Researching Strategy & Marketing PDF

Researching Strategy & Marketing Papers for Exam Table of Context {#table-of-context.Kopvaninhoudsopgave} ================ [Lecture notes 3](#lecture-notes) [Lecture 1 3](#lecture-1) [Lecture 2 6](#lecture-2) [Lecture 3 10](#lecture-3) [Lecture 4 13](#lecture-4) [Lecture 5 16](#lecture-5) [Lecture 6 21](#lecture-6) [Lecture 7 26](#_Toc168912620) [Paper summary 31](#_Toc168912621) [Article 1 Lean startup: operationalizing... 31](#article-1-lean-startup-operationalizing) [Introduction 31](#introduction) [Theory 31](#theory) [Methods 31](#methods) [Results: LSC -\> performance 32](#results-lsc---performance) [Discussion: What is lean startup? 32](#discussion-what-is-lean-startup) [Discussion: how does LSC affect performance? 32](#discussion-how-does-lsc-affect-performance) [Limitations: 32](#limitations) [Article 2 An agile co-creation process 33](#article-2-an-agile-co-creation-process) [Introduction 33](#introduction-1) [Theoretical background 33](#theoretical-background) [Methods 34](#methods-1) [Findings 35](#findings) [Contributions 36](#contributions) [Article 3 Business models 37](#article-3-business-models) [Business Models 37](#business-models-1) [Article 4 Harnessing the potential of AI 39](#article-4-harnessing-the-potential-of-ai) [Introduction 39](#introduction-2) [Literature review 39](#literature-review) [Theoretical Background, conceptual model & hypotheses 40](#theoretical-background-conceptual-model-hypotheses) [Methodology 41](#methodology) [Results 41](#results) [Discussion 42](#discussion) [Conclusion 43](#conclusion) [Article 5 Impact of Online vs Offline acculturation 44](#article-5-impact-of-online-vs-offline-acculturation) [Introduction 44](#introduction-3) [Literature review 44](#literature-review-1) [Research model & hypotheses development 45](#research-model-hypotheses-development) [Methodology 46](#methodology-1) [Data analysis and findings 46](#data-analysis-and-findings) [Discussion 47](#discussion-1) [Conclusion 48](#conclusion-1) [Article 6 How AI encourages consumers to share secrets 49](#article-6-how-ai-encourages-consumers-to-share-secrets) [Introduction 49](#introduction-4) [Literature review 49](#literature-review-2) [Theoretical background and research propositions 50](#theoretical-background-and-research-propositions) [Discussion 51](#discussion-2) [Conclusions and future research 52](#conclusions-and-future-research) [Article 7 Effective marketing communication 53](#article-7-effective-marketing-communication) [Introduction 53](#introduction-5) [Literature Review and Research Hypotheses: 53](#literature-review-and-research-hypotheses) [Method 53](#method) [Results 54](#results-1) [Conclusions, Implications, and Limitations: 55](#conclusions-implications-and-limitations) Lecture notes ============= Lecture 1 --------- **Scientific contribution** - There is a gap in the literature - There is inconsistency amongst studies - Challenges appear from social development or business practice - To understand a topic or phenomena better **Social science can be messy** - Science of (collections of) people, and their individual or collective behaviours. - psychology, sociology, economics, management, marketing - Social sciences are generally ambiguous and imprecise (vs. natural) - "Universal" answers do not exist - Unobservable constructs (like poverty, happiness) - Moving between what is abstract and what is observable - There are habits, conventions, but also good practices **Scientific research** 1\. contributes to a body of science. - Discover laws and postulate theories to explain a phenomenon - Advancing knowledge through **logic** and **evidence** 2\. follows the scientific method. - Data are collected and interpreted systematically **Research paper structure** **Before developing a research paper, these elements should be addressed:** - **SIGNIFICANCE** -- make sure your paper confronts or contributes to a grand challenge - **NOVELTY** -- make sure your paper adds new insights or creates a novel approach - **CURIOSITY** -- spark the interest of readers, challenge expectations & solve the mystery - **SCOPE** -- choose an adequate scope, tell a complete story that is feasible - **ACTIONABILITY** -- generate actionable insights that create impact. **Develop an introduction** Clearly introduces your topics, engages in discussion with other scholars/practitioners, highlights your contributions, and demonstrates how the research goals will be achieved [3 sets of questions: ] 1. Who cares? What is your exact research topic and RQ, why should we care studying it, why is it important for theory and practice? 2. What do we know, what do not we know, so what? What are the main theoretical and empirical foundations of your study, what are key unexplored topics, controversies, or paradoxes that you address. Why does it need to be actually explored? 