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Msc In Enterprise Risk Management – Modules

ERM511 Principles of Economics Risk Management

Module Code: ERM511
ECTS Credit Points: 10
Module Type: Compulsory
Offered for the academic semester: First (1st)
Language: English
Module Outline

General Description

This Thematic Unit / Module is designed to introduce students to the foundation topics in microeconomics and industrial organization. This knowledge is important for reaching decisions in everyday business. The decisions aim to maximize profit and to strategically design and prepare a future for the business that will guarantee the existence and hopefully enlargement of profit in the long run. In fact this module will prepare students to forecast the framework of their business for the next day and thus hedge against business risks. In essence the module aims to prepare students as leaders who build business tactics and they will become a lot more than middle managers who rarely blaze trails in unknown areas and uncertain times such as those that businesses are encountered with nowadays.

Overall the module provides principles to foster the goals of the organization, as well as a better understanding of the external business environment in which an organization operates. In this module, future managers will learn how to increase company’s profitability by applying economic analysis to a wide array of business problems. The course will develop students’ capacity to analyze the economic environments in which business entities operate and understand how managerial decisions can vary under different constraints that each economic environment places on a manager’s pursuit of his/her goals. Its focus will be on analyzing the functioning of markets, the economic behavior of firms and other economic agents under various market structures, and the economic and social implications of the outcomes.

Learning Outcomes

Upon completion of this module, the students will be able to:

Knowledge

  • Exhibit a deep and thorough understanding of how to run the everyday business with its profit maximization objectives and how to plan for the future in order to maintain and expand profitability within a risk framework.

Comprehension

  • Explain why market equilibrium occurs at the price for which quantity demanded equals quantity supplied.
  • Explain the concept of utility and the basic assumptions underlying consumer preferences.
  • Explain several factors that affect price elasticity of demand
  • Explain how different forces, like scale, scope and learning economies affect long-run costs
  • Explain why barriers-to-entry are necessary for market power in the long-run and discuss major types of entry barriers.
  • Understand and explain why cooperation can sometimes be achieved when decisions are repeated over time and discuss four types of facilitating practices for reaching cooperative outcomes.

Application

  • Make use of indifference curves to derive a demand curve for an individual consumer and construct a market demand curve
  • Make use of empirically estimated or forecasted values of market price, average variable cost, and marginal cost to calculate firm’s profit maximizing output and long- or short-run profits.
  • Relate marginal revenue to total revenue and demand elasticity and write the marginal revenue equation for linear inverse demand functions.
  • Construct firm’s expansion path and show how it relates to the firm’s long-run cost structure.
  • Apply optimization theory to find optimal input combinations

Analysis

  • Examine the structure of short-run production based on the relation among total, average and marginal products.
  • List the steps in the strategic management process
  • List the steps in the decision making process
  • Relate short-run costs to the production function and the basic features of firms’ technology.
  • Analyze a typical production isoquant and discuss its properties

Synthesis

  • Be able to identify the type of market conditions applicable in a business.
  • Be able to compare production and cost functions across businesses.
  • Identify ways of action and hedging in oligopolistic markets.

Evaluation

  • Evaluate the characteristics of different markets.
  • Evaluate deviations from profit maximization conditions and correct them respectively.
  • Evaluate the power of competitors and their future steps vis-s-vis one’s own business.

Subjects covered:

  • Basic principles and the consumer theory
  • The production theory and Cost Theory
  • Perfect competition and Imperfect Competition

Prerequisites: There are no prerequisites for this module.

Evaluation: Completion of written assignments during the academic semester which constitute a 40 percent of each student’s grade, if a pass is obtained in the final or repetitive examination. Final exam grades constitute a 60 percent of the students’ final course grade.

