Management Design Journal
No 17 Q1 2021
Machine learning in medicine
Machine learning tools may support physicians in evaluating treatments and forecasting outbreaks in the pandemic (Mel, 2020; Wang et al., 2020). Mel (2020) explores the potential of nitrogen in treating COVID-19 patients. Wang et al. (2020) develop an R programming package that provides a time-series analysis for studying epidemiological forecasts for epidemics such as the COVID-19 outbreak.
Machine learning may also be implemented to support physicians in disease diagnosis (Wang et al., 2020). Williamson et al. (2020) develop a machine learning tool to support primary care physicians identify fragility in patients. Awodutire, Kolawole, and Ilori (2020) develop an R script to analyze survival rates of breast cancer patients. Ioachimescu et al. (2020) study ways to test for obstructive sleep apnea by developing a cost effective measure of peripheral arterial tonometry variability as a marker of changes in sleep patterns.
One of the emerging tools in machine learning is the application of Bayesian inference in machine learning algorithms (Rothkopf, 2020). Models based on Bayesian inference may be adapted with further computational research (Rothkopf, 2020). Rothkopf (2020) evaluates the Bayesian inference method of maximum entropy for nonlinear optimization problems. Tabebordbar, Beheshti, Benatallah, and Barukh (2020) implement Bayesian algorithms with feature selection and data annotation rules for dynamic unstructured environments.
Open source tools may also support applications of machine learning in medicine (van der Naald, Wenker, Doevendans, Wever, & Chamuleau, 2020). van der Naald et al. (2020) develop an open source register for preregistration of animal studies to promote a comprehensive listing, minimize bias, and increase transparency.
Machine learning can also support studies related to medicine (Nuijten, van Assen, Augusteijn, Crompvoets, & Wicherts, 2020). Nuijten et al. (2020) examine the characteristics of statistical studies in the psychology field. Kim (2020) explains the connection between occupational science and the health profession in the promotion of healthy living.
Awodutire, P. O., Kolawole, O. A., & Ilori, O. R. (2020). Data on the survival times of breast cancer patients in a Teaching Hospital, Osogbo. Data in Brief, 32, 106109. doi:https://doi.org/10.1016/j.dib.2020.106109
Ioachimescu, O. C., Dholakia, S. A., Venkateshiah, S. B., Fields, B., Samarghandi, A., Anand, N., . . . Collop, N. A. (2020). Improving the performance of peripheral arterial tonometry-based testing for the diagnosis of obstructive sleep apnea. Journal of Investigative Medicine, jim-2020-001448. doi:10.1136/jim-2020-001448
Kim, H. (2020). Introduction to an occupational perspective of health. Global Journal of Intellectual & Developmental Disabilities, 6(5), 98.
Mel, A. d. (2020). Potential roles of nitric oxide in COVID-19: A perspective. Integrative Molecular Medicine, 7.
Nuijten, M. B., van Assen, M. A. L. M., Augusteijn, H. E. M., Crompvoets, E. A. V., & Wicherts, J. M. (2020). Effect sizes, power, and biases in intelligence research: A meta-meta-analysis. Journal of Intelligence, 8(4). doi:10.3390/jintelligence8040036
Rothkopf, A. (2020). Bryan’s maximum entropy method—diagnosis of a flawed argument and its remedy. Data, 5(3). doi:10.3390/data5030085
Tabebordbar, A., Beheshti, A., Benatallah, B., & Barukh, M. C. (2020). Feature-based and adaptive rule adaptation in dynamic environments. Data Science and Engineering, 5(3), 207-223. doi:10.1007/s41019-020-00130-4
van der Naald, M., Wenker, S., Doevendans, P. A., Wever, K. E., & Chamuleau, S. A. J. (2020). Publication rate in preclinical research: A plea for preregistration. BMJ Open Science, 4(1), e100051. doi:10.1136/bmjos-2019-100051
Wang, L., Zhou, Y., He, J., Zhu, B., Wang, F., Tang, L., . . . Song, P. X. K. (2020). An epidemiological forecast model and software assessing interventions on the COVID-19 epidemic in China (with discussion). Journal of Data Science, 18(3).
Williamson, T., Aponte-Hao, S., Mele, B., Lethebe, B. C., Leduc, C., Thandi, M., . . . Wong, S. T. (2020). Developing and validating a primary care EMR-based frailty definition using machine learning. International Journal of Population Data Science, 5(1), 32.
