DIGITAL INNOVATION AND DATA ANALYTICS - 2023/4
Module code: MAN2206
Digital innovation and data analytics are regarded as key for the development of enterprises, destinations and the services sector including hospitality, tourism, transport and events. Being aware of available technologies and being able to use data to extract patterns and generate insights to support strategic decision making are important for enterprises to be able to create innovative solutions to business problems and compete in an increasingly competitive and transparent environment. As such, awareness about digital innovation and data analytics as well as the ability to judge the quality of interfaces are vital to succeed in the social and economic business environment within the services sector, including hospitality, tourism, transport, and events.
This module provides students with an understanding of the importance of digital innovation and data analytics in the services sector. It involves a critical reflection on contemporary strategies, concepts and ideas that currently shape hospitality, tourism, transport and event practices. It also aims to equip students with the fundamental concepts and tools needed to understand the emerging role of business analytics in service organisations and shows students how to apply business analytics tools and to communicate with industry professionals to effectively use and interpret analytic models and results for making better business decisions. The module deals with the importance of consumers' perception of the design of technological interfaces for business success and covers how to critically examine user interfaces and how to make sense of user-based interface evaluation data.
Hospitality & Tourism Management
SCOTT Neil (SII DUFE)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 5
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 105
Lecture Hours: 11
Laboratory Hours: 22
Guided Learning: 11
Captured Content: 1
Prerequisites / Co-requisites
Indicative content includes:
- The importance of digital innovation and data analytics in hospitality, tourism, transport and events
- Overview of data-driven decision making and business analytics
- Fundamentals of descriptive analytics and applications of managerial statistical tools
- Fundamentals of predictive analytics and applications of forecasting and extrapolation
- Fundamental of prescriptive analytics and applications of simulation
- Emerging technologies in hospitality, tourism, transport, and events: virtual reality, artificial intelligence, etc.
- The future of digital transformation in hospitality, tourism, transport, and events
|Assessment type||Unit of assessment||Weighting|
|Project (Group/Individual/Dissertation)||Digital Business Intelligence Dashboard using Tableau (Group)||70|
|Examination Online||Online Exam (1 hour)||30|
Alternative assessment for Digital Business Intelligence Dashboard using Tableau (Group): Digital Business Intelligence Dashboard using Tableau (Individual)
The assessment strategy is designed to provide students with the opportunity build up their knowledge and understanding about developments in the service market due to opportunities digital innovation and data analytics provide and to become aware of the broad range of existing examples of digital transformations. The module is designed to allow students to evidence their achievement of the learning outcomes.
The assessment comprises:
- a group project in which students will transform data into business analytics and business intelligence reporting in the context of organisational decision making and problem solving
- an online exam to evaluate students' understanding of the subjects learnt in the module
Thus, the summative assessment for this module consists of:
- A group project that requires students to put together and present a digital business intelligence dashboard using Tableau
- A closed-book online exam during the final examination period
Formative feedback will be provided as follows:
- During the first lab session, the assignments and the feedback process is explained
- Feedback is also provided during and after in-class discussions/guided activities
- As the lab session and seminar activities are built around topic-specific exercises in a group setting, students do not only benefit from lecturer feedback but also receive peer evaluations
- A pre-assignment feedback session where students will be given feedback on the progress of their project
- Once marking is completed, students are provided with feedback, which contains detailed generic feedback as well as a breakdown of marks; this enables students to assess their own performance compared to their peers
- SurreyLearn discussion boards during the course of the module
- Through online interactions through discussion boards and in class interactions, students can recognise their agency with using and applying feedback to progress learners
- Develop a critical appreciation of the nature, role and importance of digital innovation and data analytics within the services sector, including hospitality, tourism, transport, and events
- Develop an ability to transform data into business analytics and business intelligence reporting in the context of organisational decision making and problem solving in the services sector
- Critically investigate contemporary concepts and ideas related to digital innovation in the services sector
- Critically evaluate the influences of specific technologies on both the demand and supply side of hospitality, tourism, transport, and events
|001||Gain a critical understanding of the impacts of digital innovation and data analytics in creating value propositions for customers and competitive advantage for organisations in the hospitality, tourism, and events industry||CK|