 

ERM512 Advanced Quantitative Methods for Risk Management

Module Code: ERM512
ECTS Credit Points: 10
Module Type: Compulsory
Offered for the academic semester: First (1st)
Language: English
Module Outline

General Description

This Thematic Unit / Module is designed to introduce students to econometric techniques and their applications in economic analysis and decision-making. The main objective of the module is to train students in (i) handling economic data; (ii) quantitative analysis of economic models with probabilistic tools; (iii) econometric techniques, their application as well as their statistical and practical interpretation; (iv) implementing these techniques on any given econometric software.

The module focuses on practical and conceptual issues involved in substantive applications of econometric techniques. Estimation and inference procedures are formally analysed for simple econometric models and illustrated by empirical case studies using real-life data. The module covers sampling, estimation and statistical inference techniques, linear and non-linear regression models.

Learning Outcomes

Upon completion of this module, the students will be able to:

Knowledge

  • Exhibit a deep and thorough understanding of statistical concepts underlying sampling and sample statistics, the applications of inferential statistics and the processes of hypothesis testing, and the underlying mechanisms of regression including the assumptions and the estimation process.

Comprehension

  • Distinguish among various statistical measures and modelling techniques and classify them with respect to their suitability in analysing empirical data and meeting the objectives of the study.
  • Explain the underpinnings of the Hypotheses Testing process, the significance level and the importance of considering the Type I and II errors in testing hypotheses by providing. demonstrative examples
  • Explain the construct of regression models and it is affected in practice by violation of assumptions and “non-cleaned” data in empirical observations.

Application

  • Apply various sampling techniques, describe the processes for defining and selecting sample data, calculate sample statistics and derive confidence intervals for the sample statistics.
  • Perform test of hypothesis by properly selecting statistical methodologies defining null and alternative hypotheses determining critical values and interpret the results in context.
  • Setup multiple linear regression models and derive and analyse regression results and residuals
  • Handle the effect of qualitative indicators in regression, and apply proper transformations in the data to build suitable non-linear regression models
  • Use statistical software to handle empirical data, perform statistical analysis listed above, accordingly.

Analysis

  • Analyse statistical data properly, in order to identify distribution patterns, possible relationships among data attributes, contingencies, and interaction among various factors.
  • Estimate relationships between explanatory and response variables, explain how estimators behave in terms of their probability distributions, test hypotheses on the relation between variables using F-values, t-values and p-values, and measure goodness of fit in a regression
  • Analyse the statistical significance of a regression model, the contribution of the explanatory variables and the significance of categorical explanatory variables.
  • Consider practical problems that arise in the estimation and analysis of the regression model, including multicollinearity, heteroscedasticity, presence of extreme or missing observations and take proper action to rectify the situation.

Synthesis

  • Consolidate and interpret results of statistical analysis of empirical data in context in order to communicate relative information for supporting business decision making.
  • Use the results of a regression model to identify and measure the single or interaction effects of independent variables on a dependent variable and interpret the effects in context.
  • Select the most appropriate regression model after a comparison among alternatives, and interpret the regression results in context providing insight of potential limitations.

Evaluation

  • Appraise the appropriateness of various sampling methods in collecting empirical data for specific purpose and evaluate the adequacy of a sample size, vis-à-vis acceptable sampling error, and desired confidence of the results.
  • Describe the basic statistical characteristics of a population, based on evaluation of sample statistics.
  • Evaluate relationships and patterns among the data using statistical techniques.
  • Evaluate the “fitness” and the predictive power of regression model in making extrapolations
  • Evaluate a regression model in terms of statistical significance and conformance with assumptions and detect potential problems with respect to violation of assumptions

Subjects covered:

  • Probabilities, Estimation, Sampling, Data analysis
  • Statistical Inference: Confidence Intervals Hypothesis Testing
  • Linear and Non-Linear Regression, Estimation, Prediction

Prerequisites: There are no prerequisites for this module.

Evaluation: Completion of written assignments during the academic semester which constitute a 40 percent of each student’s grade, if a pass is obtained in the final or repetitive examination. Final exam grades constitute a 60 percent of the students’ final course grade.

