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

You need a thesis topic in business informatics, or information systems as most US schools call the major, and you run straight into the field's basic problem. Information systems sits between business and computer science, so both directions are open to you. Having that much choice is exactly what makes the decision hard.

You find an information systems thesis topic by first deciding whether you want to design something or study something, then narrowing the idea to one information system in one organization, and settling before your proposal where the data comes from: your own survey, system data from a company, or public statistics. By the end you will have three ideas to take to office hours, each with one sentence on where its data comes from.

📌 Key points

• 200 information systems topics across five fields.
• Design science and behavioral research need different data.
• A prototype without an evaluation is not a result.
• Surveys of people usually need IRB review first.
• Acceptance models such as UTAUT supply your survey variables.

The suggestions here are part of our overview of thesis topic suggestions for every major, where the lists for the other programs live as well.

Four steps to the right topic: how to find an information systems thesis topic

The search for an information systems topic rarely fails for lack of ideas. It fails because an idea stays open in two directions at once: you could study it as a business question or you could design a technical solution for it. Leave that undecided and you end up writing two half theses.

The 200 suggestions further down are raw material, not finished topics. A suggestion turns into a topic once three things are fixed: which information system you are working on, which organization runs it, and how you will measure whether something worked.

Four steps from a field to an information systems topic

Each of the four steps below rules out one way an information systems topic can fail: no advisor, no clear direction, too broad a scope, or no data. Work through them in order, because the later steps only make sense once the earlier ones are settled. Many students jump straight to the third step and never get to the fourth.

1. Check the concentrations before the topic. Look in your program's course catalog for the concentrations it offers and the faculty who advise them. A data management topic will not find an advisor in a group that works on process management, even though both count as information systems.
2. Pick a direction: design or study. A design project creates something new, such as a model, a method or a prototype. A behavioral project studies how people and organizations deal with technology. Both routes are accepted in the field, but they need completely different data.
3. Narrow the field down to one system and one organization. “Artificial intelligence in customer service” is a field. “How much faster does an AI assistant resolve first-level support tickets at a municipal utility?” is a topic.
4. Settle the data source before your proposal. Write down for each idea where the numbers will come from. Counting on company data means you need a commitment, not a hope. Planning your own survey means roughly four extra weeks, more if the IRB has to review it first.

The general process for any major, step by step, is laid out in our article on finding a topic for your thesis.

Fields and specializations in an information systems degree

The fields of information systems follow from what the discipline studies: information systems in companies, in government and in everyday life, and the way people and organizations use them. In the United States the major runs under several names, including information systems (IS), management information systems (MIS) and computer information systems, and it is usually housed in a business school rather than a computer science department.

What a program actually teaches is set out in its course catalog, and many programs follow the joint curriculum guidelines of the two professional bodies in the field. The IS2020 competency model from the Association for Computing Machinery (ACM) and the Association for Information Systems (AIS), released on June 3, 2021, names 19 competency areas for undergraduate programs, 10 of them required and 9 elective. Among them are data and information management, IS project management, systems analysis and design, secure computing, data and business analytics, and business process management.

Five fields for an information systems thesis

The five fields in this article group those competency areas into the blocks under which thesis and capstone projects are usually offered. Each field allows a design project as well as a behavioral study. The column that matters most is the one on data, because it tells you early on whom you will have to ask and how long that might take.

Two neighboring majors overlap with these fields. If you lean toward algorithms, architecture and implementation, you will find the right suggestions among our computer science thesis topics. If you lean toward markets, organizations and financial metrics, look at the business administration topics instead.

200 business informatics topics for an information systems thesis, sorted by field

The 200 topic suggestions on this page are sorted into the five fields above, with 40 suggestions per field. Each one is a direction, not a finished title. What it still lacks is the organization, the time frame 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 short paragraph on the kind of work it usually demands. Read that paragraph before you skim the list, because it tells you whether you need a company, a survey or neither.

Topics on digital business models and platforms

Topics on digital business models and platforms sound good and come with a data problem. Revenue and margins of individual companies are rarely public, except for public companies, which file annual reports with the SEC. Two routes hold up: either you study the demand side with a survey of your own, or you work with published 10-K filings, investor materials and market studies.

