CLASSIFICATION OF WEARABLE ELECTRONIC DEVICES

Background. Today smart watches, fitness bracelets, smart rings are must-have accessory for everyone who cares about their health. It is projected that the average annual growth of the wearable device market in 2021–2026 will be 18 %, which is respectively reflected in this segment of the Ukrainian market. Well-known electronics stores use different approaches to group the range of wearable devices, as there are no single standards to classify these products. The aim of this article is to develop a classification of wearable devices and to identify the classification featuresfor smart watches and fitness bracelets based on the analysis of the assortment presented in online stores. Materials and methods. Methods of logical analysis, generalization of scientific literature, statistical data of export and import of wearable devices were applied. Data on their assortment and grouping in well-known retail chains were used to create a classification. Results. Based on the analysis of the world market of electronic goods and the assortment of well-known retail chains, the authors propose a classification of goods related to wearable devices. In particular, there is a division of wearable devices into 7 groups (wrist devices, head devices, smart clothes, smart shoes, smart jewelry, wearable devices, medical devices), these groups include subgroups, categories and subcategories. Only a few types of wearable devices are sold on the Ukrainian market – smart watches, fitness bracelets, virtual reality glasses and smart rings. However, only two retail chains allocate these products separately in the product group "Wearable Products", the others form a large product group "Gadgets…", which according to the authors is not entirely correct, as the latter differ significantly in purpose and characteristics. Since the range of smart watches and fitness bracelets is quite wide and includes hundreds of types, it is proposed to use a number of classification features that clearly distinguish them by their functionality. Conclusion. With the COVID-19 pandemic, the wearable devices market seg­ment will continue to grow. Restrictions on mobility and an individual’s desire to monitor vital signs of their health during a pandemic will be the main factors that will influence the market for these devices. The classification of goods related to wearable devices has been developed. The classification features for smart watches and fitness bracelets, the range of which includes hundreds of types, are proposed. It is established that the main difference between smart watches and a fitness bracelets is a wider functionality of the first and a much longer battery life of the latter.

The Role of Cause Involvement and Assortment Size on Decision Difficulty via Communal Relationships

Social enterprises aim to achieve both social and economic goals by reaching broader consumer segments through extensive assortments, but research into how this product proliferation strategy affects consumer response is scarce. In the current research we examine how consumers judge social enterprises providing large product assortments. Three experiments show that choice overload (i.e., having a decision difficulty when faced with many options) can be reversed among target consumers of social enterprises—specifically those whose involvement in a social cause is high. Because more-involved consumers view large assortments of cause-related products as an indicator of the company’s commitment to addressing social issues, they identify with the company and thereby form communal relationships. Thus, the consumers’ focus shifts from comparing options to helping the company, leading to reduced decision difficulty. The findings contribute to existing research on assortment size and the understanding of the information consumers use to evaluate the company’s commitment to social causes.

EBCR: Empirical Bayes concordance ratio method to improve similarity measurement in memory-based collaborative filtering

Recommender systems aim to provide users with a selection of items, based on predicting their preferences for items they have not yet rated, thus helping them filter out irrelevant ones from a large product catalogue. Collaborative filtering is a widely used mechanism to predict a particular user’s interest in a given item, based on feedback from neighbour users with similar tastes. The way the user’s neighbourhood is identified has a significant impact on prediction accuracy. Most methods estimate user proximity from ratings they assigned to co-rated items, regardless of their number. This paper introduces a similarity adjustment taking into account the number of co-ratings. The proposed method is based on a concordance ratio representing the probability that two users share the same taste for a new item. The probabilities are further adjusted by using the Empirical Bayes inference method before being used to weight similarities. The proposed approach improves existing similarity measures without increasing time complexity and the adjustment can be combined with all existing similarity measures. Experiments conducted on benchmark datasets confirmed that the proposed method systematically improved the recommender system’s prediction accuracy performance for all considered similarity measures.