 

ERM513 Risk and Risk Management

Module Code: ERM513
ECTS Credit Points: 10
Module Type: Compulsory
Offered for the academic semester: First (1st)
Language: English
Module Outline

General Description

The Module “Risk and Risk Management”, ERM513, is designed to introduce students to the concept of risk from a global viewpoint, as well as to the fundamental principles of Risk Management.

The main objective of the module is to train students in:

  • Implementation of the basic risk management procedure
  • Application of principles of Risk Perception
  • Taking human factors into account in Risk Management
  • Analyzing the Risk Communication process.

The Module focuses on presenting a global understanding of risk and Risk Management in all of its aspects. It starts with the fundamental aspects, parameters and metrics of risk. Α separate examination of risks related to human factors follows. The basic issues of the social aspect of Risk Management, namely Risk Communication, Risk Governance and Risk Culture are subsequently presented. Finally, the basic approaches and models of subjective perception of risk forming the “constructivist” approach to risk, are also examined.

Learning Outcomes

Upon completion of this module, the students will be able to:

Knowledge

  • Acquire deep knowledge of the notion of risk and its aspects from all points of view, including Mathematical, Psychological, Social and Managerial point of view.
  • Exhibit a deep and thorough understanding of the dual nature of risk combining the physical and human components of risk as well as of the different phenotypes of risk.
  • Understand in depth the function of Risk Management throughout the organization, as well as the role, possibilities and limitations of a Risk Manager.

Comprehension

  • Understand and distinguish between various parameters and metrics of risk, including hazard, exposure, risk shaping factors, as well as probability, severity and risk.
  • Understand and distinguish between the notions of Management and Governance, as well as their implications in Risk Management.
  • Understand and distinguish between different risk treatment strategies, their selection criteria and implications.
  • Comprehend the risk communication process and its parameters and implications.

Application

  • Apply the stepwise process of risk management in all contexts, either in quantitative or qualitative situations.
  • Apply risk communication cycle in any risk situation and use most common models for risk communication.
  • Apply risk perception and human error models to enhance risk assessment.

Analysis

  • Analyze and systematically identify all stakeholders/involved parties, their aspects, interests, power and interactions in order to build the picture.
  • Systematically identify and analyze risks and their qualitative and quantitative parameters (likelihood, exposure, impact) as well as the risk shaping factors.
  • Identify and analyze parameters of the risk communication cycle, as well as their main influencing factors and impact.
  • Identify parameters of risks that affect human factors, either through conscious (risk perception) or unconscious (human error) human acts, either in individual or social context.

Synthesis

  • Use qualitative and quantitative data and information to perform a systematic assessment and prioritization of risks.
  • Identify and combine proper risk treatment strategies to develop a coherent and robust risk treatment plan.
  • Combine information and develop a solid and structured risk communication plan, either in managerial or governance context.

Evaluation

  • Categorize risks according to their characteristics and available information.
  • Evaluate the context for handling risks and select governance or management perspective.
  • Globally evaluate risks combining their systematic risk assessment along with psychosocial parameters.
  • Evaluate risk communication strategies in terms of effectiveness and coherence

Subjects covered:

  • Introduction to Risk and Risk Management
  • Human Aspects in Risk Management
  • Social Aspects in Risk Management

Prerequisites: There are no prerequisites for this module.

Evaluation: Completion of written assignments during the academic semester which constitute a 40 percent of each student’s grade, if a pass is obtained in the final or repetitive examination. Final exam grades constitute a 60 percent of the students’ final course grade.

 

ERM521 Management Principles & Human Resources Management in an environment of risk

Module Code: ERM521
ECTS Credit Points: 10
Module Type: Compulsory
Offered for the academic semester: Second (2nd)
Language: English
Module Outline

General Description:

This Thematic Unit / Module is designed to introduce students to the foundation topics in management and human resources. This knowledge is important for reaching decisions in everyday business within a riskful environment. The decisions aim to maximize profit and to strategically design and prepare a future for the business that will guarantee the existence and hopefully enlargement of profit in the long run. In fact this module will prepare students to forecast the framework of their business for the next day and thus hedge against business risks. In essence the module aims to prepare students as leaders who build business tactics and they will become a lot more than middle managers who rarely blaze trails in unknown areas and uncertain times such as those that businesses are encountered with nowadays.

