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In Ghana, as with other developing countries, there are several health information technology (HIT) initiatives as interventions to improve healthcare delivery. HIT implementation undoubtedly results in change. However, most studies relating to HIT implementation readiness have constantly neglected the role of change in successfully implementing HIT. This study intends to identify factors affecting successful change management as part of preparation towards successfully implementing HIT in public hospital in Ghana. To carry out this study, we conducted in-depth interviews with a matrix of HIT senior managers and thematically analyzed the data. The data was transcribed and uploaded into a Nvivo 11 software for analysis using thematic analysis techniques. Five (5) themes were discovered. They are: 1) Stakeholder participation; 2) Proof of experience in similar project; 3) Availability of committed change agents/all-levels-change representatives; 4) Clearly articulated change implementation strategy; and 5) Training and improvement mechanism (post-implementation). A fresh call is made for more attention to be paid to change as part of preparatory measures towards the adoption of HIT in Ghana using the five cardinal approaches identified as a guide.

The proposed model has been aimed to predict stock market signals by designing an accurate model. In this sense, the stock market is analysed by the technical analysis of Japanese Candlestick, which is combined by the following meta heuristic algorithms: support vector machine (SVM), meta-heuristic algorithms, particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).

In addition, among the developed algorithms, the most effective one is chosen to determine probable sell and buy signals. Moreover, the authors have proposed comparative results to validate the designed model in this study with the same basic models of three articles in the past. Hence, PSO is used as a classification method to search the solution space absolutelyand with the high speed of running. In terms of the second model, SVM and ICA are examined by the time. Where the ICA is an improver for the SVM parameters. Finally, in the third model, SVM and GA are studied, where GA acts as optimizer and feature selection agent.

Results have been indicated that, the prediction accuracy of all new models are high for only six days, however, with respect to the confusion matrixes results, it is understood that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the authors to analyze the data the long length of time between the years 2013–2021, makes the input data analysis challenging. They must be changed with respect to the conditions.

In this study, two methods have been developed in a candlestick model, they are raw based and signal-based approaches which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

This paper aims to identify the importance of individual variables in the corporate real estate (CRE) decision-making process.

Nine experts received a posed scenario of a changed business strategy requiring a CRE reduction in individual interviews. Based on their suggested response, a decision network was modelled for each expert using the causal network elicitation technique, incorporating the utilities for decision variables and importance weights for attributes and benefits. The decision model offers a graphical representation of decision-benefit links for the decisions CRE managers make in such a period of decline.

Perceived facilitators of CRE dynamic alignment were identified by calculating lift ratios on their perceived importance of the attributes they mentioned during the interviews as nodes in the network that link decisions to benefits. Facilitators included CRE metrics and workplace strategy, while capital expenditure and landlords inhibit alignment processes. The research provides more granular insight into the variables used in CRE decision-making and the factors that facilitate or inhibit the dynamic alignment process.

The research set a specific scenario for the experts to consider. That could be regarded as small but there was clear evidence of saturation of expert knowledge. Additional face-to-face interviews with the experts may have generated further details on the thought processes of the experts.

The research provides more granular insight into the variables used in CRE decision-making and the factors that facilitate or inhibit the dynamic alignment process. Thereby providing CRE decision-makers with key elements for a decision model.

The research technique, causal network elicitation technique, uses semi-structured interviews to create decision networks, which is a technique that has not been widely applied to CRE research. The research provides a granular view of what are important inhibitors or facilitators of dynamic alignment of CRE to business strategy.

The purpose of this study is to describe primary health-care managers’ perceptions of management competencies at different management levels in digital health services using the management competency assessment program as a framework.

A secondary analysis study involving 21 semi-structured individual interviews was conducted among Finnish primary health-care managers at different management levels (frontline, middle and senior). The deductive framework method was used to analyze the data.

Similarities and differences were found in management competencies between different levels of management. Competencies related to the use of digitalization were highlighted by managers at all management levels. Managers at all management levels were involved in developing digital solutions and supporting employees in using digital solutions in their work. Frontline and middle managers emphasized more issues related to day-to-day management and communication with employees, whereas senior managers highlighted the management of large entities.

In the secondary analysis, data were used for purposes other than originally intended. Therefore, the data are subject to limitations of the methodology applied and should be transferred to other contexts with caution.

