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The purpose of this study is to examine and to compare the most utilized information technology (IT) investment decision methods between hotels with centrally managed IT, and hotels with locally managed IT.

The empirical data were collected via a structured questionnaire from hotel managers in the USA.

The key findings of the research are that evaluation activities for hospitality IT investments have not been performed widely and consistently. Although sophisticated evaluation methods have been developed over the years, they do not appear to have provided a satisfactory answer to improve IT decision‐making practice. In this study, significant differences were found in how IT investments are evaluated in hotels with centrally managed IT as compared to hotels with locally managed IT. The hotels with centrally managed IT tend to use more financial and non‐financial evaluation methods since all investments are expected to show a positive return on investment.

The research findings highlight the importance of the use of IT investment evaluation techniques in hotels and the major differences between hotels with centrally and locally managed IT.

The literature on IT investment evaluation methods in the lodging industry is limited. Being one of the first studies in this area, these research findings are particularly valuable for practitioners and researchers.

Despite the advantages that the VoFI approach offers compared with traditional capital budgeting methods, its application for the appraisal of information technology (IT) and information systems (IS) investments in both research and practice is not widespread to date. Given the static nature of the generic VoFI table, the method reaches its limits in its financial plan form because it is unable to investigate the dynamic behaviour of complex investment calculations. To date, there has been no attempt to address these shortcomings to advance the use of VoFI as a useful and valid capital budgeting method in finance and accounting. Therefore, the purpose of this study is to address this research gap and aim at developing a ‘dynamic’ VoFI model that integrates all input variables and target measures of a VoFI table and visualises the causal relationships among these variables.

The ‘dynamic’ VoFI model is developed through System Dynamics (SD) modelling to enhance the strength of the VoFI concept as an instrument for visualising the financial implications of investments in IT and IS at the corporate level. Case study research is used as a research method to study the behaviour of the developed model. The validity of the model is demonstrated by conducting simulation runs in Vensim software. In addition, probabilistic sensitivity analyses are performed to account for the impact of uncertainty on the main model variables.

The results demonstrate the usefulness of SD modelling for extending the generic VoFI concept by integrating risk analyses and providing a new strategy of data analysis and data presentation different from the typical financial plan form. Furthermore, the dynamic VoFI model enables the visualisation of interdependencies among the various variables incorporated in the VoFI financial plan, which significantly enhances the conceptual understanding of the investment and its financial consequences.

The integration of the VoFI concept into an SD model helps researchers and practitioners to enhance their conceptual understanding of this method. This thus increases its acceptance and popularity as a practical capital budgeting method, especially for the financial assessment of IT and IS investments. The VoFI model proposed in this paper should also enable analysts and decision makers to become more conscious of the interdependencies between the assumptions made for an appraisal and the quantitative results.

It is well known that internal rate of return (IRR) and net present value (NPV) rankings of mutually exclusive investments are sometimes inconsistent. This inconsistency, when it occurs, requires decision makers to choose between the two ranking methods. The purpose of this paper is to deduce sufficient conditions for consistent IRR and NPV investment rankings of mutually exclusive investments.

Deductive reasoning is used to obtain the sufficient conditions required for consistent rankings of mutually exclusive investments.

There are different sufficient conditions (methods) that can be used to resolve inconsistent IRR and NPV rankings. However, the different methods do not necessarily produce the same consistent rankings. In particular, different size adjustment methods and reinvestment rate assumptions can produce different IRR and NPV consistent rankings. This paper suggests the appropriate criteria for selecting a particular method for ranking mutually exclusive investments.

Like all deduced models, the results apply only to the set of assumptions and preconditions adopted in the model. Furthermore, the application is to ranking mutually exclusive investments.

There is probably no other issue in the capital budgeting literature that has generated more attention and debate than the consistency (or lack thereof) between IRR and NPV rankings. This paper summarizes conditions that can be followed to resolve the conflict which should have near universal interest to those working in the capital budging area. This paper offers alternative methods for obtaining consistent IRR and NPV rankings which can be used to improve investment ranking decisions. The particular method used should depend on the decision environment. Guides for choosing the appropriate ranking method are described in the paper.