Overall the module provides principles to foster the goals of the organization, as well as a better understanding of the external business environment in which an organization operates. In this module, future managers will learn how to apply management and human resource tools that increase company’s profitability. The main objective of the module is to train students in: providing students will all the necessary managerial knowledge in an environment of risk, navigating the students through managerial concepts with applications, navigating the students through human resource management tools with applications, acquainting students case studies and real application on these matters. The module will deal with and include basic management principles with a particular focus on the internal organization of the business and the remuneration, reward schemes.

Learning Outcomes:

Upon completion of this module, the students will be able to:

Knowledge

  • Exhibit a deep and thorough understanding of how to apply management and human resources principles and tools to run the everyday business with its profit maximization objectives and how to plan for the future in order to maintain and expand profitability within a risk framework.

Comprehension

  • Understand the difference between programmed and non-programmed decisions and the decision characteristics of certainty and uncertainty.
  • Understand the ideal, rational model of decision making and the political model of decision making.
  • Understand the process by which managers actually make decisions in the real world.
  • Understand the steps in managerial decision making.
  • Understand the biases that drive managers to make bad decisions.
  • Be aware of the theories that explain the effect of compensation on individuals.

Application

  • Apply and explain fundamental managerial and human resource from simple everyday business problems and more complex and strategic frameworks.
  • Recognize various organization schemes within a business and comment on their pros and cons.
  • Recommend solutions in applied business life with respect to organization structures and detect what has gone wrong and what cab ne rectified.
  • Employ human resources management to solve real cases. Recommend solution to real problems.
  • Recommend remuneration schemes that can keep both the employee motivated and satisfied without hindering the profit maximization orientation of the business.
  • Apply techniques for innovative group decision making

Analysis

  • List the steps in the strategic management process
  • List the steps in the decision making process.
  • Analyze the reasons for the controversy over executive pay.
  • Analyze the effects of fundamental pay programmes for recognizing employees contributions to the organization’s success
  • Apply incentive plans in a balanced scorecard.

Synthesis

  • Be able to identify the main decision areas and concepts in employee compensation management.
  • Be able to compare the major administrative tools used to manage employee compensation.
  • Be able to solve problems with jon-based pay structures.
  • Explain the importance of process issues such as communication in compensation management.
  • Identify the major factors to consider in matching the pay strategy to the organization’s strategy.

Evaluation

  • Evaluate the characteristics of different managerial techniques.
  • Evaluate the importance of competitive labor market and product market forces in compensation decisions.
  • Evaluate the significance of process issues as communication in compensation management.
  • Evaluate the design of pay structures and the regulatory framework for employee compensation.
  • Evaluate the advantages and disadvantages of the pay programmes

Subjects covered:

  • Principles of Management with respect to Organization and Planning
  • Human Resources & Management Principles in an environment of Risk

Prerequisites: There are no prerequisites for this module.

Evaluation: Completion of written assignments during the academic semester which constitute a 40 percent of each student’s grade, if a pass is obtained in the final or repetitive examination. Final exam grades constitute a 60 percent of the students’ final course grade.

 

ERM522 Predictive Analytics in Risk Management

Module Code: ERM522
ECTS Credit Points: 10
Module Type: Compulsory
Offered for the academic semester: Second (2nd)
Language: English
Module Outline

General Description:

This Thematic Unit / Module is designed to introduce students to a range of applications of advanced analytics that are suitable in risk management context. The module emphasizes more on how predictive analytics can be effective tools in reducing risk rather than the theoretical underpinnings of the models.

In the last decade, the amount of data available to organizations has reached unprecedented levels. Companies and individuals who can use this data together with analytics give themselves an edge over the competition. Predictive analytics is transforming risk management as it helps organizations by informing them what is arriving in the future. The Module covers a wide area of models and techniques from simple visual models and extending to statistical and machine learning techniques as well as some basic financial risk models. The approach is to focus on practical and conceptual issues involved in substantive applications of risk management.

