxonomies of levels of automation, human-robot interaction, and human-robot collaboration suggested for the envisioned factories of the future. Based on this foundation, we propose a compass direction for continued research efforts which both zooms in and zooms out on how to develop applicable human-robot interaction strategies that are worker-centric in order to obtain effective, efficient, safe, sustainable, and pleasant human-robot collaboration and coexistence.

The education of students to become competent user experience designers is a delicate matter as students need to obtain a multitude of knowledge, skills, and judgmental abilities. In this paper, our effort to manage this multiplicity in a bachelor’s program in user experience design is shared along with our experiences and teaching practices influenced by theories of situated and embodied cognition together with reflective teaching. The program was followed up through interviews with eight alumni and a company representative that employs user experience designers. The results show that the program overall works well, although some of the identified issues need to be addressed in the future. The interpretation is that our program curricula and teaching practices are fruitful, which hopefully can contribute to thoughts and discussions for other teachers in the field of user experience design and human-computer interaction., CC BY 4.0Correspondence: Beatrice Alenljung beatrice.alenljung@his.se

Customer churn is one of the most challenging problems for digital retailers. With significantly higher costs for acquiring new customers than retaining existing ones, knowledge about which customers are likely to churn becomes essential. This paper reports a case study where a data-driven approach to churn prediction is used for predicting churners and gaining insights about the problem domain. The real-world data set used contains approximately 200 000 customers, describing each customer using more than 50 features. In the pre-processing, exploration, modeling and analysis, attributes related to recency, frequency, and monetary concepts are identified and utilized. In addition, correlations and feature importance are used to discover and understand churn indicators. One important finding is that the churn rate highly depends on the number of previous purchases. In the segment consisting of customers with only one previous purchase, more than 75% will churn, i.e., not make another purchase in the coming year. For customers with at least four previous purchases, the corresponding churn rate is around 25%. Further analysis shows that churning customers in general, and as expected, make smaller purchases and visit the online store less often. In the experimentation, three modeling techniques are evaluated, and the results show that, in particular, Gradient Boosting models can predict churners with relatively high accuracy while obtaining a good balance between precision and recall., INSiDR

The coexistence of robots and humans in shared physical and social spaces is expected toincrease. A key enabler of high-quality interaction is a mutual understanding of each other’s actionsand intentions. In this paper, we motivate and present a systematic user experience (UX) evaluationframework of action and intention recognition between humans and robots from a UX perspective,because there is an identified lack of this kind of evaluation methodology. The evaluationframework is packaged into a methodological approach called ANEMONE (action and intentionrecognition in human robot interaction). ANEMONE has its foundation in cultural-historicalactivity theory (AT) as the theoretical lens, the seven stages of action model, and user experience(UX) evaluation methodology, which together are useful in motivating and framing the workpresented in this paper. The proposed methodological approach of ANEMONE provides guidanceon how to measure, assess, and evaluate the mutual recognition of actions and intentions betweenhumans and robots for investigators of UX evaluation. The paper ends with a discussion, addressesfuture work, and some concluding remarks., CC BY 4.0 This article belongs to the Special Issue Human-Robot Interaction and Sensors for Social Robotics

Design, textil och hållbar utveckling

In the present study, 64 users were asked to convey eight distinct emotion to a humanoid Nao robot via touch, and were then asked to evaluate their experiences of performing that task. Large differences between emotions were revealed. Users perceived conveying of positive/pro-social emotions as significantly easier than negative emotions, with love and disgust as the two extremes. When asked whether they would act differently towards a human, compared to the robot, the users’ replies varied. A content analysis of interviews revealed a generally positive user experience (UX) while interacting with the robot, but users also found the task challenging in several ways. Three major themes with impact on the UX emerged; responsiveness, robustness, and trickiness. The results are discussed in relation to a study of human-human affective tactile interaction, with implications for human-robot interaction (HRI) and design of social and affective robotics in particular., Design, Textil och hållbar Utveckling (VGR)