3. What will we learn? How does your study change, challenge or improve the current understanding about the topic? **What is a scientific method?** - Standardized set of techniques - how to make valid observations - how to interpret results - how to generalize results. - Gives trust in results that test theories and prior findings. - Methodology that is logical, confirmable, repeatable, scruitinizable - Example: representative sample **Managing two main research paradigms** 5 sequential stages of deductive research process (Robson, 2002): 1. Deduce a hypothesis from a theory 2. Express the hypothesis in operational terms 3. Test the operational hypothesis 4. Examine the specific outcome 5. ![](media/image2.png)If necessary, adjust theory in light of findings **Types of scientific research** - Exploratory research - New areas - Generates initial ideas about a phenomenon - Test feasibility of further research - Descriptive research - Observations and detailed documentation - What? Where? When? - Explanatory research - Explanations of observed phenomena, problems, or behaviors - Why? How? - Causal factors and outcomes [Plagiarism:] "The representation of another author\'s language, thoughts, ideas, or expressions as one\'s own original work. **Research ethics: what is right and wrong? Code of conduct for scientists** - Voluntary participation (informed consent), harmlessness - Anonymity and confidentiality - Disclosure - BMS Ethics Committee - GDPR **Thinking like a researcher requires practice** - Unit of analysis, unit of observation - Concepts, constructs, variables - Independent, dependent, control variables, interactions, moderators, mediators - Propositions, hypotheses, theory & models - Whom to study? What/how to measure? How to collect data? **Generalizable method** To ensure credible results, make sure that your research design is: - Reliable: where your data collection and/or analysis techniques generate consistent findings, i.e. \(1) Do your measures yield the same result on other occasions? \(2) Will other researchers reach the same observations? \(3) Are you transparent how the findings were interpreted? - Valid: where your findings truly indicate the proposed causal relationship Generalizable: external validity indicating the degree to what your findings can be equally applicable in other research settings, e.g. other countries, ventures Lecture 2 --------- Starting point Value-in-use is: - Providing **utility** (i.e., solving a problem) - **Context-dependent** - Subjectively **perceived** by the costumer Value co-creation is: - Complex - Dynamic Facilitating value co-creation involves: - Subjectivity - Complexity - Chaos Main question of today: how can firms (and individuals) strategically manage value co-creation in entrepreneurial and innovative settings? Managing value co-creation *Philosophies* - **Agile** - Iterative philosophy as described in Agile manifesto - Focused on incremental development - Aims to increase development pace & flexibility - **Design Thinking** - Human-centered approach to problem-solving - Focuses on understanding the needs and perspectives of people affected by the solution - Emphasizes empathy, creativity, and iteration *Methods* - **Scrum** - Project management framework for software development - Dominant development method in IT context - Documented in Scrum framework and includes practices, roles, events, artifacts, and rules - **Lean startup** - Agile method for business model innovation - Validated learning through build- measure-learn cycles - Aims to minimize risk and waste while maximizing customer value Agile: the ability to create and respond to change in an uncertain and turbulent environment **Four agile guidelines:** - Individuals and interactions, over processes and tools - Working software, over comprehensive documentation - Customer collaboration, over contract negotiation - Responding to change, over following a plan The agile approach tries to minimize the risk of not meeting customer needs, taking a step-by-step approach: In practice, the agile approach speeds up traditional big data projects and improves outcomes - Traditional - Long time to market - Teams work in silos with multiple handoffs - Lack of focus - Limited emphasis on delivering business value - Agile - Faster product design - Frequent iterations to deliver a 'minimum viable product' - Small focused, cross functional teams aligned with business goals - Strong emphasis on continuous improvement and delivering business value Scrum is only one of many methods that follow the agile values and principles, but they are not the same. - Agile: high level set of values defining how to work - Scrum: very concrete practice to develop a product (mainly developed in IT industry) Design thinking process - **Empathize** with you customer - Understanding the problem space: goals, behavior, experiences. - **Defining** the problem space - A set of prioritized problem statements of multiple stakeholders - **Ideating** a solution, let's get creative - Quantity over quality - Diversity is key - Focus on novelty - Every voice at the table - Let go of judgement - Developing a **prototype** - (Online) mockup - Paper prototype - Storyboard/Animatics - (PowerPoint) wireframe - Understand, converse, inspire - **Testing** your solution - Plan - Execute - Analyze *Design thinking: an iterative approach. The aspects can be executed in intertwined together.