The main objective of the module is to train students in employing methodologies and techniques for extracting information from existing data in order to determine patterns and predict future outcomes and trends, with an acceptable level of reliability, including what-if scenarios and risk assessment.

Students develop in depth understanding of the key technologies in data science and business analytics: data mining, machine learning, visualization techniques, predictive modelling, and statistics.

Through the study of proper case studies, students will be able to identify the inputs and outputs involved in each modelling approach and the suitability of the models to specific instances, gain practical, hands-on experience with statistics programming languages and big data tools through coursework, and practical assignments.

Learning Outcomes:

Upon completion of this module, the students will be able to:

Knowledge

  • Develop an understanding of the Data Science field with regard to competencies required in areas such as statistics, data analytics, machine learning, data wrangling, data visualization, communication, business foundations.
  • Have a thorough understanding of how analytics are applied to critical tasks facing business decision-making in managing risks.
  • Understand the proper use as well as advantages and disadvantages of the techniques employed in predictive analytics such as visualization, regression, clustering, and classification.
  • Understand the basic principles of machine learning

Comprehension

  • Distinguish between training data, validation data and test data in data analytics.
  • Recognize that different models fit and perform better than others, depending on the circumstances, and can measure fit and performance appropriately.
  • Explain the underpinnings of logistics and nominal regression models and explain their differences from linear regression models.
  • Understand the advantages and disadvantages of Bayesian Learning, complete a Bayesian analysis of a basic problem, and discuss the differences between Bayesian and frequentists models
  • Distinguish between supervised and unsupervised machine learning approaches and identify areas where those can be applied efficiently to mitigate risks.

Application

  • Apply quantitative modelling and data analysis techniques to the solution of real world business problems, communicate findings, and effectively present results using data visualization techniques.
  • Specify and implement models with the following techniques: k-nearest-neighbor, Naive Bayes, Classification and Regression Trees and apply the models in real-world contexts.
  • Use the logistical and nominal regression models, KNN and Bayesian classifiers to classify cases in a given data set.
  • Formulate simple models to solve problems, and implement them using software appropriate for data science work.

Analysis

  • Apply principles of Data Science to the analysis of business problems.
  • Define training and validation data sets to develop a model and measure its validity and identify the optimum model to solve a given problem.
  • In addition to performing exploratory and inferential procedures, students can fit complex models using dedicated statistical software (e.g., R, Minitab, SPSS).
  • Analyse statistical data properly, in order to identify distribution patterns, possible relationships among data attributes, contingencies, and interaction among various factors.
  • Analyse the statistical significance of a logistical regression model, and interpret the contribution of the explanatory variables in prediction and classification.

Synthesis

  • Integrate data from disparate sources, can transform data from one format to another, and can program data management in relational databases.
  • Integrate results from clustering and classification algorithms with qualitative aspects of the problem under consideration in order to provide business solutions.
  • Consolidate and interpret results of statistical analysis of empirical data in context in order to communicate relative information for supporting business decision making.

Evaluation

  • Compare the performance of multiple methods and models, recognize the connections between how the data were collected and the scope of conclusions from the resulting analysis, and articulate the limitations and abuses of formal inference and modelling.
  • Choose appropriate data management strategies, can carry out relevant analyses, can interpret and apply the results to inform understanding and solve specific problems in context, and can communicate the work to a technical audience.
  • Evaluate the “fitness” and the predictive power of logistics and nominal regression model in making predictions and classifications.

Subjects covered:

  • Visualization Models – Decision Making
  • Statistical Models – Logistical & Nominal Regression, Classification models
  • Introduction to Machine Learning Algorithms – (Apriori algorithm for Association rule learning, Bayesian classifiers, K nearest neighbor-KNN).
  • Financial models – Value at Risk, Portfolio risk assessment, CAPM

Prerequisites: ERM512

Evaluation: Completion of written assignments during the academic semester which constitute a 40 percent of each student’s grade, if a pass is obtained in the final or repetitive examination. Final exam grades constitute a 60 percent of the students’ final course grade.

