Employees and their knowledge are critical for a company's success and were extensively covered by literature. Free form of knowledge, as opinions or feedback is analyzed in some companies in order to understand potential areas of improvement or satisfaction. This research focuses on analyzing free format opinions collected with a survey instrument from 586 employees of a bank. Various techniques as text analytics, word embedding, supervised and unsupervised learning were explored, extracting key concepts or entities. Attribution and relational similarity was explored using techniques as supervised text classification or unsupervised, by word vector representation and embedding using word2vdec. The aim was to explain overall satisfaction combining structured data (collected with closed questions survey, using 7 points Likert scale) enhanced with text classification of opinions, related to best and worst applications, along with explanations. The theoretical model used for ontology and taxonomy of text classification was based on Technology Acceptance Model, mapped into the main perceptual constructs, Perceived Ease of Use and Perceived Utility. The scope of research was the banks' enterprise IT environment, not focused on a specific application. Various quantitative models, including linear models, decision trees and neural nets, were evaluated to capture potential causality of overall employee IT satisfaction level. The results suggest strong influence of perceptual constructs towards satisfaction, within all methods, while unstructured textual data provide additional insights on employees' perception from concept associations.
Employee IT user satisfaction is important for companies, especially in regulated industries as financial services, especially for workers interacting directly with the clients. This paper analyses the impact of various factors against rating given for IT User Satisfaction in a bank. In retail banking, customer-facing employees need to provide service for simple or complex transaction, as well as financial advice. We found that IT user experience is influenced positively by the trust in organization willingness to change based on the user's feedback as well as the support provided as helpdesk but negatively impacted by multiple application performance, stability issues or infrastructure performance, as part of the expected value of using IT as a job support tool. Qualitative-exploratory and quantitative research was performed using techniques as segmentation, decision tree or multinomial linear regression. Data analysis was performed on 608 survey responses out of a population of 3000 individuals. Transcript data collected during interviews was processed using natural language processing technics in Python in parallel to human driven classification to provide additional potential insight as part of content analysis phase (clustering of keywords based on tfidf vectors scores, extraction of most relevant words for clusters). We found that textual data are very powerful especially when using visualization but in general due limited corpus size and bias from selection process in interviews would be useful to collect more data, maybe from helpdesk system and email communication for IT support. Theoretical and practical implications are discussed through the lens of the Technology Acceptance Model to explain the impact of the main factors influencing the perception of the overall IT landscape.
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