A Customer-Complaint Analyzer for E-Banking Services: The Context of the Ghanaian Banking Industry

Banking and financial institutions continue to intensify their efforts to engage in technological innovations in the provision of quality e-banking products and services. With this strategic approach, many banks in Ghana have migrated from the traditional and rudimentary branch banking to web-based banking transactions. This paper develops a model for a web-based customer-complaint analyzer that addresses customer complaints or suggestions in real time as well as supporting decision making processes of banks and other financial institutions. The exploratory prototype model, context diagram and UML use-case diagram were used to simplify and explain the design and development phases of the system. Both alpha and beta tests were done at the Ghana Commercial Bank and the United Bank for Africa (UBA) Ghana Limited of the KNUST Branch in Kumasi, Ghana. It is very expedient on the part of banks in Ghana to use complaint analyzer system to enable them do analyses on customers’ complaints or suggestions as well as on performance for improved and better service delivery. Keywords: e-banking, analytical performance, customer complaints analyzer, banking industry, customer service deliver

The Ecosystem of Internal Consultants as a Structure: A Contradictory Approach to Leverage the Imbalance between Internal and External Consultants

Consultancy has become a characteristic phenomenon to many organizations in Ghana whether small or large. Organizational policies and programmes attempt to re-align consultancy issues to determine when a planned change is necessary to embark upon, and the key resources that will be employed in due course. The ecosystem of the internal consultants is though complex, it is relatively supportive in terms of planned change. This paper proposes a model that paradoxically leverages the imbalance between the internal and external consultants for same change initiative. The model was empirically experimented in College of Technology Education, Kumasi in which planned changes are pronounced. Questionnaires were used to obtain information about staffs’ confidence and acceptance when working with either internal consultant or external consultant given some critical project. It was established that the consulting process regarding level of professionalism and realization of change outcomes were also optimal using internal consultants for the same planned change agenda. Keywords: ecosystem, planned change, internal consultant, Internal-External Ecosystem Mode

A Monitoring and Control System for Micro and Small Enterprises: The Use of RUMSEG at the District Level in Ghana

Rural folks in many districts in Ghana engage in various profit-making businesses which range from traditionally skilled-based manufacturing to retailing businesses. As a result, local government authorities together with stakeholders such as Rural Enterprise Project (REP) are interested in monitoring the development tendencies of these trade categories at their micro and small-scale levels. This paper comes out with Rural Micro and Small Scale Enterprises Growth (RUMSEG) tool that enables District Assemblies (DAs) to monitor and evaluate growth performances of Micro and Small-scale Enterprises (MSEs) at the district level, and serve as an aid to revenue mobilization. Beta testing and the agile iterative method were employed during modules testing and with a backend relational database to store client’s information. RUMSEG was tested at the Business Advisory Centres (BACs) of Asuogyaman and Atwima Nwabeagya District Assemblies in Ghana. Aided by the Enterprise Monitoring Diary (EMD), RUMSEG produced differences in clients’ growth performances in the context of turning actual cost of training by stakeholders into actual outputs of skills, abilities and competencies. Keywords: growth performances, trade categories, stakeholders, RUMSEG, District Assemblies (DAs)

Data Readiness and Data Exploration for Successful Power Line Inspection

Sufficiently large, curated, and representative training data remains key to successful implementation of deep learning applications for wide-scale power line inspection. However, most researchers have offered limited insight regarding the inherent readiness of the knowledge bases that drives power line algorithm development. In most cases, these high dimensional datasets are also unexplored before modeling. In this article, power line image data readiness (PLIDaR) scale for AI algorithm development is proposed. Using the PLIDaR benchmark, this study analyzes the fundamental steps involved in preparing overhead transmission power line (OTPL) insulator image data for deep supervised learning algorithm development. Data visualization approach is implemented by reengineering the ground truth instance annotations of two recent public insulator datasets, while exploratory data analysis is also employed by implementing a robust dimensionality reduction technique to optimize construction, visualization, clustering, and analysis of these recent insulator datasets in a lower dimensional space. The implementations reveal representational variabilities and hidden patterns that could be exploited to improve data quality before predictive modeling. Moreover, the visualizations from dimensionality reduction technique have potential to help develop classifiers that are more reliable