 

ERM523 Risk Management Standards and Techniques

Module Code: ERM523
ECTS Credit Points: 10
Module Type: Compulsory
Offered for the academic semester: Second (2nd)
Language: English
Module Outline

General Description:

This Thematic Unit / Module, is designed to introduce students to the application of the most important methods, tools and Standards applied in Risk Management.

The main objective of the module is to train students in (i) internal and external Risk Management reporting, (ii) application of main Risk Management standards, (iii) use of most important methods and tools in Risk Management.

The Module focuses on providing students with a global picture of the technical aspects used in applied Risk Management. It starts with requirements and methodologies for Risk Management reports. The presentation of the three main Risk Management standards and their application follows. Main conceptual models for risk and main tools for risk analysis are subsequently presented. Finally, the basic methods and techniques for Risk Management are presented.

Learning Outcomes:

Upon completion of this module, the students will be able to:

Knowledge

  • Acquire deep knowledge of the basic conceptual approaches to risk evolution.
  • Exhibit a deep and thorough understanding of how risks are modelled and analyzed and the main principles for risk analysis.
  • Understand in depth the organization requirements and structures for Risk Management, as well as the objectives they serve.

Comprehension

  • Understand and distinguish between various accident models and conceptual approaches to risk situations.
  • Understand and distinguish between the notions of Management and Governance, and their impact on Risk Management structures and standards.
  • Understand the main requirements and structure of a risk report.
  • Comprehend the main methodologies and tools applied in risk analysis.

Application

  • Develop a comprehensive Risk Management report in any context.
  • Apply main risk management tools to model and present any risk situation.
  • Select and apply the proper Risk Management standard in any context.
  • Apply the CORAS method for simple cases of risk management and small enterprises.

Analysis

  • Analyze and systematically identify all available information according to the structure and components of each one of the three main Risk Management standards.
  • Analyze risks, risk shaping factors and treatment options according to the most widely applied risk models and techniques.
  • Analyze quantitative data and perform Monte Carlo simulations.
  • Perform PESTLE/SWOT analysis in the Risk Management context.

Synthesis

  • Synthesize existing information in order to build the organizational structure according to the most important Risk Management standards.
  • Combine existing information to build structures of the most common risk models and tools.
  • Select, combine and synthesize information to create a risk management report according to the receiver it is addressed to.

Evaluation

  • Select the proper risk model or technique to apply to each context.
  • Evaluate compliance of a Risk Management structure to any certain Risk Management Standard.
  • Evaluate reliability and relevance of existing information for risk reporting.
  • Evaluate economic risks according to “Value at Risk” approach

Subjects covered:

  • Risk Management Reporting
  • Risk Management Standards
  • Risk Management Models and Techniques

Prerequisites: ERM513

Evaluation: Completion of written assignments during the academic semester which constitute a 40 percent of each student’s grade, if a pass is obtained in the final or repetitive examination. Final exam grades constitute a 60 percent of the students’ final course grade.

 

ERM611 CRisis Management

Module Code: ERM611
ECTS Credit Points: 10
Module Type: Compulsory
Offered for the academic semester: Third (3rd)
Language: English
Module Outline

General Description:

Module “Crisis Management”, ERM611, is designed to introduce students to evaluation of implications from Crisis Management decision-making theories, frameworks, approaches, and models for organisations in the Digital Society.

The main objective of the module is to train students in:

  • Introduce the concept(s) of crisis, risk evaluation, and risk communication.
  • Critically evaluate key stages in a crisis.
  • Identify and assess socio-cultural, and other factors shaping a crisis, including the role of digital social media.
  • Evaluate models, theories, and emerging professional trends in crisis management.
  • Assess the underlying role of ethics in crisis management for the digital age.