* **Lean startup** Successful startups follow five lean startup principles: 1. Entrepreneurs are anywhere 2. Entrepreneurship means management 3. Validated learning 4. Build-Measure-Learn 5. Innovation accounting ![](media/image4.png) The build-measure-lean feedback loop is the key element of the lean startup approach -- get feedback from the market as early as possible. - Idea - Build - Product - Measure - Data - Learn Generate validated learnings as soon as possible in the market! Experiment examples: - Split (A/B) testing with online shop changes (e.g., compare conversion rates) - "Fake it until you make it" - Offering product sketches online - Join trade fair with a PPT and test potential customer reactions Start with the Minimum Viable Product (MVP) and generate feedback for each hypothesis at a time. Test your hypotheses: - "Will anybody use an outdoor multi tool?" - "Will anybody pay for an outdoor multi tool?" - "Is a corkscrew an appreciated feature?" The vision stays, everything else pivots based on the outcomes of the validated learning process - Product -\> experiments show no approval in the market: - Optimization - Strategy -\> ongoing optimizations show no improvement: - vision -\> keep your vision Lecture 3 --------- **Elements of a theory** - What are the concepts, constructs and variables? - How are they related? Using logic to formulate propositions (abstract) & hypotheses (empirical) - What are the boundaries of the theory - All studies published in marketing journals contain these elements - Section names: "theory development", "theoretical/conceptual framework", "literature review" **Exam** A model will be presented explain what's illustrated - For example, moderator & mediator (as explained during RDMS) - Identify different types of variables within a proposed research model **Types of variables** ![](media/image6.png)INDEPENDENT AND DEPENDENT VARIABLES - **Independent** variables explain other variables - **Dependent** variables are explained by other variables **Mediator:** shows the connection between two variables. - Full mediation occurs when the mediator explains the entire relationship between the independent and dependent variable. - Partial mediation occurs when the mediator explains part of the relationship, but not all of it. **Moderator**: may be acting upon two variables, changing the strength and direction of that relationship. **Common approaches to theory building** - INDUCTION: Build a theory based on observed patterns. Identify predictors (antecedents) and outcomes (consequences) to the phenomenon - DEDUCTION: Apply existing theory in a new context, by drawing on similarities and differences between contexts. Modify existing theories to explain a new context **Different research objectives:** - Define new concepts, constructs, phenomena (INDUCTION) - Test new connections between defined concepts (DEDUCTION) - E.g. Kizgin (2020) show that offline acculturation mediates the relationship between online acculturation and purchase intentions. the argument is based on definition of 'offline acculturation' **Social media marketing** - Traditional one-way advertising = Bowling approach - Social media marketing = Pinball approach **Select a research topic** - Without a clear research goal, it is difficult to plan & execute research - Papers can be an inspiration - A topic should be: - Feasible and worthwhile - Not too risky - Something you are excited about **Writing an introduction** - Introduction is NOT a literature review - Many authors write the introduction in the last step - Do not embellish! Write what you did. - Use precise language - "The goal of this paper is\..." - "The specific research questions are\... 