Identifying the management competencies needed to manage digital health services is important to target managers’ training according to needs in the future.

The results could be used to develop the management of digital health services, as well as improve digital health services and their deployment.

Previous literature mostly examined managers’ informatics competencies and paid little attention to other management competencies. This study discusses more broadly the management competencies that digital health services require from managers at different levels of management.

Service providers increasingly use conversational agents (CAs), such as chatbots, to effectively communicate with customers while managing interaction costs and providing round-the-clock customer service. Yet, the adoption and implementation of such agents in service contexts remains a hit-and-miss, and firms often struggle to balance their CAs implementation complexities and costs with relation to their service objectives, technology design and customer experiences. The purpose of this paper is to provide guidance on optimizing CA design, therefore, the authors develop a conceptual framework, TRISEC, that integrates service logic, technology design and customer experience to examine the implementation of CA solutions in search, experience and credence (SEC) contexts.

The paper draws on service marketing and communications research, combining the service context classification scheme of search, experience and credence and the technology infused service marketing triangle foci (service, technology and customer) in its conceptual development.

The authors find that an opportunity exists in recognizing the importance of context when designing CAs and aiming to achieve a balance between service objectives, technology design and customer experiences.

This study contributes to service management and communications research literature by providing interactive service marketing researchers with the highly generalizable TRISEC framework to aid in optimizing CA design and implementation in interactive customer communication technologies. Furthermore, the study provides an array of future research avenues. From a practical perspective, this study aims at providing managers with a means to optimize CA technology design while maintaining a balance between customer centricity and implementation complexity and costs in different service contexts.

The purpose of this paper is to present the major gaps in the field of planning policy and its implementation regarding climate change and disaster risk reduction (DRR), with special reference to the displacement of people, together with the knowledge needed to increase community resilience. The researched relations are illustrated by the example of Serbia. The Republic of Serbia has been faced with increasingly visible impacts of climate change in recent years – floods, heat waves, droughts and others. During the floods that hit Serbia in 2014, over 30,000 people experienced displacement. These events have triggered numerous efforts, both to repair the incurred damage and to analyze opportunities for prevention.

This research has used document analysis to investigate contemporary approaches defined by policies, programs and research reports regarding climate change and DRR, with special reference to the displacement of people. An analytical framework has been used to evaluate to what extent the planning policy framework in Serbia addresses these issues in the context of achieving resilient development. Secondary analysis of research data has been used to recognize the gaps and identify needs for increasing community resilience.

Based on the growing trends in projections of climate change as a result of induced natural disasters for the region in the future and international trends in coping with these issues, this paper argues that it is necessary to improve the implementation of the planning policy framework and the capacities of professionals and citizens, to reduce future displacement and increase community resilience to climate change. The key weaknesses found within DRR and the emergency management system in Serbia were the lack of an appropriate information base of the cadastre of risk zones and the lack of information and coordination of actors on the local to the national level. During the “pre-disaster” period, findings stress a weak partnership and capacity development practice at the local level, as well as between local responsible bodies and regional/national entities in charge of emergency management and DRR. The paper singles out the main preconditions for achieving effective resilient planning, so that such a plan can move “people away from marginal areas” and provide living conditions that are resilient.

This paper provides a comprehensive insight analysis of the relations between climate change and DRR, with special reference to the planning policy. Using the lessons learned from the recent climate-induced disaster with its implications on displacement, the paper identifies needs for strengthening capacities to establish more resilient communities in Serbia. The gaps and needs identified, as well as the recommendations provided, may be of value for neighboring countries as well, who face similar challenges in climate change adaptation and who need to increase disaster risk resilience.

In this research, the main purpose is to use a suitable structure to predict the trading signals of the stock market with high accuracy. For this purpose, two models for the analysis of technical adaptation were used in this study.

It can be seen that support vector machine (SVM) is used with particle swarm optimization (PSO) where PSO is used as a fast and accurate classification to search the problem-solving space and finally the results are compared with the neural network performance.

Based on the result, the authors can say that both new models are trustworthy in 6 days, however, SVM-PSO is better than basic research. The hit rate of SVM-PSO is 77.5%, but the hit rate of neural networks (basic research) is 74.2.

In this research, two approaches (raw-based and signal-based) have been developed to generate input data for the model: raw-based and signal-based. For comparison, the hit rate is considered the percentage of correct predictions for 16 days.