The module focuses on cognitive, conceptual approaches and ways to link theory and practice in the evaluation of issues impacting organizational decision-making about Crisis Management in the digital age. Theories are discussed in relation to actual case studies detailing critical events in organisations and implications are drawn out. Case studies are historical in nature, but where appropriate current crisis management events are used to highlight issues and implications of relevance to the module.

Learning Outcomes:

Upon completion of this module, the students will be able to:

Knowledge

  • Exhibit a deep and thorough understanding of crisis management concepts decision-making theories, frameworks, approaches and models for organisation in the Digital Society.

Comprehension

  • Assess factors shaping a crisis.
  • Distinguish and classify different crisis types.
  • Explain crisis management processes, strategies, and assessments.
  • Explain crisis management concepts, theories, and frameworks.
  • Assess the role and effectiveness of different social media for communication during crisis management.

Application

  • Apply risk / conflict / crisis management techniques to understand case study material.
  • Use crisis management techniques to understand how operational issues and event became a crisis.
  • Use models and theories to understand ethical implications in decision-making during a crisis.

Analysis

  • Analyse issues and events which lead to crisis and require management.
  • Categorise and prioritise crisis factors.
  • Create visual assessment and analysis of crisis.

Synthesis

  • Assemble information for performing crisis assessment.
  • Consolidate and interpret results of research data and information from different sources in context to communicate relative information for supporting business decision making.
  • Design crisis treatment strategies and crisis portfolios.
  • Select the most appropriate model after a comparison among alternatives and interpret crisis management issues and ethics in context providing insight of potential implications for stakeholders.

Evaluation

  • Appraise the appropriateness of various theories, models, and techniques in addressing some crises, ethical responsibilities, and the need for conflict management.
  • Evaluate models for their currency in helping us understand the impact of crises in the Digital Society.
  • Evaluate factors and stages in the evolution of a crisis including impacts on organisational effectiveness and the need for proactive and positive communications

Subjects covered:

  • Concepts of crisis and evaluation of risk and communication of key stages in a crisis.
  • Identification and assessment of factors shaping a crisis; role played by social media.
  • Evaluate models, theories, trends in crisis management and assess role of ethics.

Prerequisites: ERM513

Evaluation: Completion of written assignments during the academic semester which constitute a 40 percent of each student’s grade, if a pass is obtained in the final or repetitive examination. Final exam grades constitute a 60 percent of the students’ final course grade.

 

ERM612 Business Continuity Planning (BCP)

Module Code: ERM612
ECTS Credit Points: 10
Module Type: Compulsory
Offered for the academic semester: Third (3rd)
Language: English
Module Outline

General Description:

This Thematic Unit / Module is designed to advance student knowledge and analytical skills in crisis types, contexts, techniques, protocols, and procedures in Business Continuity Planning (BCP) for Transformational Leadership in the Digital Society.

The Module focuses on practical and conceptual issues in the field of BCP. Context-related challenges are assessed in relation to BCP development and techniques, protocol and procedures are evaluated against specific BCP criteria that fit contextual circumstances, but which also account for standards and good practices in the industry as a whole. Main parts of BCP are presented and the need for exercising, maintaining, and reviewing plans for embedding BCP strategic awareness and action when needed in organisational development of long-term strategy perspectives are discussed. Case studies and published sources are used for analysis and evaluation of organizational contexts and discussions about BCP. Students are also urged to use their own organisation context and/or a specific organizational context they wish to better understand in relation to BCP. An E-Book and research resources are provided to support student learning, but students are also encouraged to engage with research and independent learning under tutor guidance.

Learning Outcomes:

Upon completion of this module, the students will be able to:

Knowledge

  • Exhibit a deep and thorough understanding of Business Continuity Planning (CP) concepts, tools, processes, strategies, BCP comprehensive framework and disaster management life cycle in public and private organisations.

