Business Informatics: Topics for Bachelor's and Master's Thesis

You need a dissertation topic in business information systems and you have run into the basic problem of the subject: your degree sits between management and computing, so both directions are open to you. That freedom is exactly what makes the choice hard.

You find a business information systems dissertation topic by first deciding whether you want to design something or to study something, then narrowing the idea down to one information system in one organisation, and settling before you register where the data will come from: your own survey, an organisation’s system data or official statistics. By the end you will have three suggestions for your supervisor meeting, each with one sentence on where its data comes from.

📌 Key points

• 200 business information systems topics across five fields.
• Design projects and behavioural studies need different data.
• BCS accreditation expects practical computing work in the project.
• The UK has no AI-specific law (as of 2026).
• Acceptance models such as UTAUT supply your survey variables.

The suggestions here are part of our collection of dissertation topic ideas for every subject, where the other degree subjects have their own lists.

Four steps to the right topic: how to find a business information systems dissertation topic

Business information systems topics rarely fail for lack of ideas. They fail because a suggestion stays open in two directions: you could study it as a management question or design something technical around it. Leave that open and you write two half-dissertations.

The 200 suggestions further down are raw material, not finished topics. A suggestion becomes a topic once three things are fixed: the information system, the organisation that runs it and your measure of success.

1. Check the specialisms first, then the topic. Check your programme specification for the specialisms your course offers and the staff who supervise them. A data management topic will struggle to find a supervisor in a group that works on process management, even though both count as business information systems.
2. Decide on the direction: design or study. A design project creates something new, such as a model, a method or a prototype. A behavioural study examines how people and organisations use technology. Both routes are recognised, but they need completely different data.
3. Narrow the field to one system and one organisation. “Artificial intelligence in customer service” is a field. “What handling time does an AI assistant achieve in the first-line support of a housing association?” is a topic.
4. Settle the data source before you register. Note for every suggestion where the numbers will come from. Hoping for a company’s data means you need a commitment, not an idea. Planning your own survey means roughly four extra weeks, plus the time your ethics application takes.

The general steps for any subject are set out in our guide to finding a topic for your dissertation.

Fields and specialisms in a business information systems degree

Business information systems sits between management and computing, and UK universities give it several names: business information systems, business computing, management information systems, or simply information systems. The label business informatics is more common in Germany and Austria. Whatever the name, the subject studies information systems in organisations, from companies to public bodies.

The Subject Benchmark Statement for Computing published by the Quality Assurance Agency for Higher Education (QAA, fifth edition, March 2022) names management information systems among the joint and interdisciplinary courses in which computing is taught. Its appendix states that “Information systems is concerned with the modelling, codification and storage of data and information” so that it can be retrieved and analysed to support decisions, and counts data management, databases, systems analysis and the system life cycle among its main areas.

Which specialisms your own course makes of that is set out in its programme specification. The five fields here group them into the blocks under which topics are usually offered. The data column matters most, because it tells you whose permission you need.

Two neighbouring subjects overlap with this one. Leaning towards algorithms, architecture and implementation? Then the suggestions sit under computer science topics. Leaning towards markets, organisation and performance measures? Try the business and management topics.

200 business information systems dissertation topics across five fields

The 200 topic suggestions on this page are sorted by the five fields above, 40 per field. Each one is a direction rather than a finished title: what is still missing is the organisation, the time period and the data source.

Every field is split into two blocks below so you can find what fits your situation faster. Each block opens with a paragraph on the kind of work it usually demands, so read that before you skim the list. Several suggestions refer to UK rules and schemes, and those need checking again when you register.

Topics on digital business models and platforms

Topics on digital business models and platforms read well and come with a data problem. Revenue and margins of individual providers are rarely public. Two routes work instead: either you study the demand side through a survey of your own, or you work with published annual reports, such as the accounts UK companies file at Companies House, and with market studies.

