It is difficult to justify an IT investment unless the investment is expected to have a direct impact on a corporation's performance under rapidly changing business environment. It is important to demonstrate the benefit of the investment through a pre-investment assessment process to induce the executive's decision. This paper presents a methodology to assess the investment by analyzing cost and benefit of the investment. This methodology shows tangible cost as well as hidden cost by analyzing total cost of ownership. The methodology also produces ROI by performing cost benefit analysis including financial and non-financial factors. This paper suggests a systematic way to support If investment decision marking process by evaluating the investment objectively.
The purpose of this study to empirically examine a smart work investment and job performance by change resistance. Firstly, There investigates mediating role of the communication between the smart work investment and the job performance. Secondly, It will identify the job productivity differences through a level of organizational change resistance that reduced smart work investment. The smart work is to provide the flexibility of time and location and is a working method to improve a work productivity of organization members. The introduction of smart work means the adoption of new organizational culture, institution and technology and requires a novel change of a custom and pattern on existing organization culture and institution because of transformation form of communication and collaboration. The method of this study adopts a structural equation model to test a mediating effect of communication and a moderating effect of change resistance level. This model confirms whether smart work investments provide a positive impact on communication and organizational productivity. In addition, I will classify a change resistance level of smart work by cluster analysis and then check a critical path difference of job productivity between each group. As a result, The organizational IT, institution and culture on the smart work investment appeared to important influencers in communication and also had a direct influence of individual performance. Also, The three independent variables of smart work investment have an indirect influence of individual and organizational performance through communication mediating variables. However, the organizational IT and institution as independent variables do not provide direct influence of organization performance. Nevertheless, two independent variables of organizational IT and institution have an indirect influence the organization performance through communication mediating variables. As a result of confirming a productivity of three groups on organization resistance, there was a difference the individual and organizational performance among groups. The low-level group of organizational resistance showed high coefficient value of performance compared to other groups. The group analysis implications, The smart work investment appeared significantly to revise the institution first, build culture secondly and advanced technology lastly. The theoretical implication from this study contributes an extension of social science theory through socio-technical systems, institution, culture, change resistance and job performance based on smart work. The practical implications explain the smart work success in step-by-step investment rather than radical investment as level management of change resistance. In future research, the smart work performance between private and public firms will analyze a difference of the organizational culture, institution, technology and performance.
Since imposing Performance Requirements (PRs) on investors have been conducted as a means to protect a host state's domestic industry in the short run, with its effect on improving the international balance of payments, it has been implemented mostly in developing countries. From the viewpoint of investors, however, PRs hinder free competition and investment activities and have the effect of distorting international trade activities; therefore, they are expected to bring detrimental effects on the host state's economic development in the long run. PRs provided by International Investment Agreements (IIAs) and WTO-TRIMs, too, included many abstract regulations which are grounded on the host state's economic efficiency in the past; however, those PRs are gradually being more concretely specified, shifting to a form of prohibition with the goals of increasing the protection on investors and realizing investment liberalization. Accordingly, when Korea freshly concludes IIAs or revises them afterwards, one should focus more on following the points regarding PRs. First, to protect Korean companies' investment activities, it is advantageous to list the contents of prohibited PRs extensively and concretely and create a stipulation. Second, it is necessary to list the contents of the PRs prohibited and add the phrases for prohibiting "any other similar requirements" explicitly, as well so as to cover the PRs that can appear newly. Third, as in the cases associated with PRs, issuable matters are mostly either the range of applying PRs or the conditions of applying them (relevance or detrimental effects); therefore, it is necessary to classify the cases accumulated by issues and analyze them thoroughly. In conclusion, as this study has analyzed the theoretical characteristics of PRs provided in IIAs and related cases and suggests exquisite theory regarding PRs, it not only lays fundamental grounds for follow-up research but also gives useful and practical guidelines for the parties concerned and the arbitrators according to the ISDS procedure.
Purpose This study builds a prediction model to find stocks that can reach intrinsic value among KOSPI and KOSDAQ-listed companies to improve the stability and profitability of the stock investment. And investment simulations are conducted to verify whether stock investment performance is improved by comparing the prediction model, random stock selection, and the market indexes. Design/methodology/approach Value investment theory and machine learning techniques are applied to build the model. Various experiments find conditions such as the algorithm with the best predictive performance, learning period, and intrinsic value-reaching period. This study selects stocks through the prediction model learned with inventive variables, does not limit the holding period after buying to reach the intrinsic value of the stocks, and targets all KOSPI and KOSDAQ companies. The stock and financial data are collected for 21 years (2001-2021). Findings As a result of the experiment, using the random forest technique, the prediction model's performance was the best with one year of learning period and within one year of the intrinsic value reaching period. As a result of the investment simulation, the cumulative return of the prediction model was up to 1.68 times higher than the random stock selection and 17 times higher than the KOSPI index. The usefulness of the prediction model was confirmed in that the number of intrinsic values reaching the predicted stock was up to 70% higher than the random selection.
Despite high uncertainty on financial return, firms have continuously increased their investment on technologies because they recognize the potential value of technology investment in terms of enhancing operational efficiency and sustaining competitive advantage. Notably, an individual technology investment pattern or strategy within an industry may ultimately lead to significant differences in business performance. Hence, we first categorized technology investment into traditional research and development investment and information technology investment. Afterward, we examined the effects of each pattern with combination of the two types of technology investment on business performance according to firm size and position in the supply chain through fuzzy-set qualitative comparative analysis. Data collected from 562 manufacturing firms in Korea were used in the analysis. Results showed that large-sized firms were slightly affected with microscopic patterns in their technology investments, whereas small firms were highly affected with their technology investment patterns and their positions in the supply chain. The findings implied that a small enterprise requires an appropriate technology investment strategy to achieve successful business outcomes.