De robotarm als collega

SUMMARY Dutch industrial manufacturers are confronted with a new and promising industrial robot: the collaborative robot (cobot). These small robotic arms are revolutionary as they allow direct and safe interaction with production workers for the very first time. The direct interaction between production worker and cobot has the potential to not only increase efficiency, but also enhance flexibility as it can align the strengths of (wo)man and machine more thoroughly. Currently, Dutch manufacturers are experimenting with cobots. To obtain a first understanding about the use of cobots in Dutch industrial practice and to see what the consequences are for production workers and production work, we conducted an exploratory interview study (N=61). We learnt that most cobots under study are used for the production of one or a few large product batches (mass production) and work highly autonomous. The interaction between cobot and production worker is limited and reduced to production workers preventing the cobot from falling into a standstill. The results tend to be in line with traditional industrial automation practices: an overemphasis on leveraging the technology’s potential and limited attention for the production workers’ work design and decision latitude. HR professionals were not involved and, therefore, miss out on a crucial opportunity to be of an added value.

Influencing the forming zone by altering the contact pressure in a bending process

In a modern production environment, flexible manufacturing methods are important because an overall trend towards mass customization and on-demand production is observed. Kinematic and incremental forming methods with generic tools can provide a large product variation but a deeper understanding of the forming mechanisms is required for process modelling, e.g. Incremental Swivel Bending (ISB). Particularly crucial is to identify the influences on the forming zone in order to purposefully control the material flow of a forming process. For a bending process where the bending moment is transmitted by clamping tools, this paper presents a method to alter the contact pressure distribution in order to affect the angular size and strain gradient of the forming zone. In the light of these results, the presented method can be deployed for a tooling with adaptive contact pressure to directly influence material flow, in particular using generic tools to overall provide a better control of flexible forming methods.

Product Choice with Large Assortments: A Scalable Deep-Learning Model

Personalized marketing in retail requires a model to predict how different marketing actions affect product choices by individual customers. Large retailers often handle millions of transactions daily, involving thousands of products in hundreds of categories. Product choice models thus need to scale to large product assortments and customer bases, without extensive product attribute information. To address these challenges, we propose a custom deep neural network model. The model incorporates bottleneck layers to encode cross-product relationships, calibrates time-series filters to capture purchase dynamics for products with different interpurchase times, and relies on weight sharing between the products to improve convergence and scale to large assortments. The model applies to loyalty card transaction data without predefined categories or product attributes to predict customer-specific purchase probabilities in response to marketing actions. In a simulation, the proposed product choice model predicts purchase decisions better than baseline methods by adjusting the predicted probabilities for the effects of recent purchases and price discounts. The improved predictions lead to substantially higher revenue gains in a simulated coupon personalization problem. We verify predictive performance using transaction data from a large retailer with experimental variation in price discounts. This paper was accepted by Gui Liberali, Management Science Special Issue on Data-Driven Prescriptive Analytics.

Understanding Large-Scale Dynamic Purchase Behavior

A scalable model-based approach to gain insights in dynamic purchase behavior for large product assortments and customer bases.

A Josephson junction based on a highly disordered superconductor/low-resistivity normal metal bilayer

We calculate the current–phase relation (CPR) of a SN-S-SN Josephson junction based on a SN bilayer of variable thickness composed of a highly disordered superconductor (S) and a low-resistivity normal metal (N) with proximity-induced superconductivity. In such a junction, the N layer provides both a large concentration of phase in the weak link and good heat dissipation. We find that when the thickness of the S and the N layer and the length of the S constriction are about the superconducting coherence length the CPR is single-valued and can be close to a sinusoidal shape. The product I c R n can reach Δ(0)/2|e| (I c is the critical current of the junction, R n is its normal-state resistance, Δ(0) is the superconductor gap of a single S layer at zero temperature). Our calculations show, that the proper choice of the thickness of the N layer leads both to nonhysteretic current–voltage characteristics even at low temperatures and a relatively large product I c R n.

Super Size Me: An Unsuccessful Preregistered Replication of the Effect of Product Size on Status Signaling

Dubois, Rucker, and Galinsky (2012, Experiment 1) found that consumers view larger-size options as a signal of higher status. We conducted a close replication of this finding (N = 415), and observed a nonsignificant effect in the opposite direction (small vs. large product size: doriginal = 1.49, 95%CI [1.09, 1.89], dreplication = 0.09 95%CI [-0.15, 0.33]; medium vs. large: doriginal = 0.89 95%CI [0.52, 1.26], dreplication = 0.11 95%CI [-0.13, 0.34]; small vs. medium: doriginal = 0.62 95%CI [0.26, 0.98], dreplication = -0.01 95%CI [-0.25, 0.23]). We discuss potential reasons for this unsuccessful replication as well as implications for the status-signaling literature in consumer psychology