• Success factors for UK fintech start-ups
• Digital transformation in a traditional family business: a case study
• Platform businesses and their effect on local high streets
• Subscription models compared: an analysis of one industry
• Equity crowdfunding as a source of finance for start-ups
• Business models for sustainable technology in a digital economy
• Blockchain models for transparent supply chains
• Digital business models in healthcare: remote consultations
• Business models in the Internet of Things: a cross-sector analysis
• Online marketplaces: success factors and competitive dynamics
• The effect of open-source software on the software industry
• Growth strategies for software-as-a-service providers
• The on-demand economy: ride-hailing business models
• Drone delivery in logistics: business models and regulation
• Smart farming: digital business models in agriculture
• Esports as a digital business model
• The effect of cryptoassets on established business models
• Data-sharing arrangements as the basis for new SME business models
• Online booking platforms among tradespeople: adoption and barriers
• Moving a software product from a perpetual licence to a subscription

Topics on digital products, pricing and customer behaviour

Topics on digital products, pricing and customer behaviour are the most forgiving if you have no partner organisation. Willingness to pay, acceptance and buying behaviour are things you can ask any target group about yourself. You need no company for that, just enough participants and a questionnaire that captures the constructs cleanly.

• Big data and personalised pricing in online retail
• How social media shapes the development of digital products
• Virtual reality as a sales channel: potential and obstacles
• Business model innovation through smart home technology
• The role of influencer marketing in digital business models
• AI-based recommender systems in e-commerce
• Open Banking apps: trust and adoption among young adults
• How artificial intelligence changes the personalisation of services
• Using data analytics for business model innovation
• Contactless and mobile payments and their effect on spending behaviour
• Digital transformation in education: business models in e-learning
• Augmented reality and the customer experience in retail
• Digital business models for sustainable consumption
• Competitive advantage through digital value-added services
• Gamification strategies in digital business models
• Pay-what-you-want pricing in digital distribution
• Omnichannel strategies for UK high-street retailers
• The role of artificial intelligence in the music industry
• Generative AI in digital products: what users are willing to pay
• Pricing AI features: usage-based or flat-rate

Topics on technical IT security in organisations

Topics on technical IT security almost always require access to practitioners. Organisations do not discuss their weak points in public, so the data comes from interviews held under a promise of anonymity. Purely conceptual work is possible without a partner, for example applying the Cyber Essentials controls to a fictional small business and setting out what it would take to pass.

• Designing an incident response plan for a small or medium-sized enterprise
• End-to-end encryption and its effect on business communication
• Assessing the security of smart home devices on company networks
• Implementing a zero-trust network architecture in practice
• Security concepts for cloud storage in sensitive sectors
• Detecting and preventing insider threats
• Mobile device management in corporate IT
• Use and effectiveness of intrusion detection systems
• Secure software development policies inside an organisation
• Risks and security strategies for IoT devices in manufacturing
• Countering advanced persistent threats in mid-sized firms
• Authentication protocols and their use in company networks
• Securing software supply chains with a software bill of materials
• Uses and limits of encryption techniques
• Patch management in IT infrastructure: processes and metrics
• Risk assessment and risk management in critical national infrastructure
• Security considerations in developing and using APIs
• Strategies against distributed denial-of-service attacks
• What quantum computing means for organisational IT security
• Achieving Cyber Essentials certification in a small business

Topics on data protection, legal duties and new attack routes

Topics on data protection and legal duties have one advantage that makes the effort worthwhile: statutes and regulators’ guidance are freely available and reliable, with UK legislation published on legislation.gov.uk. The catch is shelf life. A dissertation built on an outdated version looks weak, so every legal statement needs the date it refers to.