Purpose - This paper empirically investigates what factors contribute to corporate investments under financial constraint condition in the Korean stock market. In the paper, tangible assets' growth rate and fixed assets' growth rate were employed as investment performance and total assets were also used for comparison purpose. Research design and methodology - Samples are constructed by manufacturing firms listed on the stock market of Korea as well as those who settle accounts in December from 2001 to 2018. Financial institutions are excluded from the sample as their accounting procedures, governance and regulations differ. This study adopted a fixed panel regression model to assess the sample construction including yearly and cross-sectional data. Results - This results support the literatures that major shareholders showed positive significance to investment in financially unconstrained firms and no significance to investment in financially constrained firms. ROA showed positive significance to investment in financially unconstrained and constrained firms, whereas firm size showed negative significance to investment in financially unconstrained and constrained firms. Debt showed no positive significance to investment in financially unconstrained firms and negative significance to investment in financially constrained firms. Conclusions - This paper documented evidence that ROA and firm size are important factors to investment irrespective of firms' financial constraints. And this paper also supports that major shareholders give positive impact to investments in financially unconstrained firms. This means that financial constraints itself rule corporate' investment decision in financially constrained firms.
The Journal of Asian Finance, Economics and Business
/
v.7
no.8
/
pp.69-76
/
2020
The study examines lagged economic effects of research and development (R&D) investment on the market value of manufacturing firms listed on the Shanghai Stock Exchange or the Shenzhen Stock Exchange in China. This study applies panel data analysis methods to address the following issues: 1) There might be an adjustment lag in the impact of R&D investment on corporate market value, and 2) Unobserved firm effects must be taken into account. The balanced panel data includes a total of 1,462 observations with 34 cross-sections of manufacturing firms listed on Chinese stock markets and with 27 time-specific quarterly periods from 2007 to 2017. The results indicate that the R&D investment of Chinese manufacturing firms tends to yield favorable market value of the firm with some adjustments to time. The results show that R&D investment exhibits a strong positive impact on their market value of manufacturing firms in Chinese stock markets. Moreover, R&D investment has a positive time-lag effect on the market value of the firm. Interestingly, the R&D investment of Chinese manufacturing firms generate a relatively constant positive effect on their market value, supporting the notion that the corresponding returns of R&D investment for such firms yield lagged but added market values.
Korea has been ranked at the top consecutively in UN e-Government Survey. This fact reflects consistent efforts by public institutes in Korea. However, there were some duplicate efforts and inefficiency in the investment into IS(information systems). In other words, management and decision making on e-government have yet to improved. Accordingly, governmental institutes have long been promoting policies for EA in order to carry out and manage effectively IT projects including the e-Government projects. EA is being regarded as a means to facilitate IT Governance for systematic management of IT projects, A major objective is to firmly establish the decision making process for IS investment through EA, since the each institute is a large organization with so complicated and specialized demand for IS. As a result, systematic decision making is becoming quite difficult. Thus, this study attempts to identify different types of IT investment decision making and to figure out the relationship between the decision types and the organizational performance of public institutes where EA was implemented.
The main purpose of this study is to identify the factors influencing the implementation performance of ERP system from the investment such as hardware, software and consulting fee. The main goal of this study to analyze of ERP system implementation in various sections depending on the amount of investment and research about the contribution of ERP utilization on overall PCB business activities. Survey questionnaires were distributed via post and fax to PCB (Printed Circuit Board)Company in Korea. The results of this study can be summarized as follow. First, only 43.1% of respondents implemented ERP system at the enterprise level, which shows that ERP system does not completely play its role as a truly integrated system. Second, companies shows higher satisfaction rate with selectively invested on particular sub-modules. Third, the efficiency of ERP system can be maximized by improving efficiency on core areas through selective investments. Also, this can be maximized by performing overall investments on the general environment in addition to the direct investment on ERP. Lastly, 59.26% of respondents reported that the utilization of ERP has highly significant effects on the contribution of business whereas 29.63% respondents reported as significant effects on the business. These shows positive effects of ERP on the contribution of business.
The finance-investment industry is currently focusing on research related to artificial intelligence and big data, moving beyond conventional theories of financial engineering. However, the case of equity optimization portfolio by using an artificial intelligence, big data, and its performance is rarely realized in practice. Thus, the purpose of this study is to propose process improvements in equity selection, information analysis, and portfolio composition, and lastly an improvement in portfolio returns, with the case of an equity optimization model based on quantitative research by an artificial intelligence. This paper is an empirical study of the portfolio based on an artificial intelligence technology of "D" asset management, which is the largest domestic active-quant-fiduciary management in accordance with the purpose of this paper. This study will apply artificial intelligence to finance, analyzing financial and demand-supply information and automating factor-selection and weight of equity through machine learning based on the artificial neural network. Also, the learning the process for the composition of portfolio optimization and its performance by applying genetic algorithms to models will be documented. This study posits a model that the asset management industry can achieve, with continuous and stable excess performance, low costs and high efficiency in the process of investment.
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