• Success factors for fintech startups
• Digital transformation at a traditional company: a case study
• Platform economies and their effect on local markets
• Subscription models compared: an industry analysis
• Crowdfunding as a way to finance early-stage startups
• Business models for sustainable technologies
• Blockchain models for transparent supply chains
• Telehealth as a digital business model
• Business models in the Internet of Things: a cross-industry analysis
• Digital marketplaces: success factors and competitive dynamics
• How open-source software shapes the software industry
• Growth strategies for software-as-a-service providers
• The on-demand economy: ride-hailing business models compared
• Drone delivery: business models and FAA regulation
• Precision agriculture: digital business models on US farms
• Esports as a digital business model
• How cryptocurrencies affect traditional business models
• Data clean rooms as the basis for new advertising business models
• Online booking platforms among plumbers, electricians and other trades
• Moving a software product from perpetual licenses to subscriptions

Topics on digital products, pricing and customer behavior

Topics on digital products, pricing and customer behavior are the most forgiving for students without a company partner. Willingness to pay, adoption and buying behavior are things you can ask any target group about yourself. You do not need an employer for that, just enough participants and a questionnaire that measures the constructs cleanly.

• Big data and personalized pricing in online retail
• How social media shapes the development of digital products
• Virtual reality as a sales channel: potential and limits
• Smart home technology as a source of business model innovation
• Creator and influencer marketing in digital business models
• AI-based recommender systems in e-commerce
• Adoption of mobile banking apps among college students
• Artificial intelligence and the personalization of services
• Data analytics as a driver of business model innovation
• Mobile wallets and their effect on purchasing behavior
• Business models in online education: from MOOCs to coding bootcamps
• Augmented reality and the in-store customer experience
• Digital business models for sustainable consumption
• Competitive advantage through digital value-added services
• Gamification in digital products and loyalty programs
• Pay-what-you-want pricing in digital distribution
• Omnichannel strategies for brick-and-mortar retailers
• Artificial intelligence in the music industry
• Generative AI features in digital products: users' willingness to pay
• Pricing AI features: usage-based or flat fee

Topics on technical IT security in organizations

Topics on technical IT security almost always need access to practitioners. Companies do not discuss their weak spots in public, so the data comes from interviews conducted under a promise of anonymity. Purely conceptual work is possible without a company, for example applying the NIST Cybersecurity Framework to a fictional small business and justifying every choice.

• Designing an incident response plan for a mid-sized company
• End-to-end encryption and its effect on corporate communication
• Security of smart home devices on corporate networks
• Implementing a zero trust architecture in practice
• Security concepts for cloud storage in regulated industries
• Detecting and preventing insider threats
• Mobile device management in enterprise IT
• Intrusion detection systems: deployment and effectiveness
• Secure software development policies inside a company
• IoT devices on the factory floor: risks and safeguards
• Defending against advanced persistent threats
• Authentication protocols in enterprise networks
• Securing the software supply chain with a software bill of materials
• Encryption techniques: where they help and where they stop
• Patch management in IT infrastructure: processes and metrics
• Risk assessment and risk management for critical infrastructure IT
• Security issues in API design and use
• Strategies against distributed denial-of-service attacks
• What quantum computing means for enterprise security
• Applying the NIST Cybersecurity Framework 2.0 in a small business

Topics on privacy, compliance and new attack paths

Topics on privacy and compliance have one advantage that makes the extra reading worth it: statutes, agency rules and official guidance are freely available and reliable. The catch is shelf life. A thesis built on a superseded version looks bad, so every legal statement you make needs the date or version it refers to.

• How the GDPR affects US companies with European customers
• Adapting company policies to the California Consumer Privacy Act
• Security audits and compliance reviews in IT departments
• How artificial intelligence changes network security
• Privacy questions when a company introduces fleet telematics
• Security requirements for remote work and how companies 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
• Privacy programs 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
• Personal data in the cloud: who is responsible for what
• HIPAA and IT security for patient data in small medical practices
• How public companies word cybersecurity incident disclosures on Form 8-K
• Board oversight of cyber risk in 10-K filings: an industry comparison
• Security risks of large language models inside companies
• Prompt injection as an attack path on AI assistants

Topics on reporting, dashboards and data architecture

Topics on reporting, dashboards and data architecture need real data, or the thesis turns into a description of a software product. Two ways out work: an open dataset from a portal such as data.gov, or a company partner that lets you have an extract of its system data. Sort out either one before your proposal, not after it.