Comprehension

  • Assess internal and external factors of risk to organisational continuity and impacting BCP.
  • Classify crisis types and mitigation strategies in different organisational contexts.
  • Identify main parts of a Business Continuity Plan.
  • Explain BCP processes, strategies, and assessments.
  • Understand the need for planning in relation to prevention, preparedness, response, and recovery (PPRR).
  • Assess the role and effectiveness of communication and information support systems during BCP conceptualisation, implementation, and evaluation.
  • Understand the need for strategic information policy (SIPs) to guide BCP.

Application

  • Apply BCP management techniques to understand case study material.
  • Use BCP techniques to understand how operational issues and events are analysed and represented in BCP outputs to ensure continuity in organisational performance.
  • Use models and theories to understand coordination and decision-making during BCP.
  • Use the BCP Life Cycle in public and private sector organisational contexts.

Analysis

  • Analyse internal and external issues and events which require BCP strategies.
  • Categorise and prioritise crisis factors impacting BCP.
  • Create visual assessment and analysis of crisis for use in BCP activities.
  • Analyse steps in the development of BCP prevention, preparedness, response, and recovery.

Synthesis

  • Assemble information from diverse sources for use in BCP.
  • Consolidate and interpret results of research data and information from different sources in context to communicate relative information for supporting business decision making.
  • Design crisis treatment strategies and crisis portfolios for use during BCP and for evaluation of outcomes.
  • Select the most appropriate model after a comparison among alternatives and interpret crisis issues and ethics in context providing insight of potential implications for BCP stakeholders.

Evaluation

  • Appraise the appropriateness of techniques, protocols, and procedures to address specific crisis events requiring specific BCP criteria.
  • Evaluate models for their currency in helping us understand requirements for BCP in the Digital Society.
  • Evaluate factors and stages in the evolution and periodic review, update, and maintenance of BCP outputs including impacts on organisational effectiveness and the need for proactive and positive communications.

Subjects covered:

  • Introduction to BCP
  • BCP Life Cycle in Public and Private Organizational Contexts
  • BCP Auditing and Acceptance Challenges in Organisations

Prerequisites: There are no prerequisites for this module.

Evaluation: Completion of written assignments during the academic semester which constitute a 40 percent of each student’s grade, if a pass is obtained in the final or repetitive examination. Final exam grades constitute a 60 percent of the students’ final course grade.

 

ERM701A Master thesis i

Module Code: ERM701A
ECTS Credit Points: 10
Module Type: Compulsory
Offered for the academic semester: Third (3rd)
Language: English
Module Outline

General Description:

As part of completing the studies for obtaining a Master’s Degree (Master’s) in Enterprise Risk Management, students must prepare, present and be assessed in a Master’s Thesis. The preparation of the Master’s Thesis is one of the most creative challenges of the program as it offers the student the opportunity to demonstrate that he/she has the ability to use the knowledge acquired in the Master’s Program and to complete a study on his/her own, with the guidance of the Supervising Professor. In addition, it provides the student with the opportunity to explore a topic of interest in depth by applying the rigorous, systematic and scientific approach to problem solving.

This is the first Thematic Unit of the Master’s Theses series, ERM701A.

Prerequisites: ERM512

 

ERM701B Master thesis iI

Code: ERM701B
ECTS Credit Points: 30
Module Type: Compulsory
Offered for the academic semester: Fourth (4th)
Language: English
Module Outline

General Description:

As part of completing the studies for obtaining a Master’s Degree (Master’s) in Enterprise Risk Management, students must prepare, present and be assessed in a Master’s Thesis. The preparation of the Master’s Thesis is one of the most creative challenges of the program as it offers the student the opportunity to demonstrate that he/she has the ability to use the knowledge acquired in the Master’s Program and to complete a study on his/her own, with the guidance of the Supervising Professor. In addition, it provides the student with the opportunity to explore a topic of interest in depth by applying the rigorous, systematic and scientific approach to problem solving.

This is the second and last Thematic Unit of the Master’s Thesis series, ERM701B.

Prerequisites: ERM511, ERM512, ERM513, ERM521, ERM522, ERM523, ERM611, ERM612

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