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Precursors of Role-Based Access Control Design in KMS: A Conceptual Framework

Role-based access control (RBAC) continues to gain popularity in the management of authorization concerning access to knowledge assets in organizations. As a socio-technical concept, the notion of role in RBAC has been overemphasized, while very little attention is given to the precursors: role strain, role ambiguity, and role conflict. These constructs provide more significant insights into RBAC design in Knowledge Management Systems (KMS). KMS is the technology-based knowledge management tool used to acquire, store, share, and apply knowledge for improved collaboration and knowledge-value creation. In this paper, we propose eight propositions that require future research concerning the RBAC system for knowledge security. In addition, we propose a model that integrates these precursors and RBAC to deepen the understanding of these constructs. Further, we examine these precursory constructs in a socio-technical fashion relative to RBAC in the organizational context and the status–role relationship effects. We carried out conceptual analysis and synthesis of the relevant literature, and present a model that involves the three essential precursors that play crucial roles in role mining and engineering in RBAC design. Using an illustrative case study of two companies where 63 IT professionals participated in the study, the study established that the precursors positively and significantly increase the intractability of the RBAC system design. Our framework draws attention to both the management of organizations and RBAC system developers about the need to consider and analyze the precursors thoroughly before initiating the processes of policy engineering, role mining, and role engineering. The propositions stated in this study are important considerations for future work

Evaluation of Tree-Based Ensemble Machine Learning Models in Predicting Stock Price Direction of Movement

Forecasting the direction and trend of stock price is an important task which helps investors to make prudent financial decisions in the stock market. Investment in the stock market has a big risk associated with it. Minimizing prediction error reduces the investment risk. Machine learning (ML) models typically perform better than statistical and econometric models. Also, ensemble ML models have been shown in the literature to be able to produce superior performance than single ML models. In this work, we compare the effectiveness of tree-based ensemble ML models (Random Forest (RF), XGBoost Classifier (XG), Bagging Classifier (BC), AdaBoost Classifier (Ada), Extra Trees Classifier (ET), and Voting Classifier (VC)) in forecasting the direction of stock price movement. Eight different stock data from three stock exchanges (NYSE, NASDAQ, and NSE) are randomly collected and used for the study. Each data set is split into training and test set. Ten-fold cross validation accuracy is used to evaluate the ML models on the training set. In addition, the ML models are evaluated on the test set using accuracy, precision, recall, F1-score, specificity, and area under receiver operating characteristics curve (AUC-ROC). Kendall W test of concordance is used to rank the performance of the tree-based ML algorithms. For the training set, the AdaBoost model performed better than the rest of the models. For the test set, accuracy, precision, F1-score, and AUC metrics generated results significant to rank the models, and the Extra Trees classifier outperformed the other models in all the rankings

Effects of Internal CSR Activities on Social Performance: The Employee Perspective

Corporate social responsibility (CSR) continues to receive greater attention in the current business world. Many studies on CSR focus on manufacturing or industrial companies by examining external CSR activities from external stakeholders’ perceptions. However, academic institutions such as higher education institutions (HEIs) remain highly unexplored in the context of internal corporate social responsibility (ICSR). Employees are the most valuable and vital assets for every business organization. Therefore, this study focuses on CSR’s internal dimensions to determine its impact on social performance in HEIs in Ghana. Recognizing the social exchange theory (SET), we specifically examined the effects of five internal CSR dimensions (i.e., health and safety, human rights, training and development, workplace diversity, and work-life balance) on social performance. We used a multi-case approach to assess internal CSR activities in private and public Ghanaian universities. We purposely selected three public universities and one private university because of their varying contexts and academic mandates. We used structured questionnaires to collect data from both teaching and non-teaching staff of the selected universities. Structural equation modeling (SEM) was used to assess the data. We found that health and safety, workplace diversity, and training and development positively and significantly impact social performance. At the same time, human rights and work-life balance have an insignificant effect on social performance. Thus, ICSR practices have a substantial influence on both employees’ and organization’s performance, and hence this study gives important implications for both researchers and practitioner