• Designing a real-time dashboard for sales data
• People analytics: analyzing employee turnover with business intelligence
• Introducing a business intelligence system at a small business
• Metrics for connected production equipment
• Business intelligence strategies for customer loyalty programs
• Self-service BI tools and their effect on decision-making
• A model for assessing BI maturity in organizations
• Reporting on energy consumption data in smart grids
• Business intelligence for resource planning in city government
• Integrating geographic information systems into BI applications
• Business intelligence in support of sustainability decisions
• How business intelligence affects company culture
• Data warehouse strategies for heterogeneous company data
• BI tools for performance analysis of e-commerce sites
• Online analytical processing for multidimensional analysis
• Business intelligence in event planning
• Real-time business intelligence for steering production
• Data lakes for company-wide analysis
• Data mesh or central data warehouse: decision criteria
• Adoption 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 field. What your committee will look for is an assessment of quality: how accurate is your model, and compared with what simple baseline? A model without a baseline produces a number nobody can interpret, and that is exactly where these projects tend to fail.

• Machine learning to predict customer behavior in retail
• Data mining to identify cross-selling potential
• Predictive analytics for inventory management
• Supply chain optimization with business intelligence methods
• Business intelligence for risk reduction in investment portfolios
• Time series analysis for sales forecasting
• Business intelligence in healthcare to improve care delivery
• Text mining and sentiment analysis of customer feedback on social media
• Market trend analysis with BI to guide product development
• An early warning system for falling sales
• Automated anomaly detection in sales metrics
• Customer segmentation with BI to improve marketing campaigns
• Forecasting models for wholesale electricity trading
• Customer retention seen through a touchpoint analysis
• BI methods for efficiency gains in logistics
• Business intelligence for fraud detection at insurance companies
• Business intelligence for strategic decisions at startups
• Process mining to uncover deviations in the purchasing process
• Natural-language queries on company data: accuracy and limits
• Data quality as a prerequisite for AI sales forecasts

Topics on IT strategy, governance and IT projects

Topics on IT strategy, governance and IT projects are the natural home of behavioral research. The question is how organizations decide, and no measurement answers that, only a survey or an interview does. You do not need programming skills here, but you do need access to people who make decisions about IT.

• A digital transformation playbook for mid-sized companies
• IT governance during digitalization: problems 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 startups in the growth phase
• Managing multi-cloud environments: governance and cost
• Cyber insurance: what insurers check before they cover a company
• IT portfolio management when technology moves fast
• Strategies for managing cloud applications
• Scaling effects of software-defined networking
• Methods for measuring the success of IT projects
• Data quality management as the foundation of digital projects
• Green IT strategies and how companies put them into practice
• Strategic IT management at banks under pressure from fintechs
• Project management software for steering IT projects
• The role of open-source software in IT management
• Vendor lock-in 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 especially easy to research right now, because so many organizations are in the middle of introducing new systems. Respondents are easier to find than for security topics. Make sure you test a relationship between two things rather than just describing where a company stands.

• The CIO's role in steering digital transformation
• Cloud computing as a driver of digital transformation
• Change management in IT departments during system rollouts
• Robotic process automation and its effect on business processes
• How widely companies use artificial intelligence in their processes
• Designing security awareness training and measuring its effect
• Mobile technology, business strategy and IT management
• Building a culture of digital innovation: barriers and success factors
• Digital ethics in IT management
• Social media inside the company: risks and opportunities
• Virtual and augmented reality in training and support
• The Internet of Things in manufacturing: management issues
• Technology acceptance models in the context of digital innovation
• Designing a company-wide knowledge management system
• Digital leadership: the skills managers need
• Digital twins and their effect on product development
• Governance for generative AI: policies and whether employees follow them
• Mapping a company's AI use to the NIST AI Risk Management Framework
• AI skills in the workforce: training needs and suitable programs
• Low-code platforms: real benefits and shadow IT

Topics on ERP, CRM and enterprise software

Topics on ERP, CRM and enterprise software are close to practice, which makes them a good fit for a thesis or capstone with a company. They live on a specific workflow: a purchase order, an approval, a customer complaint. Without that workflow the topic turns into a generic description of a software package.