1), 2), 3)" - Explain the research goal early **Elements of paper introduction** - Provides a convincing story (PITCH) - General background - Specific background - Research questions - Knowledge gap/problem statement - Aims for your research project - Research contributions (theory + practice) and limitations - Paper organization **Good research question** - Clear, specific, researchable - Not too broad, not too narrow - With a potential to make a contribution to knowledge **How to write a conceptual framework** - Do you need a separate section? - Literature review -- focus on academic contributions - Theoretical framework -- focus on development of hypotheses - Draw the relationship between concepts / constructs / variables - What is your IV, DV, moderator, mediator, interactions? - What are the common theories used in the field? **Article search strategy** - Google search, Scopus, and library (!!!) - Iterative process (define keywords, search, refine) - Journal articles, review articles, fewer non-scientific sources - Use a reference manager: e.g. Mendeley, EndNote **Critical literature review** ![](media/image8.png)What has already been done & how? What has not been done? **Writing critical literature review** - Tradeoff: completeness vs. selectivity - Focus on issues & findings relevant to your research questions - Organize by issue, not by paper! - Logical structure & progression - Explain why / how the concepts are connected - Explain how previous work relates to your research - Article comparison in form of a table **Critically evaluate the papers** - Relevance for your topic (keywords, abstracts) - Quality (where/when published, \# citations) - Chartered ABS Journal Guide Lecture 4 --------- **Possible exam questions (important topics previous weeks & articles):** 1. **What comes to your mind when you think of starting your research?** Knowing the field of the research subject area and see what has been already contributed to literature 2. **What does the Cronbach's alpha tell us?** Coefficient of reliability i.e. consistency, that is, how closely related a set of items are as a group. 3. **What does validity refer to?** Refers to how accurately a method measures what it is intended to measure- accurate correspondence the intended. - Internal measures the accuracy within the items. - External examines whether the study findings can be generalized to other contexts, so, drawing conceptual paper but don't collect data proposing a model to be tested. 4. **What is the purpose of a Systematic Literature Review?** This review identifies, evaluates, and synthesizes research results to create a summary of current research that can contribute to an understanding and provide avenues for future research. (justify research gap) 5. **Why is it important to identify research gaps?** A research gap is a question or a problem that has not been answered by any of the existing studies or research within your field. Sometimes, a research gap exists when there is a concept or new idea that hasn\'t been studied at all. 6. **How can you identify a research gap?** Research gaps can be identified by citation analysis, systematic reviews and in the introduction section of research articles and finally in the discussions and future research sections in research papers or journals which researchers have already published. *(4 and 5 are pretty similar, but slightly differently asked read question carefully)* **Reading papers strategy** 1. Abstract, introduction a. Motivation (p.1-2) b. Goal, and RQs (p.2) c. Contributions d. Paper organization 2. Conceptual development + hypotheses 3. Constructs measurement Variable operationalization What is Digital Marketing? "Digital marketing" is the process of **building and maintaining** customer relationships through online activities to facilitate the exchange of ideas, products, and services that **satisfy** the goals of both parties. - Risks: mistakes do not disappear easily, it means that you need to be sure enough that relationships are being build. Digital marketing \| why are people going online? - Information on product, service, location etc. - Have a question or looking for help - If you need information - Meeting attendance - Network ( business & personal) - Jobs - Peers etc. etc\...\..... Growth of the web, more people connected every day Digital Marketing is a necessity for many organizations. - E.g. small businesses wanting to trade online and make a name **Benefits of digital marketing** - Puts the consumer in control - Provides convenience (time and place does not matter) - Increases satisfaction (electronic word of mouth) - Builds brands & Drives brand loyalty (satisfaction leads to loyalty) - Reduces the selling cycle - Reduces the cost of sales - Provides targeted results - It is measurable (over traditional marketing) - Cost effective **What does digital marketing consist of?