• UK GDPR and EU GDPR side by side: compliance for firms trading in both
• Adapting company policy to growing data protection requirements
• Security audits and compliance reviews in IT departments
• The effect of artificial intelligence on network security
• Data protection when introducing vehicle telematics
• Security requirements for hybrid working and how firms meet them
• Phishing attacks and prevention: a survey of employees
• Privacy by design for company websites and apps
• Security of payment service providers in e-commerce
• Cloud data security requirements for financial services firms
• Data protection concepts for social media platforms
• Bring-your-own-device policies: can they be enforced day to day?
• Detecting deepfakes in corporate communication
• Security challenges in big data analytics
• Protecting personal data in the cloud: responsibilities and limits
• IT security in the NHS: working with the Data Security and Protection Toolkit
• Which mid-sized firms fall under the UK NIS Regulations 2018?
• Managed service providers and the Cyber Security and Resilience Bill: a readiness study
• Security risks of large language models in the workplace
• Prompt injection as an attack route on AI assistants

Topics on reporting, dashboards and data architecture

Topics on reporting, dashboards and data architecture need real data, otherwise the project ends up as a description of a tool. Two ways out work: an open dataset from a portal such as data.gov.uk, or a partner organisation that lets you have an extract of its system data. Sort out either one before you register, not afterwards.

• Designing a real-time dashboard for sales data
• Business intelligence in HR: analysing staff turnover
• Implementing a business intelligence system in an SME
• KPI systems for connected production equipment
• Business intelligence strategies for customer loyalty schemes
• How self-service BI tools change decision-making
• A model for assessing BI maturity in organisations
• Reporting on smart meter energy data
• Business intelligence for resource planning in local government
• Integrating geographic information systems into BI applications
• Business intelligence to support sustainability decisions
• The effect of business intelligence on organisational culture
• Data warehouse strategies for heterogeneous company data
• BI tools for analysing e-commerce platform performance
• Online analytical processing for multidimensional analysis
• Business intelligence for planning live events
• Real-time business intelligence for steering production
• Data lakes for organisation-wide analysis
• Data mesh or a central data warehouse: decision criteria
• Acceptance of self-service analytics in business departments

Topics on forecasting, data mining and machine learning

Topics on forecasting, data mining and machine learning are the most computation-heavy in the subject. What examiners look for here is an assessment of quality: how accurate is your model, and against which simple baseline? A model without a baseline produces a number nobody can interpret, and that is exactly where these projects come unstuck.

• Machine learning to predict customer behaviour in retail
• Data mining to identify cross-selling potential
• Predictive analytics for stock control
• Improving supply chain management with business intelligence
• Business intelligence for reducing risk in investment portfolios
• Time series analysis for sales forecasting
• Business intelligence in healthcare to improve patient care
• Text mining and sentiment analysis of customer feedback on social media
• BI-supported analysis of market trends for product development
• An early-warning system for falling sales
• Automated anomaly detection in sales figures
• Optimising marketing campaigns through BI-based customer segmentation
• Forecasting models for energy trading
• Customer retention through a BI-based touchpoint analysis
• BI methods for efficiency gains in logistics
• Business intelligence for fraud detection in insurance
• Business intelligence for strategic decisions in start-ups
• Process mining to uncover deviations in a purchase-to-pay process
• Natural-language queries on company data: accuracy and limits
• Data quality as a precondition for AI-based sales forecasts

Topics on IT strategy, governance and IT projects

Topics on IT strategy, governance and IT projects are the natural home of the behavioural study. The question is how organisations make decisions, and no measurement answers that; a survey or an interview does. You need no programming skills here, but you do need access to people who make IT decisions. On a BCS-accredited course, check first whether a study of this kind meets the practical-work requirement for your final project.