• ERP systems and the efficiency of business processes
• CRM systems for better customer communication
• Business process management software for continuous improvement
• Streamlining procurement with integrated information systems
• Supply chain management systems for transparency in the supply chain
• Decision support systems for senior management
• Process modeling and simulation to improve manufacturing
• E-procurement systems to cut process costs
• Lean management principles in IT-supported processes
• User-friendly interfaces for process control
• Document management systems to speed up administrative work
• Cloud computing and its effect on process improvement
• Quality management systems for process monitoring and improvement
• Information systems in agile organizational structures
• Information systems for compliance monitoring in regulated industries
• Content management systems in marketing workflows
• SaaS versus on-premises operation for process improvement
• Energy management systems in day-to-day operations
• IT frameworks for standardizing business processes
• Finding bottlenecks in administrative processes with metrics

Topics on automation, documents and interfaces

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

• Designing a workflow management system for process automation
• Mobile apps for field service teams
• Artificial intelligence for adaptive process control
• RFID for warehouse and inventory processes
• Integrating IoT devices into existing information systems
• Predictive maintenance to improve maintenance processes
• Collaboration tools and their effect on teamwork
• IT solutions that support change management
• Chatbots for automating customer interactions
• Augmented reality for maintenance and repair work
• Real-time shipment tracking: data integration across system boundaries
• Learning management systems and faster employee onboarding
• Self-service portals to take load off customer service
• Blockchain technology for securing transaction processes
• Enforcing segregation of duties in approval workflows
• AI assistants in customer service: handling time and satisfaction
• Automated invoice processing with document recognition
• Electronic invoicing at small US businesses: adoption and hurdles
• Agent-based automation: where it still hits its limits
• Process documentation with generative AI: how good is the output?

Design science or behavioral research: which kind of topic to choose

Information systems recognizes two established routes to a result, and your topic has to commit to one of them. Design science research creates an artifact: a model, a method, an architecture or a prototype. Behavioral research explains or predicts how people and organizations deal with information systems.

The split is not a teaching convenience but the way the discipline describes itself. In their much-cited paper “Design Science in Information Systems Research” (MIS Quarterly, 2004), Alan Hevner, Salvatore March, Jinsoo Park and Sudha Ram write that two paradigms “characterize much of the research in the Information Systems discipline”. According to the paper, behavioral science aims to develop and verify theories that explain or predict human and organizational behavior, while design science aims to extend what people and organizations can do by creating new artifacts.

How to recognize a design science topic

• Your topic starts with designing, developing or prototyping.
• At the end there is something that did not exist before.
• The evaluation measures the artifact against the goals you set.
• The data comes from testing, not from a field study.
• Programming or modeling skills are a prerequisite.

How to recognize a behavioral topic

• Your topic asks about adoption, use, impact or success factors.
• At the end there is a tested claim about people or organizations.
• The data comes from surveys, interviews or official statistics.
• A theoretical model supplies the variables.
• Without access to respondents the topic cannot be done.

Mixed designs are possible and also the more demanding option. Building a prototype and then having users rate it means doing both, and both take time. In a one-semester senior capstone that rarely works out. Which route suits your situation is also the subject of our article on choosing between an empirical or a theoretical thesis.

Which information systems topics need a study of your own

Which information systems topics need a study of your own is given away by the first word of the topic. Anything that asks about adoption, satisfaction, success factors or use in practice needs answers from people. Everything else comes from system data, published statistics or the literature.

The table below maps the most common wordings to where their data has to come from. Every row with your own survey on the right means extra lead time: drafting the questionnaire, getting consent forms and IRB clearance in place, finding participants.

Acceptance models hand you the survey variables

For the rows that need your own survey, the established models are worth a look, because they save you half the questionnaire work. The most widely used is the Unified Theory of Acceptance and Use of Technology by Venkatesh, Morris, Davis and Davis (2003), known as UTAUT. UTAUT combines four constructs: performance expectancy, effort expectancy, social influence and facilitating conditions. According to its authors, the model accounts for 70 percent of the variance in usage intention.