Significant decisions, projects, and investments are evaluated using either IRR or NPV methods. This paper shows that existing evaluation methods can lead to sub-optimal investment choices and provides an improved framework that facilitates better investment choices. Lacking an understanding of the sufficient conditions for IRR and NPV consistency – means that resource allocations have been made to investments and projects that are not optimal.

To the best of the authors’ knowledge, the results are this paper have not been published nor are they available elsewhere. That said, this paper builds on important earlier work which is carefully cited and credited.

Robotic process automation (RPA) has recently emerged as a technology focusing on the automation of repetitive, frequent, voluminous and rule-based tasks. Despite a few practical examples that document successful RPA deployments in organizations, evidence of its economic benefits has been mostly anecdotal. The purpose of this paper is to present a step-by-step method to RPA investment appraisal and a business case demonstrating how the steps can be applied to practice.

The methodology relies on design science research (DSR). The step-by-step method is a design artefact that builds on the mapping of processes and modelling of the associated costs. Due to the longitudinal nature of capital investments, modelling uses discounted cashflow and present value methods. Empirical grounding characteristic to DSR is achieved by field testing the artefact.

The step-by-step method is comprised of a preparatory step, three modelling steps and a concluding step. The modelling consists of compounding the interest rate, discounting the investment costs and establishing measures for comparison. These steps were applied to seven business processes to be automated by the case company, Estate Blend. The decision to deploy RPA was found to be trivial, not only based on the initial case data, but also based on multiple sensitivity analyses that showed how resistant RPA investments are to changing circumstances.

By following the provided step-by-step method, executives and managers can quantify the costs and benefits of RPA. The developed method enables any organization to directly compare investment alternatives against each other and against the probable status quo where many tasks in organizations are still carried out manually with little to no automation.

The paper addresses a growing new domain in the field of business process management by capitalizing on DSR and modelling-based approaches to RPA investment appraisal.

This paper presents a combined method of ensemble learning and genetics to rebalance the corporate portfolio. The primary purpose of this paper is to determine the amount of investment in each of the shares of the listed company and the time of purchase, holding or sale of shares to maximize total return and reduce investment risk.

To achieve the goals of the problem, a two-level combined intelligent method, such as a support vector machine, decision tree, network Bayesian, k-nearest neighbors and multilayer perceptron neural network as heterogeneous basic models of ensemble learning in the first level, was applied. Then, the majority vote method (weighted average) in the second stage as the final model of learning was collectively used. Therefore, the data collected from 208 listed companies active in the Tehran stock exchange (http://tsetmc.com) from 2011 to 2015 have been used to teach the data. For testing and analysis, the data of the same companies between 2016 and 2020 have been used.

The results showed that the method of combined ensemble learning and genetics has the highest total stock portfolio yield of 114.12%, with a risk of 0.905%. Also, by examining the rate of return on capital, it was observed that the proposed method has the highest average rate of return on investment of 110.64%. As a result, the proposed method leads to higher returns with lower risk than the purchase and maintenance method for fund managers and companies and predicts market trends.

In the forthcoming research, there were no limitations to obtain research data were easily extracted from the site of Tehran Stock Exchange Technology Management Company and Rahvard Novin software, and simulation was performed in MATLAB software.

In this paper, using combined machine learning methods, companies’ stock prices are predicted and stock portfolio optimization is optimized. As companies and private organizations are trying to increase their rate of return, so they need a way to predict stock prices based on specific indicators. It turned out that this algorithm has the highest stock portfolio return with reasonable investment risk, and therefore, investors, portfolio managers and market timers can be used this method to optimize the stock portfolio.

The homogeneous and heterogeneous two-level hybrid model presented in the research can be used to predict market trends by market timers and fund managers. Also, adjusting the portfolio with this method has a much higher return than the return on buying and holding, and with controlled risk, it increases the security of investors’ capital, and investors invest their capital in the funds more safely. And will achieve their expected returns. As a result, the psychological security gained from using this method for portfolio arrangement will eventually lead to the growth of the capital market.