• A guide to digital transformation for SMEs
• IT governance in digitalisation: challenges and solutions
• Agile methods in IT project management: success factors and pitfalls
• IT service management frameworks in practice
• IT outsourcing decisions: a critical assessment
• IT compliance: regulatory requirements and their technical implementation
• IT strategy for start-ups in the growth phase
• Governing multi-cloud environments: control and cost
• Insurability of cyber risk: how insurers decide
• IT portfolio management under fast technological change
• Strategies for managing cloud applications
• Scaling effects through software-defined networking
• Measuring the success of IT projects
• Data quality management as the basis for digitalisation projects
• Putting green IT strategies into practice in organisations
• Strategic IT management in high-street banks under pressure from fintechs
• Project management software for steering IT projects
• The role of open-source software in IT management
• Digital sovereignty as a criterion when choosing a cloud provider
• How big data analysis shapes strategic business decisions

Topics on digital change, leadership and AI adoption

Topics on digital change, leadership and AI adoption are easier to research than security topics, because people talk about change projects more openly than about weak points. Respondents are therefore easier to find. Make sure you test a relationship, for instance between training and use, rather than simply describing where an organisation stands.

• The role of the chief information officer in steering digital transformation
• Cloud computing in digital transformation: use and impact
• Change management in IT departments: strategies for implementation projects
• The effect of robotic process automation on business processes
• The spread of artificial intelligence in business processes
• Designing security awareness training and measuring its effect
• How mobile technology shapes business strategy and IT management
• Building a culture of digital innovation: barriers and success factors
• Digital ethics in IT management
• Integrating social media into corporate IT: risks and potential
• Virtual and augmented reality in training and support
• The Internet of Things in manufacturing: management issues
• Technology acceptance models and digital innovation
• Designing an organisation-wide knowledge management system
• Digital leadership: management skills for a digital workplace
• Digital twins and their effect on product development
• Governance for generative AI: policies and whether staff follow them
• A UK SME selling into the EU: preparing for the EU AI Act
• AI skills in the workforce: needs and suitable training
• Introducing low-code platforms: benefits and shadow IT

Topics on ERP, CRM and enterprise software

Topics on ERP, CRM and enterprise software are close to practice and therefore suit a dissertation written with a company or during a placement. They depend on one concrete process: an order, an approval, a complaint. Without that process the topic turns into a general description of software.

• The effect of ERP systems on business process efficiency
• CRM systems to improve customer communication
• Business process management software for continuous improvement
• Optimising procurement through integrated information systems
• Supply chain management systems for more transparency
• Decision support systems for senior management
• Process modelling and simulation to optimise manufacturing
• E-procurement systems to cut process costs
• Lean principles in IT-supported processes
• Designing user-friendly interfaces for process control
• Document management systems to speed up administrative processes
• The effect of cloud computing on process improvement
• Quality management systems for monitoring and improving processes
• The role of information systems in agile organisational structures
• Information systems for compliance monitoring in regulated industries
• Content management systems in marketing processes
• SaaS or running software in-house: a process comparison
• Energy management systems for optimising operations
• IT frameworks for standardising business processes
• Using metrics to find bottlenecks in administrative processes

Topics on automation, documents and interfaces

Topics on automation, documents and interfaces suit a design project, because the result is visible. Measuring is easier here than in other fields: handling time, error rate and throughput time can be recorded before and after the change, provided the organisation releases its log data.

• A workflow management system for process automation
• Mobile apps to support field service staff
• Artificial intelligence for adaptive process controls
• RFID technology to improve warehouse and stocktaking processes
• Integrating IoT devices into existing information systems
• Predictive maintenance to optimise maintenance processes
• Collaboration tools and their effect on teamwork
• IT solutions to support change management
• Chatbots for automating customer interaction
• Augmented reality for maintenance and repair
• Real-time parcel tracking: integrating data across systems
• E-learning systems to speed up onboarding
• Self-service portals to relieve customer service
• Blockchain technology for securing transactions
• Enforcing the four-eyes principle in approval workflows
• AI assistants in customer service: handling time and satisfaction
• Automated invoice processing with document recognition
• Making Tax Digital for VAT: how small firms choose compliant software
• Agent-based automation: where it reaches its limits today
• Process documentation with generative AI: quality of the output

Design project or behavioural study: which kind of topic to choose

Business information systems recognises two routes to a result, and your topic has to commit to one of them. A design project creates an artefact: a model, a method, an architecture or a prototype. A behavioural study explains or predicts how people and organisations deal with information systems.