Financial and project management evaluation models and trends
Assessment models for financial management may have greater impact with increasing demand for project management resources (Silva, Pérez, & Puentes, 2018). Silva et al. (2018) explain that budget, deadlines, deliverables and quality are factors related to the success of a project for project managers. Odoardi and Pagliari (2016) analyze techniques for measuring economic household wealth. Janicka, Pieloch-Babiarz, and Sajnóg (2020) determine whether requiring short term profit reports have an influence on the market value of companies. Ding and Uryasev (2020) develop an innovative technique for measuring systemic risk of financial institutions.
Ding, R., & Uryasev, S. (2020). CoCDaR and mCoCDaR: New approach for measurement of systemic risk contributions. Journal of Risk and Financial Management, 13(11). doi:10.3390/jrfm13110270
Janicka, M., Pieloch-Babiarz, A., & Sajnóg, A. (2020). Does short-termism influence the market value of companies? Evidence from EU countries. Journal of Risk and Financial Management, 13(11). doi:10.3390/jrfm13110272
Odoardi, I., & Pagliari, C. (2016). Household wealth as a factor of economic growth: A case study of Italy. Contemporary Economics, 14(3), 337-353.
Silva, H. F. C., Pérez, T. V., & Puentes, M. P. R. (2018). Adoption of project management methodologies in Colombia project manager's perspective. Paper presented at the International Meeting on Applied Sciences and Engineering.
Elections and social, political, and technological change
Social, political, and technological changes may influence the performance of future elections (Ringhand, 2020). Ringhand (2020) explain how the efforts of foreign governments to interfere with the elections of another country may be encouraged by emerging social, political and technological influences. Pettigrew (2020) study whether long election voting lines may be predecessor for less future voting turnout. Garrett (2019) describes the increase of the role of social media as a news source and the influence that it may have for creating misperceptions during elections. Kanthak and Woon (2015) provide a history of differences in gender for election candidates and study the reasons for these differences.
Garrett, R. K. (2019). Social media’s contribution to political misperceptions in U.S. Presidential elections. PLoS One, 14(3), e0213500 doi:10.1371/journal.pone.0213500
Kanthak, K., & Woon, J. (2015). Women don't run? Election aversion and candidate entry. American Journal of Political Science, 59(3), 595-612. doi:10.1111/ajps.12158
Pettigrew, S. (2020). The downstream consequences of long waits: How lines at the precinct depress future turnout. Electoral Studies, 102188. doi:https://doi.org/10.1016/j.electstud.2020.102188
Ringhand, L. A. (2020). Foreign election interference: Comparative approaches to a global challenge. Election Law Journal: Rules, Politics, and Policy. doi:10.1089/elj.2020.0683
Digital transformations in support of organizational cultural change
Sugianto and Pontjoharyo (2020) describe how lean processes can support organizational cultural change. Kwet (2020) propose government policies to transform a democratic commons ecosystem from social media platforms. Fink, Shao, Lichtenstein, and Haefliger (2020) describe the advantage of software libraries for innovation by providing tools for experimentation and prototyping. Jaramillo, Rascon, Adams, and Jauregui (2020) develop a software testing strategy that is capable of qualifying applications in complex technology environments.
The pandemic may have provided opportunities to redesign some of the processes to developing digital transformations (Crandall, North, & Crandall, 2020). Crandall et al. (2020) discover that data collection is one of the highest ranked challenges for school counselors in the digital transformation of education. Iivari, Sharma, and Ventä-Olkkonen (2020) explain how the digital divide has created a challenge for basic education during the pandemic. Mirbabaie, Bunker, Stieglitz, Marx, and Ehnis (2020) examine how social media is perceived during a time of crisis. Córdoba-Pachón (2020) describes how systems thinking design can be influenced by strategies developed during the pandemic.
Córdoba-Pachón, J.-R. (2020). Inter-work and ethical vigilance two scenarios for the (post-)pandemic future of systems thinking. Systems, 8(36).
Crandall, K. S., North, M., & Crandall, K. (2020). Digitally transforming the professional school counselor. Issues in Information Systems, 21(1), 1-11.