A tested model also connects your thesis to the literature, because your results can be compared with other studies. How the constructs become actual questions is explained in our guide to running a survey for your thesis.

IRB review for surveys and interviews

Surveys and interviews with people count as human subjects research at US universities, and that means your institution's IRB gets a say before you collect anything. The federal Common Rule lists an exemption for research that only involves survey or interview procedures, provided certain conditions are met, for example that responses are recorded so that participants cannot readily be identified, in 45 CFR 46.104(d)(2). Whether your study qualifies is decided under your own institution's procedures, not by you, so ask your advisor how to file.

For a standardized 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 thesis work.

The four-question test: will your topic hold up academically?

Whether an information systems topic holds up academically can be checked against four questions that the field itself has formulated. Hevner, March, Park and Ram set out seven guidelines for design science research in MIS Quarterly in 2004, and the first four of them work directly as a test for a thesis topic: artifact, problem relevance, evaluation and research contribution.

The remaining three guidelines, on research rigor, design as a search process and communicating the results, matter more once you are writing. For choosing a topic, the first four are enough. Take each one as a question and answer it in a sentence before you meet your advisor.

1. Artifact. The paper asks for “a viable artifact”, which can be a construct, a model, a method or an instantiation, meaning a working system. Ask yourself what exactly will exist at the end that did not exist before. “Recommendations for company X” is not an artifact.
2. Relevance. The artifact has to address “important and relevant business problems”. Name early who benefits from your result: the IT department, a business unit or the customers. A problem nobody has is not made relevant by a clever solution.
3. Evaluation. The guideline calls for the artifact's utility, quality and efficacy to be “rigorously demonstrated”. Decide at the topic stage how you will show that your solution works, and against what you will compare it.
4. Contribution. Hevner and his coauthors expect “clear and verifiable contributions”. A concept for exactly one company is consulting work. Your topic becomes research once you say which class of organizations it applies to and what it adds to what has already been published.

The four-question test for a behavioral thesis

For a behavioral thesis the four questions apply in a slightly different form, and the tested claim takes the place of the artifact. Contribution then means the claim holds not just for the 60 people who answered your survey but for a named population. Relevance means someone needs the answer, and evaluation means your measures come from a validated model rather than from questions you invented the night before.

A topic is not academic because it is complicated. A topic is academic because someone else can follow how you reached your answer.

Once the four questions are answered, your research question is almost written. How to phrase it so that it can actually be answered is covered in our article on writing a research question.

What makes an information systems topic current in 2026

An information systems topic is not current because a technology appears in it, but because the situation around that technology has changed. For US students two developments stand out: the European Union's AI Act, which reaches every company that offers AI systems on the EU market, and the federal rules and frameworks that now shape cybersecurity at American companies.

For your choice of topic, that cuts both ways. Topics with a regulatory angle are unusually easy to support, because statutes, rules and agency pages are open to everyone. They also date faster than anything else, so every such statement needs a date attached.

The EU AI Act and its deadlines for US companies

The EU AI Act, Regulation (EU) 2024/1689, entered into force on August 1, 2024 according to the European Commission, and has applied in large part since August 2, 2026. The prohibitions and the AI literacy obligation have applied since February 2, 2025, and the rules for general-purpose AI models since August 2, 2025. An amendment that entered into force on July 27, 2026 moved the high-risk deadlines: December 2, 2027 for the use cases in Annex III and August 2, 2028 for high-risk systems built into products under Annex I.

Inside the United States the federal reference point for AI is voluntary guidance, not a statute. The National Institute of Standards and Technology (NIST) released its AI Risk Management Framework on January 26, 2023 and describes it as intended for voluntary use. A thesis on a US company that sells into Europe can compare the two, as long as it names the version and date of each.

SEC disclosure rules and NIST CSF 2.0 for security topics

For IT security topics, one particularly useful development is the SEC's cybersecurity disclosure rule. The Securities and Exchange Commission adopted it on July 26, 2023: public companies report a material cybersecurity incident under Item 1.05 of Form 8-K within four business days of determining that it is material, and describe their cyber risk management and board oversight in the annual 10-K. Every one of those filings is public, which turns them into a ready-made dataset for a thesis.