The distinction has a firm place in the information systems literature. In Design Science in Information Systems Research (MIS Quarterly, 2004), Hevner, March, Park and Ram describe two paradigms behind much of the discipline: behavioural science, which develops and tests theories about how people and organisations behave, and design science, which seeks to extend “the boundaries of human and organizational capabilities” by creating new artefacts.

Signs of a design project

• The topic starts with designing, developing or modelling.
• The outcome is something that did not exist before.
• The evaluation measures the artefact against the goals you set.
• The data comes from testing, not from fieldwork.
• Programming or modelling skills are a precondition.

Signs of a behavioural study

• The topic asks about acceptance, use, impact or success factors.
• The outcome is a tested statement about people or organisations.
• The data comes from a survey, interviews or official statistics.
• A theoretical model supplies the variables.
• Without access to respondents the topic cannot be done.

BCS accreditation and the choice between the two routes

UK degrees add a rule that matters for your choice. BCS, The Chartered Institute for IT, expects an accredited degree to include a substantial individual project, and its guidelines on course accreditation (January 2020) state: “Dissertations based solely on literature review activity and/or user/market surveys are not acceptable.” On a BCS-accredited course, a pure survey study may therefore not count as your final project. Ask your module leader before you settle on a behavioural topic.

Mixed designs are possible and also the more demanding option: building a prototype and then having users evaluate it means doing both, and both take time. On an accredited course a mix can still be the safer route, because the user study sits on top of practical work instead of replacing it. Which route suits your situation is also discussed in our article on choosing between an empirical or theoretical dissertation.

Which business information systems topics need your own data collection

Which business information systems topics need your own data collection is given away by the first word of the topic. Anything asking about acceptance, satisfaction, success factors or use in practice requires statements from people. Everything else comes from system data, published statistics or the literature.

The table below maps the commonest wordings to where their data has to come from. Every row that says “your own survey” means extra lead time: designing the questionnaire, applying for ethical approval and recruiting participants.

Ethical approval comes before the first response

Any survey or interview with people needs ethical approval from your university before you collect a single answer. UWE Bristol puts it plainly: approval must be obtained “before any primary data collection for the project begins”, and the rule covers undergraduate and postgraduate students alike. Your own institution’s research ethics committee sets the details, so look up its process and deadlines when you choose the topic, not when the questionnaire is finished.

Acceptance models supply the variables for your survey

For the rows that need a survey of your own, the established models save you half the questionnaire work. One of the most widely used is the Unified Theory of Acceptance and Use of Technology by Venkatesh, Morris, Davis and Davis (2003), UTAUT for short, summarised in Newcastle University’s open theory collection. UTAUT brings together four constructs: performance expectancy, effort expectancy, social influence and facilitating conditions. According to the authors, the model accounts for 70 per cent of the variance in intention to use.

An established model also connects your project to the literature, because your results can be compared with other studies. Turning the constructs into actual questions is covered in our article on running a survey for your dissertation.

For a standardised survey you need a survey tool, a participation link and a group that answers. empirio.ai, an online survey tool from Germany, is made for students and dissertations.

The four-point test: will your topic stand up academically?

Whether a business information systems topic stands up academically can be checked against four criteria the discipline wrote down itself. The Memorandum on design-oriented information systems research sets out four requirements for artefacts: abstraction, originality, justification and benefit.

The memorandum was written by Hubert Österle, Jörg Becker, Ulrich Frank, Thomas Hess, Dimitris Karagiannis, Helmut Krcmar, Peter Loos, Peter Mertens, Andreas Oberweis and Elmar J. Sinz and published in English in the European Journal of Information Systems in 2011 (volume 20, issue 1). The four requirements appear there word for word and work directly as questions to put to your topic.