Fink, L., Shao, J., Lichtenstein, Y., & Haefliger, S. (2020). The ownership of digital infrastructure: Exploring the deployment of software libraries in a digital innovation cluster. Journal of Information Technology, 35(3), 251-269. doi:10.1177/0268396220936705
Iivari, N., Sharma, S., & Ventä-Olkkonen, L. (2020). Digital transformation of everyday life – How COVID-19 pandemic transformed the basic education of the young generation and why information management research should care? International Journal of Information Management, 102183. doi:https://doi.org/10.1016/j.ijinfomgt.2020.102183
Jaramillo, P., Rascon, M., Adams, C., & Jauregui, E. (2020). Fundamental principles, processes, and roles of environmental qualification test strategy for complex engineered systems. Journal of Information Technology & Software Engineering, 10(265).
Kwet, M. (2020). Fixing social media: Toward a democratic digital commons. Markets, Globalization & Development Review, 5(1).
Mirbabaie, M., Bunker, D., Stieglitz, S., Marx, J., & Ehnis, C. (2020). Social media in times of crisis: Learning from Hurricane Harvey for the coronavirus disease 2019 pandemic response. Journal of Information Technology, 35(3), 195-213. doi:10.1177/0268396220929258
Sugianto, A., & Pontjoharyo, W. (2020). Lean accounting in transforming the organizational culture in PT. A. IJRDO - Journal of Business management, 6(9).
Corporate and national financial risk management
The economic crisis may demonstrate the needs for corporations to manage their own financial risk (Drobyazko, Barwinska-Malajowicz, Slusarczyk, Chubukova, & Bielialov, 2020). Drobyazko et al. (2020) discuss the importance o enterprises to assess and manage their own financial stability with increased international economic uncertainty. Corporations may develop their own tools for managing risks within each division (Deng, 2020). Deng (2020) describes the process for developing a financial control system to be used internally for the divisions of a company.
Corporations may optimize their portfolios to reduce risks (Karpenko, Chunytska, Oliinyk, Poprozman, & Bezkorovaina, 2020). Karpenko et al. (2020) optimize portfolio management methodologies for corporations to reduce risk while still building a profit. Corporations may also develop simulations to evaluate possible scenarios (Ercole & Paolo, 2020). Ercole and Paolo (2020) apply Bayesian models and Markov Chain Monte Carlo simulations to measure insurance risk.
National policy makers may develop a number of regulations to reduce financial risk (Gnangnon, 2021). Gnangnon (2021) describe how tax reform can be beneficial to national policy makers and financial institutions for reducing public debt instability. National policy makers may also create long term projects that are partnerships between the private and public sector (Sarmento & Renneboog, 2020). Sarmento and Renneboog (2020) describe the advance of long term partnership projects between private corporations and government entities.
Deng, T. (2020). Reflections on the construction of the internal financial control system of group company subsidiaries under the financial centralized management system. Journal of Financial Risk Management, 9, 268-277.
Drobyazko, S., Barwinska-Malajowicz, A., Slusarczyk, B., Chubukova, O., & Bielialov, T. (2020). Risk management in the system of financial stability of the service enterprise. Journal of Risk and Financial Management, 13(12). doi:10.3390/jrfm13120300
Ercole, C. G., & Paolo, C. G. (2020). A Bayesian internal model for reserve risk: An extension of the correlated chain ladder. Risks, 8(4). doi:10.3390/risks8040125
Gnangnon, S. K. (2021). Tax reform and public debt instability in developing countries: The trade openness and public revenue instability channels. Economic Analysis and Policy, 69, 54-67. doi:https://doi.org/10.1016/j.eap.2020.11.005
Karpenko, L., Chunytska, I., Oliinyk, N., Poprozman, N., & Bezkorovaina, O. (2020). Consideration of risk factors in corporate property portfolio management. Journal of Risk and Financial Management, 13(12). doi:10.3390/jrfm13120299
Sarmento, J. M., & Renneboog, L. (2020). Renegotiating public-private partnerships. Journal of Multinational Financial Management, 100661. doi:https://doi.org/10.1016/j.mulfin.2020.100661
Business models and organizational research
Business domain models may provide tools to support organizational research and operations (Eshuis, 2021). Eshuis (2021) develop a model that allows fragmented features to be engineered and reused to support procedural modelling and the business phase life cycle. Benitez, Henseler, Castillo, and Schuberth (2020) provide guidelines for partial least squares applications in structural equation modeling for statistical research.