This paper tries to present the best combination of stock portfolios of active companies of the Tehran Stock Exchange by using the two-level combined intelligent method and genetic algorithm.

This article reports on the results of a survey on how companies listed on the main board of the JSE Securities Exchange SA make a capital investment decision in practice. The respondents to the survey questionnaires provided answers regarding the methods that their companies use to evaluate capital investments, as well as to evaluate mutually exclusive projects. The results suggest that South African companies prefer to use the internal rate of return (IRR) and net present value (NPV) to evaluate capital investments. In addition, there appears to be a correlation between the methods that companies use and the size of their annual capital budget. Finally, a hypothetical problem was presented to the respondents, who were asked to choose between two mutually exclusive projects. Interestingly, the majority of the respondents chose the project which added the least value.

This study aims to make investment decisions in stock markets using forecasting-Markowitz based decision-making approaches.

The authors’ approach offers the use of time series prediction methods including autoregressive, autoregressive moving average and artificial neural network, rather than calculating the expected rate of return based on distribution.

The results show that using time series prediction methods has a significant effect on improving investment decisions and the performance of the investments.

In this study, in contrast to previous studies, the alteration in the Markowitz model started with the investment expected rate of return. For this purpose, instead of considering the distribution of returns and determining the expected returns, time series prediction methods were used to calculate the future return of each asset. Then, the results of different time series methods replaced the expected returns in the Markowitz model. Finally, the overall performance of the method, as well as the performance of each of the prediction methods used, was examined in relation to nine stock market indices.

Special purpose companies issue stocks to raise money to finance development of real estate and infrastructure. The advantage of a stock issue is that it does not entail financial cost such as interest on a loan. However, financing obtained in this way has been insufficient due to low interest by investors because of the large variability of the stocks’ earnings rates. The purpose of this paper is to propose methods to improve investment earnings rate for financing.

The proposed methods are Markowitz’s model and a combination of Markowitz’s model and Monte Carlo simulation. The proposed methods were verified by comparison with actual earnings rate.

The earnings rate was increased by as much as 23 percent over the actual value. Then, earnings rate compared with risk was analyzed using the Sharpe ratio which is a method to measure investment performance. The performance was also increased by as much as 23 percent over the actual value. The proposed method can help activate investment by increasing investors’ interest in the stock issue.

This study verified that Markowitz’s portfolio model, which is used for econometrics, could be applied for financing of construction project. It is valuable because the previous studies did not propose the method for financing.

This paper investigates the extent to which formal capital budgeting methods are used in small high-tech firms. We define high-tech firms by their R&D intensity. In addition, we define software industry as a special type of R&D-intensive firm. We focus on the methods that are used by the small high-tech firms in evaluating the profitability of investment projects, estimating the cost of capital and making decisions related to the capital structure. Our results based on two surveys of Finnish firms indicate that the high-tech firms use similar capital budgeting methods and estimate their cost of capital in a similar way to other small-sized firms in other industries. Moreover, high-tech firms seek external financing and co-owners.

Investment in manufacturing is important to sustained UK economic recovery, quality of life and national economic standing. Considers results from a survey of UK manufacturers on influences on capital investment, the appraisal methods used and the impact of recent changes, particularly in interest rates. Compares results with the Bank of England and CBI surveys and studies of appraisal methodology. Outcomes include the finding that UK interest rates are not seen by most manufacturers as an important influence on their investment decisions. Examines the effects of factors such as inflation, taxation and the UK and EU economic outlooks. Responses confirm near universal usage of the payback method in financial appraisals and widespread use of multiple criteria. Looks at UK investment in advanced manufacturing technology (AMT) and what allowances are made for intangible benefits. Considers a number of aspects of short‐ termism and concludes that the Cadbury recommendations are unlikely to have a major impact.