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Studies dedicated to user's adoption, acceptance, and use of IT have been rampant since the advent of computers and IT. Therefore, it becomes essential to assess the value of IT to organizations and to understand the determinants of such value in terms of acceptance, integration, and technology use. In this regard, Enterprise Resource Planning (ERP) adoption remains challenging for professionals and practitioners alike. Previous research neglected many vital factors, such as user involvement (participation, engagement, and resilience) and environmental factors (change management, competitiveness, and pandemic pressure). Therefore, this study aims to identify the most significant factors that influence the successful adoption of ERP and propose a conceptual model. The Technology Acceptance Model with DeLone and McLean Information Success Models were the base for the study. The findings of this study reveal a conceptual model that facilitates the successful adoption of ERP systems. Through the identification of critical factors, one factor emerged as a permanent component in the adoption process. This conceptual model provides valuable insights for organizations seeking to implement and adopt ERP systems effectively. The study's findings offer valuable insights for firms pursuing long-term economic success through ERP adoption. Understanding the influencing factors enables efficient and effective deployment. Implications are relevant to organizations, ERP vendors, and decision-makers, guiding informed decision-making and prioritizing success factors.
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In this study, we propose a new method to detect outlying observations in spherical data. The method is based on the k-nearest neighbours distance theory. The proposed method is a good alternative to the existing tests of discordancy for detecting outliers in spherical data. In addition, the new method can be generalized to identify a patch of outliers in the data. We obtain the cut-off points and investigate the performance of the test statistic via simulation. The proposed test performs well in detecting a single and a patch of outliers in spherical data. As an illustration, we apply the method on an eye data set.
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Simulación por ComputadorRESUMEN
UNLABELLED: Kidney cancer accounts for approximately 2% of all cancers worldwide, with renal cell carcinoma being the most common form and this report is focused on renal cell carcinoma. Globally, the incidence and mortality rates are increasing by 2-3% per decade. Kidney cancer is less common in Asia compared with the West. Cigarette smoking, obesity, acquired cystic kidney disease and inherited susceptibility are known risk factors for kidney cancer. The National Comprehensive Cancer Network Guidelines recommend surgical excision as first line of treatment for Stage I, II or III kidney cancer patients and Stage IV patients with resectable tumours. Immunotherapy has a 20-year history in treatment of metastatic kidney cancer. High-dose interleukin-2 (IL-2) is administered in some countries, whereas low-dose IL-2 and interferon-alpha (IFN-α) are popular in Japan. Molecular-targeted drugs, including sunitinib, bevacizumab and sorafenib, are being used for previously untreated and refractory patients. Asian and non-Asian populations have shown large differences in the incidences of adverse events with sorafenib and sunitinib. CONSENSUS STATEMENT: Kidney cancer is relatively uncommon in Asia compared with the West, but its incidence is increasing in more developed Asian nations. Guidelines from the National Comprehensive Cancer Network , etc., for treating metastatic renal cell carcinoma are based on Phase III clinical trials conducted primarily in Western patients. Targeted therapies are now becoming primary recommendations, but efficacy/toxicity data from Asian patients are lacking. Some drugs cause adverse effects in Asians because their recommended dosages are optimal for Caucasians but may be too high for Asians. Further research is necessary to develop optimal treatment strategies for Asians.