Modelling can also support system design in information systems (Mohd & Azad, 2011). Mohd and Azad (2011) explain that the stages of systems design in web engineering include configuration, procurement, and design and integration phases. Knisely and Vaughn-Cooke (2020) explain how digital human modeling can be implemented to represent the interaction of a human with a product or system.
Benitez, J., Henseler, J., Castillo, A., & Schuberth, F. (2020). How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research. Information & Management, 57(2), 103168. doi:https://doi.org/10.1016/j.im.2019.05.003
Eshuis, R. (2021). Feature-oriented engineering of declarative artifact-centric process models. Information Systems, 96, 101644. doi:https://doi.org/10.1016/j.is.2020.101644
Knisely, B. M., & Vaughn-Cooke, M. (2020). Virtual modeling of user populations and formative design parameters. Systems, 8(35).
Mohd, R., & Azad, A. (2011). A web-engineering solution to academic management system of an educational institute. Journal of Global Research in Computer Science, 2(1).
Traditional and innovative marketing
Traditional marketing tools may provide advantages even with the competition of emerging media tools (Fortenberry & McGoldrick, 2019). Fortenberry and McGoldrick (2019) investigate the effectiveness of billboards in marketing. Social media may be an opportunity for developing new marketing and advertising models (Marchand, Hennig-Thurau, & Flemming, 2020). Marchand et al. (2020) develop and explore strategies for analyzing the performance of social media resources for corporate marketing campaigns. Big data analytics may also impact advertising research (Dekimpe, 2020). Dekimpe (2020) explains how the advance of big data analytics has affected the retail value chain and retail research models.
Dekimpe, M. G. (2020). Retailing and retailing research in the age of big data analytics. International Journal of Research in Marketing, 37(1), 3-14. doi:https://doi.org/10.1016/j.ijresmar.2019.09.001
Fortenberry, J. L., & McGoldrick, P. J. (2019). Do billboard advertisements drive customer retention? Journal of Advertising Research, JAR-2019-2003. doi:10.2501/JAR-2019-003
Marchand, A., Hennig-Thurau, T., & Flemming, J. (2020). Social media resources and capabilities as strategic determinants of social media performance. International Journal of Research in Marketing. doi:https://doi.org/10.1016/j.ijresmar.2020.09.011
Intellectual property regulation and technology
Vidrascu (2014) describes intangible assets such as intellectual property as the most essential and valuable to an organization. Vidrascu (2014) divides intangible assets as technology and marketing assets and intellectual property as registered and unregistered rights. Intellectual property may also be complex to regulate in international business (Stepanov, 2020). Stepanov (2020) discusses the complexity of regulations for intellectual property in foreign direct investment. The increase of datasets available for analysis may also present a challenge for intellectual property regulation (Carroll, 2015). Carroll (2015) explores how intellectual property rights can be protected for datasets.
Carroll, M. W. (2015). Sharing research data and intellectual property law: A primer. PLOS Biology, 13(8), e1002235. doi:10.1371/journal.pbio.1002235
Stepanov, I. (2020). Economic development dimension of intellectual property as investment in international investment law. The Journal of World Intellectual Property, n/a(n/a). doi:10.1111/jwip.12171
Vidrascu, P. A. (2014). Debates on intellectual property rights. Hyperion Economic Journal, 3(2), 74-85.
Emerging modes of commerce
The growth of forms of electronic payments may have a difficult path to mainstream acceptance (Oyelami, Adebiyi, & Adekunle, 2020). Oyelami et al. (2020) evaluated a number of components that contribute to the adoption of electronic payments. New methods of electronic payments may benefit from an evaluation of how early companies strategize for success. New firms that are entering the market may face challenges in the early years of business (Islami, Mustafa, & Topuzovska Latkovikj, 2020). Islami et al. (2020) define strategies and measurements for the survival capabilities of new firms entering the market. The emergence of electronic commerce may apply models from business startups to compete with existing payment methods.
Islami, X., Mustafa, N., & Topuzovska Latkovikj, M. (2020). Linking Porter’s generic strategies to firm performance. Future Business Journal, 6(1), 3. doi:10.1186/s43093-020-0009-1
Oyelami, L. O., Adebiyi, S. O., & Adekunle, B. S. (2020). Electronic payment adoption and consumers’ spending growth: empirical evidence from Nigeria. Future Business Journal, 6(1), 14. doi:10.1186/s43093-020-00022-z
Qualitative Design Data Analysis