The purpose of this paper is to promote sustainability as an important research topic within the computer-supported collaborative learning (CSCL) community. CSCL can play a crucial role in the achievement of sustainability, which is paramount for the well-being of current and future generations. While CSCL brings formal educational settings to mind, computers and cooperative learning should be considered in a wider perspective since learning also takes place in and through people’s everyday practices. This paper considers two on-going research projects outside mainstream CSCL research, to illustrate ways that technology can lead to changed practices for the benefit of increased environmental and social sustainability. The projects concern children’s online practices and social sustainability, and information and communication technology (ICT) and practices in sustainable agriculture, respectively., KidCog

A fundamental challenge of human interaction with socially interactive robots, compared to other interactive products, comes from them being embodied. The embodied nature of social robots questions to what degree humans can interact ‘naturally' with robots, and what impact the interaction quality has on the user experience (UX). UX is fundamentally about emotions that arise and form in humans through the use of technology in a particular situation. This chapter aims to contribute to the field of human-robot interaction (HRI) by addressing, in further detail, the role and relevance of embodied cognition for human social interaction, and consequently what role embodiment can play in HRI, especially for socially interactive robots. Furthermore, some challenges for socially embodied interaction between humans and socially interactive robots are outlined and possible directions for future research are presented. It is concluded that the body is of crucial importance in understanding emotion and cognition in general, and, in particular, for a positive user experience to emerge when interacting with socially interactive robots.

Socially interactive robots are expected to have an increasing importance in everyday life for a growing number of people, but negative user experience (UX) can entail reluctance to use robots. Positive user experience underpins proliferation of socially interactive robots. Therefore, it is essential for robot developers to put serious efforts to attain social robots that the users experience as positive. In current human-robot interaction (HRI) research, user experience is reckoned to be important and is used as an argument for stating that something is positive. However, the notion of user experience is noticeably often taken for granted and is neither described nor problematized. By recognizing the complexity of user experience the intended contributions can be even more valuable. Another trend in HRI research is to focus on user experience evaluation and examination of user experience. The current research paths of user experience of socially interactive robots are not enough. This chapter suggests that additional research directions are needed in order accomplish long-term, wide-spread success of socially interactive robots.

Complex decision-making is a prominent aspect of requirements engineering (RE) and the need for improved decision support for RE decision-makers has been identified by a number of authors in the research literature. Decision-supporting features and qualities can be integrated in RE tools. Thus, there is a need to evaluate the decision-supporting capabilities of RE tools. In this paper, we introduce a summative, criteria-based evaluation method termed DESCRY, which purpose is to investigate to what extent RE tools have decision-supporting capabilities. The criteria and their related questions are empirically as well as theoretically grounded.

Complex decision-making is a prominent aspect of requirements engineering and the need for improved decision support for requirements engineers has been identified by a number of authors. A first step toward better decision support is to understand decision-makers’ complex decision situations. Aspects of decision situations are decision matters, decision-making activities, and decision processes. In this paper we present two requirements engineering decision processes and their decision-making activities and decision matters. These were identified in a case study conducted at Ericsson Microwave Systems. We also discuss the consequences of these for requirements engineering decision support.

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Complex decision-making is a prominent aspect of requirements engineering and the need for improved decision support for requirements engineers has been identified by a number of authors. A first step toward better decision support in requirements engineering is to understand decision-makers’ complex decision situations. To gain a holistic perspective of the decision situation from a decision-makers perspective, a decision situation framework has been created. The framework evolved through a literature analysis of decision support systems and decision-making theories. The decision situation of requirements engineers has been studied at Ericsson Microwave Systems and is described in this thesis. Aspects of decision situations are decision matters, decision-making activities, and decision processes. Another aspect of decision situations is the factors that affect the decision-maker. A number of interrelated factors have been identified. Each factor consists of problems and these are related to decision-making theories. The consequences of this for requirements engineering decision support, represented as a list that consists of desirable high-level characteristics, are also discussed.