Abstract

Abstract
Innovation capabilities are among the main driving sources of export performance; however, the literature on how exploration and exploitation innovation influence export performance in the context of emerging economies is scarce. As such, the purpose of this paper is to assess the relationship between innovation capabilities and export performance, mediated by exploration and exploitation innovation. For that, an online questionnaire was implemented surveying 250 Mozambican Small and Medium Enterprises (SMEs) in the agro-industry, agro-processing, and fishing sectors. Based on a Partial Least Squares Structural Equation Model (PLS-SEM) relating innovation capabilities, exploitation, and exploration innovation to export performance, it is possible to state that innovation capabilities influence the export performance of SMEs in this emerging country. Moreover, exploration and exploitation innovation exert important mediation effects, the latter being more prevalent than the former. The results constitute a major contribution as it is possible to claim that, in the context of emerging economies, Mozambican SMEs have managed to enhance their export performance, based on innovation capabilities, but, also, to master their ambidexterity with exploiting capabilities, normally linked to their current technological trajectories, being more prevalent that exploitation capabilities, which are focused on the pursuit of radical innovation. © 2022 by the authors.

Abstract
The digital transformation associated with the huge volume of data that healthcare organizations deal with today is based on transforming this complex knowledge-driven industry to turn data into knowledge. The healthcare industry requires comprehensive models that help identify priorities to implement business intelligence (BI) solution. Business intelligence can help organizations make better decisions by showing current and historical data within their business context. This paper systematizes and analyzes three business intelligence maturity models into one and also attempts to understand the main key performance indicators in adopting business intelligence maturity model in healthcare organizations. For this purpose, we present a questionnaire that was based on the systemized business intelligence maturity model that was sent to X% of the Portuguese hospitals with the objective of identifying not only the business intelligence maturity stage of the Portuguese hospitals but also to infer the most important key performance indicators that will characterize each stage. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
Spotify is a world class platform for music streaming, and it offers various kinds of services. As with many digital platforms, Spotify uses artificial intelligence to personalize the user experience, also known as a recommender system. This study investigates what role Spotify’s recommender system plays in the use of Spotify and if there are any differences in satisfaction between different ages and gender. Therefore, we conducted a survey about how customers are using Spotify, which was shared in different forums. In total we received 159 answers with respondents from 21 different countries. One of the main findings was that the three services “Make your own playlist”, “Playlist made by Spotify” and “Recommended songs” are the most popular. Also, a correlation was made to investigate the relationship between the satisfaction of “Recommended songs” and if customers add them to their own playlists. The Spearman’s Rank Correlation Coefficient was 0.43 (significant at the 0.01 level), which is a moderate value. This means that almost half of the time the Spotify users place the recommended songs in their playlist. Further, two conclusions were arrived at. Firstly, the recommender system plays a major part in how customers use Spotify. Secondly, we cannot see that age and gender would significantly affect the satisfaction of the recommended songs that Spotify suggest. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract

Abstract
The advantages associated with mobile ticketing solutions are undeniable; however, most of these solutions are designed for the local population without taking into account the specific needs of tourists. Therefore, this study fills an important research gap in the literature by assessing the adoption drivers of mobile ticketing services by tourists and pointing out possible directions to the design of such services. The proposed model includes constructs of the technology acceptance model (TAM), diffusion of innovations (DOI) theory, and others widely disseminated in the literature on mobile payments, such as mobility. The model was empirically tested through an online survey, and Structural Equation Modeling (SEM) was applied to analyze the data. The results show that the intention of tourists to use mobile ticketing services is positively affected by the perceived usefulness and mobility. The survey findings also describe additional services that respondents value in a mobile ticket service for tourists, both in normal and in pandemic contexts, useful to shape future mobile ticketing solutions for tourists.