Abstract
PurposeWhile academics and practitioners increasingly recognize the impacts of gamification on customer experience (CX), its role in the customer journey remains undeveloped. This article aims to identify how gamification can leverage each customer journey stage, integrate the findings into a conceptual model and propose future research opportunities.Design/methodology/approachSince CX and customer journey are interrelated concepts, the authors rely on CX research to identify research themes that provide insights to propose the conceptual model. A systematic review of 154 articles on the interplay between gamification and CX research published from 2013 to 2022 was performed and analyzed by thematic content analysis. The authors interpreted the results according to the service customer journey stages and the taxonomy of digital engagement practices.FindingsThis article identified five main thematic categories that shape the conceptual model (design, customer journey stages, customer, technology and context). Gamification design can support customer value creation at any customer journey stage. While gamification can leverage brand engagement at the pre-service stage by enhancing customer motivation and information search, it can leverage service and brand engagement at the core and post-service stages by enhancing customer participation and brand relationships. Moreover, customer-, technology- and context-related factors influence the gamified service experience in the customer journey.Originality/valueThis article contributes to a conceptual integration between gamification and customer journey. Additionally, it provides opportunities for future research from a customer journey perspective.

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This study presents the SENSOMATT sensor sheet, a novel, non-invasive pressure monitoring technology intended for placement beneath a mattress. The development and design process of the sheet, which includes a novel sensor arrangement, material selection, and incorporation of an elastic rubber sheet, is investigated in depth. Highlighted features include the ability to adjust to varied mattress sizes and the incorporation of AI technology for pressure mapping. A comparison with conventional piezoelectric contact sensor sheets demonstrates the better accuracy of the SENSOMATT sensor for monitoring pressures beneath a mattress. The report highlights the sensor network's cost-effectiveness, durability, and enhanced data measurement, alongside the problems experienced in its design. Evaluations of performance under diverse settings contribute to a full understanding of its potential pressure injury prediction and patient care applications. Proposed future paths for the SENSOMATT sensor sheet include clinical validation, more cost and performance improvement, wireless connection possibilities, and improved long-term monitoring data analysis. The study concludes that the SENSOMATT sensor sheet has the potential to transform pressure injury prevention techniques in healthcare.

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Interactive Voice Assistants (IVAs) are intelligent conversational agents capable of communicating with users using natural language. Although IVAs are more frequent in our lives, customer experience research with these agents is still in its infancy. This article aims to identify the factors that form the customer experience (CX) with Alexa and assesses its impact on traditional marketing outcomes: satisfaction and recommendation. This research presents a conceptual model of CX with IVAs and an empirical validation of the model using Structural Equation Modelling based on a sample of 580 IVA users. The results confirm that CX is a multidimensional higher-order construct composed of six factors (usefulness, ease of use, trust, privacy concerns, communication skills, and enjoyment). We also highlight the positive impact of experience on satisfaction and recommendation. Finally, we test the enthusiasm moderating role, showing its negative influence on the investigated relationships. Theoretical and practical implications are discussed.

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The Atacama desert is a region with exceptional conditions for solar power production. However, despite its relevance, the impact of climate change on this resource in this region has barely been studied. Here, we use regional climate models to explore how climate change will affect the photovoltaic solar power resource per square meter (PVres) in Atacama. Models project average reductions in PVres of 1.5% and 1.7% under an RCP8.5 scenario, respectively, for 2021-2040 and 2041-2060. Under RCP2.6 and the same periods, reductions range between 1.2% and 0.5%. Also, we study the contribution to future changes in PVres of the downwelling shortwave radiation, air temperature and wind velocity. We find that the contribution from changes in wind velocity is negligible. Future changes of downwelling shortwave radiation, under the RCP8.5 scenario, cause up to 87% of the decrease of PVres for 2021-2040 and 84% for 2041-2060. Rising temperatures due to climate change are responsible for drops in PVres ranging between 13%–19% under RCP2.6 and 14%–16% under RCP8.5. We conclude that climate change has the potential to impact the PVres in the Atacama region while retaining exceptional conditions for solar power production.

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Plant diseases and pests significantly influence food production and the productivity and economic profitability of agricultural crops. This has led to great interest in developing technological solutions to enable timely and accurate detection. This systematic review aimed to find studies on the automation of processes to detect, identify and classify diseases and pests in agricultural crops. The goal is to characterize the class of algorithms, models and their characteristics and understand the efficiency of the various approaches and their applicability. The literature search was conducted in two citation databases. The initial search returned 278 studies and, after removing duplicates and applying the inclusion and exclusion criteria, 48 articles were included in the review. As a result, seven research questions were answered that allowed a characterization of the most studied crops, diseases and pests, the datasets used, the algorithms, their inputs and the levels of accuracy that have been achieved in automatic identification and classification of diseases and pests. Some trends that have been most noticed are also highlighted.