1. Abstraction. The memorandum requires: “Each artifact must be applicable to a class of problems.” A concept for exactly one company is consultancy. It becomes research once you say which kind of organisation it applies to.
2. Originality. The second requirement reads: “Each artifact must substantially contribute to the advancement of the body of knowledge.” So check what has already been published on your topic before you register. An implementation plan taken straight from a textbook does not meet this criterion.
3. Justification. The wording here is: “Each artifact must be justified in a comprehensible manner and must allow for its validation.” Every decision in your design needs a reason drawn from the literature or from data.
4. Benefit. The memorandum asks for benefit “for the respective stakeholder groups”, now or in the future. Name early on who gains from your result: the IT department, the business side or the customers.

Applying the four-point test to a behavioural study

For a behavioural study the four points apply by analogy, with the tested statement taking the place of the artefact. Abstraction then means the statement holds not just for the 60 people who answered but for a defined group. Originality means the relationship you test has not already been tested three times in the same setting.

A topic is not academic because it is complicated. A topic is academic because someone else can follow how you got there.

Once the four points are answered, your research question is nearly written. How to phrase it well is explained in our guide to writing a research question.

What makes a business information systems topic current in 2026

A business information systems topic is not current because a technology appears in it, but because the situation around that technology has changed. For UK students two areas show this clearly in 2026: the rules on artificial intelligence, where the UK and the EU have taken different paths, and cyber security, where UK legislation is being reworked.

For choosing a topic that means two things. Topics with a legal angle are easy to back up with sources, because statutes, parliamentary papers and regulators’ pages are freely available. They also date fastest, so every statement of that kind needs a date attached.

AI rules: no UK AI Act, but the EU AI Act still reaches UK firms

The UK has no cross-sector AI law. In a House of Lords Library briefing of 20 May 2026 the position is summed up as “The UK does not have AI-specific legislation or regulators”. AI is governed through existing law in the sector where it is used and through non-statutory principles, and the 2026 King’s Speech contained no cross-sector AI bill.

UK organisations can still fall under EU law. Regulation (EU) 2024/1689, the EU AI Act, covers providers outside the EU that place AI systems on the EU market, as well as providers and deployers whose AI output is used in the EU (Article 2). According to the European Commission, the Act became applicable on 2 August 2026 with some exceptions. Since an amendment that entered into force on 27 July 2026, high-risk rules apply from 2 December 2027 for the use cases in Annex III and from 2 August 2028 for systems in products under Annex I.

Cyber security: the NIS Regulations and a bill still in Parliament

For IT security topics the UK framework in October 2026 is still the Network and Information Systems Regulations 2018, published on legislation.gov.uk as SI 2018/506. Reform is on the way: the Cyber Security and Resilience (Network and Information Systems) Bill had reached the House of Lords by October 2026 and had not yet received Royal Assent. A dissertation on it describes a moving target and has to name the stage of the bill it refers to.

Official figures make the security field easy to justify. The Cyber Security Breaches Survey 2025/2026 published by the Department for Science, Innovation and Technology and the Department for Culture, Media and Sport, based on 2,112 UK businesses and 1,085 charities surveyed between August and December 2025, found that 43 per cent of businesses and 28 per cent of charities reported a breach or attack in the previous 12 months. Phishing was the most common type.

As a rule, supervisors of such topics expect a clear account of the legal position and what it means for the organisation you study, not a legal opinion. Courses with a strong law component are the exception. What binds you is your own university’s assessment regulations. This article is not legal advice.

Topics for an MSc dissertation, a final year project and a placement

Topics for an MSc dissertation, a final year project or a placement project in business information systems differ not in subject matter but in what is expected of method and scope. The same idea carries a smaller project if you describe a process, and an MSc dissertation if you test a hypothesis or evaluate an artefact.

How much room you get is set by your course, and the differences between universities are larger than many students expect. Two published examples show the range for an undergraduate final project.

