ABSTRACT
Organisations undertake profound changes to fit in a rapidly evolving digital setting. However, although the IT capabilities of the organisational members play a critical role in this, the mechanism driving IT capabilities towards enhanced firm performance is not fully understood. A theoretical model to analyse the role of digital orientation and digital transformation in this relationship is introduced and tested on a set of 246 firms through the Partial Least Squares-Structural Equation Modeling method (PLS-SEM). This research contributes to the literature by introducing the social aspect to the study of technology management, delving also into the antecedents of digital transformation. Results confirm a positive effect of IT capabilities on firm performance through the development of a digital orientation and the digital transformation of the organisation.
ABSTRACT
On page 646, Section 'RESULTS',On paragraph 'Model estimation and selection',Line 3, where it reads (in red):Firstly, we examined fit statistics (Table 5), namely the Akaike Information criterion (AIC) (...)It should read (in blue):Firstly, we examined fit statistics (Table 3), namely the Akaike Information criterion (AIC) (...)Line 9, where it reads (in red):(...) (LRT = 57.33, p < 0.0001, see Table 5) (...)It should read (in blue):(...) (LRT = 57.33, p < 0.0001, see Table 3) (...)On paragraph 'Classification accuracy of the model',Line 1, where it reads (in red):The probabilities of correct classification of observations are shown in the main diagonal of Table 6, (...)It should read (in blue):The probabilities of correct classification of observations are shown in the main diagonal of Table 4, (...)Line 7, where it reads (in red):The classification accuracy of the testing subsample was 96%, as shown in Table 7.It should read (in blue):The classification accuracy of the testing subsample was 96%, as shown in Table 5.On page 647,Chapter Description of profiles, 2nd paragraph, line 4, where it reads (in red):This group scores negatively (less than 2.5, below the green, dotted bottom line) in all dimensions (Table 3), (...)It should read (in blue):This group scores negatively (less than 2.5, below the green, dotted bottom line) in all dimensions (Table 6), (...)On page 648,Line 6, where it reads (in red):(...) equal parental control rates or absence thereof (Table 4).It should read (in blue):(...)equal parental control rates or absence thereof (Table 7).2nd paragraph, line 9, where it reads (in red):(...) compared with other profiles, are noteworthy (Table 4).It should read (in blue):(...) compared with other profiles, are noteworthy (Table 7).3rd paragraph, line 7, where it reads (in red):Here we also highlight users with the least difficulty in making friends (Table 4).It should read (in blue):Here we also highlight users with the least difficulty in making friends (Table 7).4th paragraph, line 10, where it reads (in red):(...) and lower parental control rate stood out compared with the other profiles (Table 4).It should read (in blue):(...) and lower parental control rate stood out compared with the other profiles (Table 7).Article published with errors: https://www.actamedicaportuguesa.com/revista/index.php/amp/article/view/17047.
ABSTRACT
INTRODUCTION: Addictive use of the Internet among adolescents has been linked to a negative psychosocial development, but more detailed information about Internet addiction (IA) profiles is warranted. The aim of this study was to identify IA profiles in adolescents based on psychometric properties from the Internet Addiction test (IAT), and to assess the associations between the profiles and personal/social behaviors. MATERIAL AND METHODS: A cross-sectional study was performed at public schools from a Portuguese region, using a survey that included the IAT. We performed a latent profiling analysis to identify the profiles of adolescent based on the six IAT dimensions. RESULTS: From the 1915 responses, students' mean age was 15 ± 1.82 years, 53% were female. IA was found in 16.5%. Four models were estimated with latent profiling analysis. Analysis of the models by fit statistics, integrated completed likelihood and Lo-Mendell-Rubin likelihood ratio test, indicated a better solution with four profiles: Profile 1 - Worrisome lack of control users, Profile 2 - Balanced users, Profile 3 - Worrisome anticipation users, Profile 4 - Problematic users. CONCLUSION: This study provides a characterization of different patterns in adolescents' traits and behaviors associated with Internet addiction. Preventive approaches may be useful to reduce IA.
Introdução: A dependência da Internet em adolescentes tem sido associada a problemas no seu desenvolvimento psicossocial. Porém, a literatura carece de dados sobre diferentes perfis do uso de Internet. Este estudo pretendeu identificar perfis de dependência de Internet (DI), baseado nas características psicométricas do Internet Addiction test (IAT), verificando associações entre os perfis e comportamentos sociais. Material e Métodos: Estudo transversal realizado em escolas públicas de uma região Portuguesa mediante questionário que incluiu o IAT. Realizou-se uma análise de perfis latentes (APL) para identificar perfis de adolescentes, com base nos seis domínios do IAT. Resultados: Dos 1915 participantes, a idade média foi 15 ± 1,82 anos; 53% eram do sexo feminino. Identificou-se DI em 16,5%. A análise de modelos por qualidade de ajuste e rácio de verossimilhança de Lo-Mendell-Rubin revelou um modelo adequado com 4 perfis: 1 Utilizadores com dificuldade de controlo; 2 Utilizadores equilibrados; 3 Utilizadores com problemas de antecipação; 4 Utilizadores problemáticos. Conclusão: Este estudo permitiu a caracterização de diferentes padrões e comportamentos de adolescentes na DI, pelo que se alerta para uma abordagem preventiva na redução da DI.