The second reference point is the NIST Cybersecurity Framework 2.0, released on February 26, 2024. Version 2.0 adds a sixth function, Govern, to Identify, Protect, Detect, Respond and Recover, and is written for organizations of every size, not just critical infrastructure. That makes it a good yardstick for a design project at a small business.

As a rule, an advisor does not expect a legal opinion on topics like these, but a careful description of the rules and what they mean for the organization you study. Programs with a strong law or compliance track can be the exception. Your program's requirements and your advisor have the final word. This article is not legal advice.

Topics for a master's thesis, a capstone project and a thesis at a company

Topics for a master's thesis, a senior capstone and an honors thesis in information systems do not differ in subject matter, but in what is expected of method and scope. The same idea carries a capstone project if you analyze and improve one workflow, and a master's thesis if you test a hypothesis or evaluate an artifact.

How much time and credit you get is set by your program, and the differences between schools are bigger than most students expect. Three examples from published catalogs show the range.

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

The undergraduate capstone at the University of Texas at Dallas, ITSS 4395 Capstone Senior Project, has student teams work as consultants on a project of industry interest and finish with a final project report. The thesis option at the University of Nebraska Omaha spreads 6 thesis hours over two semesters, requires a committee of three graduate faculty members, one of them from outside the department, and ends with a public oral defense. The MS in Information Systems at UT Arlington sets its thesis option at a minimum of 30 semester hours, including 6 hours of thesis. What binds you is always your own program's catalog.

What a master's thesis in information systems adds

A master's thesis in information systems generally requires 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. For a design science thesis that means the evaluation goes beyond a functional test and checks the artifact against the goals you set at the start. Expect your committee to ask about exactly that.

Topics for a thesis or capstone at a company

A thesis or capstone with a company is common in information systems, and it does not solve the topic problem, it only moves it. The company has a request, your program needs a research question, and the two rarely line up on their own. Treat the company's request as your case and write the question one level above it. What to settle with the company before you start is covered in our guide to writing your thesis at a company.

Common mistakes when choosing an information systems topic

The most common mistakes when choosing an information systems topic have little to do with the topic itself. They concern what comes after it: data, access and scope. Advisors see the same four again and again.

Each of these mistakes can be spotted before your proposal is approved, and that is the only moment at which fixing it is still cheap. Once the proposal stands, every correction costs weeks.

The topic is chosen before the data access is settled

Topics on ERP systems, process improvement or business intelligence depend on data from a company. A friendly yes in an internship interview is not permission to export data. Get it in writing before you commit: which data you will receive, in what format and with whose sign-off. Without that, you end up writing a literature review about a topic that was meant to be hands-on.

The topic is a consulting job, not a thesis

A digital transformation plan for exactly one company is an assignment, not a research topic. Without a step toward generalization, the research contribution that Hevner and his coauthors ask for is missing. The fix is small: describe the company as your case and frame the question for a class of organizations. The one company then becomes mid-sized equipment manufacturers, or regional hospitals, or community banks.

The survey is planned too late

A survey is the most common reason a thesis falls behind schedule. The questionnaire is quick to write, the responses are not, and the IRB step comes before either. Needing 50 more responses in the last week before submission is a problem no tool can solve. Which routes to participants actually work is covered in our guide to finding survey participants.

The topic takes on too much technology at once

Blockchain, artificial intelligence and process automation in one senior thesis add up to three theses. Every technology brings its own literature, its own terminology and its own standards for judging results. Cut back to one technology and one use case. The depth you gain counts for more in grading than the breadth you give up.

Checklist before your proposal

• Your concentration matches a faculty member who advises it.
• The direction is set: design science or behavioral.
• The data source is named and confirmed in writing.
• The four questions from Hevner et al. are answered.
• Your timeline holds four weeks for a survey plus IRB review.
• Three ideas are ready to take to office hours.

Conclusion

Take three of the 200 suggestions that fit a concentration at your own school, then choose between them on the data question rather than on interest alone.

Topics on adoption, IT management and customer behavior run on a survey of your own, which makes them easy to plan once the IRB has cleared them. Topics on business intelligence and process improvement need a company willing to hand over its data, and that is where the bottleneck sits.

What to do next

• Want to know how an empirical thesis runs from start to finish? Empirical research in your thesis
• Need a questionnaire for your study? 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 your own?

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

Create a survey for your thesis