|002||Gain an understanding of how managers use business analytics to formulate and solve business problems and to support managerial decision making in a global service business context||CKP|
|003||Demonstrate an ability to use and apply data analytics software package for data input, analysis, and output||CPT|
|004||Demonstrate an ability to use and apply data analytics software package for data input, analysis, and output||CKPT|
|005||Demonstrate an ability to make data-driven decisions to optimise the business process and address contemporary issues in services||CKPT|
|006||Demonstrate a reflective approach to business intelligence to support sustainability in the services industry||CKPT|
|007||Develop digital capabilities by using and applying data analytics software package for data input, analysis, and output||PT|
|008||Develop resourcefulness and resilience through working with unstructured business problems and data||CKPT|
|009||Equip students with cutting-edge knowledge and skills related to digital innovation and data analytics for employability||CKPT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The learning and teaching strategy is designed to provide students with an overview of digital innovation and data analytics, covering the key areas of data-driven decisions, business intelligence reporting, emerging technologies such as immersive technologies and artificial intelligence. The foundations of business analytics will be supplemented with practical projects using an analytical software package to input and analyse data and generate and interpret insights for managerial decision making. The module will focus on providing students with a combination of the necessary conceptual knowledge and practical skills to make critical and strategic business decisions in a global context, with an emphasis on business sustainability in the services sector, including hospitality, tourism, transport, and events.
The learning and teaching methods include:
- Theoretical lectures with supporting materials from a range of perspectives within digital innovation and data analytics in the context of services, including hospitality, tourism, transport, and events
- In-class exercises, practical examples and topical case studies, and lab sessions to apply knowledge and analytical skills to real-world issues and datasets
- A project in which students extract patterns from big data and interpret the results to generate business solutions through a business intelligence dashboard
- Data analytics coaching sessions
- Supporting guest lecture sessions
- Formative feedback sessions
- Independent learning through reading, practising with the software, collaborative activities, and research associated with the assessments
Students are expected to take responsibility for their own learning within a supportive environment, developing self management skills along the way, building their resourcefulness and resilience.
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.
Upon accessing the reading list, please search for the module using the module code: MAN2206
Surrey's Curriculum Framework is committed to developing postgraduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills and capabilities in the following areas:
Digital Capabilities: This module focuses on developing students¿ critical evaluation of digital innovation and emerging technologies and their capabilities in analysing data and capturing business insights for managerial decision making. Students will develop their skills in using descriptive and predictive analytics and data visualisation. They will learn how to use a software to create business analytics dashboard. Students will use the virtual learning environment (VLE), SurreyLearn, video conferencing platforms such as Zoom and Microsoft Teams, and data analytics software to facilitate learning. These include accessing teaching and learning materials and engaging with their instructors and peers. Module assessments require students to work collaboratively with peers to analyse datasets using a software, Tableau, and to create a dashboard to present insights from the datasets.
Employability: This module will introduce different types of digital innovation, emerging technologies and data analytical skills that can change the future of workplaces and thus equipping students with cutting-edge knowledge and skills for their employability. The assessments in this module require students to extract business insights from big datasets and to create a dashboard, using Tableau, to present the managerial implications of these insights. These business analytics and data-driven decision-making skills will prepare students to be successful managers in the digital age.
Sustainability: Students will learn how to reflect on digital innovation and data analytics to support sustainability in the services industry.
Resourcefulness and Resilience: Students will be required to use a range of sources to identify relevant datasets, conduct independent research and work collaboratively with peers to extract patterns and critical insights from datasets. Finding solutions through unstructured problems is the key learning aspect of this module that will develop students¿ resourcefulness and resilience.
Global and Cultural Capabilities: Students will learn how to different types of digital innovation and emerging technologies that can lead to digital transformation in the services industry and visitor economy in a global context. Students will also learn how to interpret results of business analytics and their implications to service business in a global context by extracting, comparing, and contrasting individual and group behaviours, as well as sectoral, national, and regional differences captured from big datasets.
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 2023/4 academic year.