MANAGER DECISION MAKING AND INSIGHT - 2019/0

Module Overview

This module looks at manager decision-making and the creation of organisational value in the era of &lsquoBig Data&rsquo. We take a qualitative look at data science and big data technologies using a decision-making perspective to give MBA's an overview of how these disruptive technologies are changing their industries. Decisions such as digital service design decisions, digital personalisation options, market segmentation decisions, consumer online support decisions, commercial partnering and networking decisions, product development decisions and business model change decisions. 

 

We focus on the strategy of how new digital technologies are completely changing business government and society. So there is no programming and no in-depth maths in this module. A main objective of the module is to help MBA's to understand how to use these technologies to help their own firms and their own careers. This is reflected in the assignments because MBA's can choose the focus of the assignments, in consultation with the lecturer.

 

Big Data promises to become a fundamental source for competitive advantage and a driver for the creation of value in the business organisation, through the support to manager decision-making, automation of multiple business functions and the promise for emergent products, services and markets.   It will shape the way management works in the coming years and how the organisation carries out its fundamental business functions.  This module looks at how managers can strategically use new digital data technologies in their firms &ndash such as Big Data, data science and data scientists, the Internet of Things, new digital services and AI. The emphasis of the module is to present the benefits and pitfalls of new digital data technologies to managers as they strive to create competitive advantage and value for their organisations. 

Module provider

Surrey Business School

Module Leader

SHAW Duncan (SBS)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Module Availability

Semester 2

Prerequisites / Co-requisites

N/A

Module content

Indicative content includes:

• Emergence of Big Data and Managing Organizational Knowledge


• Learning - Individual, Team, Organization, and Machine


• Manager Decision Making using digital data: Concepts and Topics


• Problems and Issues in using digital data in firms


• Managing the data that helps Managers make decisions


• Combining Data and Human decision-making: How AI is changing business


• Ethics in light of Big Data e.g. data privacy


• Emergent trends and perspectives for Manager decision-making


• Emergent trends and perspectives for the environment: Uncertainty vs. Complexity -trends that impact the organization and the economy at large, e.g. Digital Ecosystems, competition from different sectors, disruptive business models


• New web technologies and digital start-ups


• The Internet of Things (IoT)


• How firms use customer data to make profits


• Strategies for Big Data Analytics


• Digital services

Assessment pattern

Alternative Assessment

Alternative to Group Presentation - Assess the business potential of free Saas or Web services tools for an industry or specific organisation of your choice (1500 words)

Assessment Strategy

The assessment methods include two assignments – one 15 minute group presentation (30%) and one individual project report of 3000 words (70%).

It is anticipated that these assignments will demonstrate the development of qualitative analytical abilities, using concepts derived from Information Systems theory, service theory, value creation theory, business model theory, ecosystem theory and other business school literatures, directed towards business problem-solving in an international environment.

 

I. Group presentation (15 minutes) (30%):

The group assignment will ask the students to showcase new digital web technologies that they have found in order to demonstrate their use in a business of their choice. MBAs will build real systems for recruiting customers, communicating to customers, working with customers, collaborating with colleagues, gathering and using digital data and producing digital services.

No coding will be required because modern web start-ups make using their services feel just the same as using normal MS Word or Excel, i.e. no programming just mouse clicks and templates. MBAs will explore, find and harness the services that can be found on the web to make one or more websites which will form a foundation for attaching many software-as-a-service (saas) products and web services. E.g. plug in maps and other functionality, social media services, analytics services and other modern uses of digital data to produce personalised customer experiences – both B2C and B2B  

 

The MBA groups will be introduced to these technologies at the start of the module. The lecturer will support them group by group as they develop their presentation as the module progresses[SD1] . The presentation will consist of demonstrations not slides. MBAs will be assessed on how well they showcase a large number of new technologies, i.e. how well they use examples and dummy data to show how the technologies can help their chosen businesses. In this assessment the theory is embodied in the functionality and capabilities of many new technologies not in the implementation of a theory-based analysis, like it is in the Individual project report.

 

II. Individual Project (3000 words):

Students will choose a business and a Research Question and provide specifics on a business (e.g. their own or a past employer) where they will need to analyse and develop solutions using the concepts and materials lectured and discussed in the class sessions.

 

The assignments are due by third Monday after completion of the module (FT) and by the fourth Monday after completion of the module (PT).

 

Formative assessment and feedback

Ongoing participation in the class discussion of the pre-readings. The Teaching sessions in the module will be a combination of lecture and discussions.

Students will receive written comments for the group presentation.

Students will receive written comments for the submitted individual project report.

The feedback process will incorporate not just the grades but advice to facilitate learning.

The teaching staff are available during office hours and are happy to give individual feedback on the lectures and course materials during those hours.

Module aims

• The module is structured in two broad sections.
• The first section covers topics associated with the use of new digital web technology and digital Data in businesses. It combines topics such as the different ways that new web start-ups and mature firms are now using the Web and the Cloud to gather and process data at a huge scale. It covers how managers can generate customer and other digital data and then use it – plus the aspects, advantages and issues of the use of Big data in the business organisation.
  
  This section also looks at the implications and challenges of a fast moving business environment dominated by data intensive tools and applications. It covers current developments in the domain of cooperative work, information intensive products (including the Internet of Things) and the commercial use of social media. It also questions fundamental aspects of organization, work and ethics in a world of Big Data.
• The second section provides an introduction to learning, knowledge and human (manager) decision making in digital businesses. It introduces the diverse concepts and issues in that topic space and distinguishes between IT and human learning and decision-making.
  
  This section provides many practical examples from different industries together with a theoretical base for analysing how managers produce, use and profit from using modern digital data technologies, digital services, disruptive business models and data analytic strategies. There is a special emphasis on how firms partner with other firms to share data and other resources and on using emerging Internet of Things technologies.
• The aim is for MBAs to learn how to take advantage of these new data technologies and use them to benefit their careers and their firms.

Learning outcomes

Methods of Teaching / Learning

Total student learning time 150 hours.

The learning and teaching strategy is designed to focus on learning by doing and reflection.

The learning and teaching methods include:

• Theories applied to company case examples and examples based on MBAs interests and experience


• Teaching concepts and then learning through group work to analyse real business situations


• Presentations MBAs and Q&A sessions


• Development of real web solutions using free web technologies for collaboration, social media marketing, data gathering, data analysis and process automation


• Workshop activities conducted in teams


• Self-directed learning


• Video examples

The module consists of regular lectures and discussion sessions (typically, 2.5 hours each). The lecture component will introduce the topics, concepts, and relevant issues and problems. The discussion will look at critical issues and examples of the topics from the lectures in order to facilitate student understanding and the overall mastering of the material. Students will be expected to have read the assigned readings prior to each session in order generate a discussion of the concepts. Students will be expected to read materials outside of the regular class sessions (environmental scanning) in order to improve their environmental awareness and ability to work with concepts in the module.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: MANM342

Programmes this module appears in

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2019/0 academic year.