As of October 2026, taken from the documents linked below.

The programme specification of the University of Westminster for cohorts starting in 2024/25 lists the Business Information Systems Final Project as a 40-credit core module at Level 6 and describes the course as “CITP and partial CEng accredited by the BCS”. Staffordshire University’s module descriptor for the Business Information Systems Management Project sets 30 credits, 300 hours of study and a written report weighted at 80 per cent. Both sit at or above the BCS floor, because the accreditation guidelines expect the individual project to be worth at least 30 credits at Level 6 or above. What applies to you is in your own programme documents.

What an MSc dissertation adds

An MSc dissertation in business information systems usually expects a methodological decision of your own and a justification for it. Applying a method is not enough; you explain why you chose it over the alternatives. In a design project that means the evaluation goes beyond a functional test and checks the artefact against the goals you set at the start.

Topics for a dissertation written with a company

Many business information systems courses offer a placement year, and plenty of students later write their dissertation with the organisation they got to know there. That arrangement shifts the topic problem rather than solving it. The organisation has something to fix, the university wants a research question, and the two rarely match on their own. Treat the company’s issue as your case and frame the question around it. What to watch for in that set-up is covered in our article on writing a dissertation with a company.

Common mistakes when choosing a business information systems topic

The commonest mistakes when choosing a business information systems topic have little to do with the topic itself. They concern what comes after it: data, access and scope. Four of them come up in supervision meetings again and again.

Each of these mistakes can be spotted before you register, and that is the only point at which fixing one is cheap. After registration every correction costs weeks.

The topic is chosen but the data access is not

Topics on ERP systems, process improvement or business intelligence depend on data from an organisation. A verbal yes during a placement interview is not permission for a data export. Before you register, get it in writing which data you will receive, in what format and with whose sign-off. Without that you end up writing a literature review on a topic that was meant to be practical, and on a BCS-accredited course a project like that may not even count.

The topic is a consultancy job, not a dissertation

A digital strategy for exactly one firm is a commission, not a topic. Without generalisation, the abstraction criterion from the memorandum is missing. The fix is small: describe the case as a case and frame the question for a class of organisations. The one firm then becomes, for instance, small engineering manufacturers in the West Midlands.

The data collection is planned too late

A survey is a frequent reason why a dissertation falls behind schedule. The questionnaire is quick to write, the responses are not, and the ethics application has to be approved before the first one comes in. Needing 50 more responses in the week before submission is a problem no tool can solve. Routes to participants that actually work are set out in our article on finding survey participants.

The topic takes on too much technology at once

Blockchain, artificial intelligence and process automation in one undergraduate dissertation add up to three dissertations. Each technology brings its own literature, its own terms and its own yardsticks. Cut the topic down to one technology and one use case. The depth you gain counts for more in the marking than the breadth you give up.

Checklist before you register

• A supervisor at your university covers the specialism.
• The direction is set: design or behaviour.
• The data source is named and confirmed.
• The ethics application is planned into the timetable.
• The four points from the memorandum are answered.
• The timetable allows four weeks for data collection.
• Three suggestions are ready for the supervisor meeting.

Conclusion

Take three of the 200 suggestions that fit a specialism at your university, then choose between them on the data question rather than on interest. On a BCS-accredited course, ask a second question as well: does the topic include practical work with computing technology?

Topics on acceptance, IT management and customer behaviour run on a survey of your own and are therefore easy to plan. Topics on business intelligence and process improvement need an organisation willing to hand over its data, and that is where the bottleneck sits.

What to do next

• Want to know how an empirical dissertation runs from start to finish? Empirical research in a dissertation
• Need a questionnaire for your data collection? How to create a questionnaire
• Looking for a ready-made template to start from? Survey and questionnaire templates

Does your topic need a survey of its own?

Create the questionnaire in a few minutes, share the link with your target group and have the responses analysed straight away.

Create a survey for your dissertation