**\ *Key components* - Website design (user experience) - Search engine optimization (SEO)\* - Pay per click (PPC)\* - Social Media Marketing (SMM)\* - Email marketing - Display advertising (banner ads) - Affiliate & Content marketing - Online reputation management (ORM) **Towards a strong theoretical framework** Analyse your research results 1. Start by providing a brief (statistical) recap of your search results - Most recent and oldest publication from selected 2-5 articles - Journal names publishing you selected - Methodology used in your selected literature - Number of times a certain concept has been addressed, e.g. concept A: addressed in 3 out of 5 articles, while concept B: addressed in 4 out of 5 articles 2. Concept matrix building a matrix of concepts upon the theory (gives foundation) 3. Report your finding - State the main findings of the paper - Present empirical evidence - Logical connection to the next paper **Stages of systematic literature review** Stage 1 -- Planning the review - Phase 0 -- Identification for the need for a review - Phase 1 -- Preparation of a proposal for a review - Phase 2 -- Development of a review protocol Stage 2 -- Conducting a review - Phase 3 -- Identification of research - Phase 4 -- Selection of studies - Phase 5 -- Study quality assessment - Phase 6 -- Data extraction and monitoring progress - Phase 7 -- Data synthesis Stage 3 -- reporting and dissemination - Phase 8 -- The report and recommendations - Phase 9 -- Getting evidence into practice **Time dimension** - Cross-sectional Study -- observations represent a single point in time, a cross section of a population - Longitudinal study -- involves collection of data at different points in time - Trend study -- a characteristic of some population is monitored over time - Cohort study -- a specific subpopulation, or cohort, is studied over time - Panel study -- data are collected from the same set of people at several points in time - Panel mortality -- the failure of some panel subjects to continue participating in the study - Cross-sections leverage the differences between individuals - Longitudinal designs explore the differences over time Lecture 5 --------- #### Business models Business model definition "A business model is a simplified and aggregated representation of the relevant activities of a company. It describes how marketable information, products and/or services are generated by means of a company\'s value-added component. In addition to the architecture of value creation, strategic as well as customer and market components are taken into consideration, in order to achieve the superordinate goal of generating, or securing the competitive advantage. To fulfill this latter purpose, a current business model should always be critically regarded from a dynamic perspective, thus within the consciousness that there may be the need for business model innovation, due to internal or external changes over time." Can generate competitive advantage for existing and new businesses **Business model components** - Strategic components - Strategy model - Resources model - Network model - Customer & market components - Customer model - Market offer model - Revenue model: Revenue streams, revenue differentiation - Value creation components - Manufacturing model: Manufacturing model, value generation - Procurement model: Resource acquisition - Financial model: Financing model, capital model, cost structure model **Examples of business model components** - Razor-razor blade model A dependent good is sold at a low price and a paired consumable good generates the profits. - Rolls-Royce jet engines and maintenance Question: Which of the previously mentioned business model components is the most relevant in the two examples above?\ → Answer: Revenue model Business model canvas - Design or architecture of a firm's value creation, delivery, and capture The activity system design framework **Framework provides insight by** - Giving business model design a language, concepts, and tools - Highlighting business model design as a key managerial/entrepreneurial task - Emphasizing system-level design over patrial optimization **Design elements** - Content: what activities should be performed? - Structure: how should they be linked and sequenced? - Who should perform them, and where? **Design themes** - Novelty: adopt innovative content, structure, or governance - Build in elements to retain business model stakeholders, e.g. customers - Complementarities: bundle activities to generate more value - Efficiency: reorganize activities to reduce transaction costs #### Servitization **It's all about service** *Everybody is in services* "There is no such thing as service industries. There are only industries whose service components are greater or less than those of other industries. Everybody is in services." (Levitt, 1972) Question: Which industries (or companies) with great service components have emerged recently? → Possible answers: - Streaming (Netflix, Amazon Prime, Disney+, etc.) - Sharing Economy (Uber, Lyft, AirBnB) **Buying result not ownership** What happens to ownership? - No need to buy something to access the benefits - 'Hiring' pineapples in the 17th and 18th century in England Servitization definitions - A trend in which manufacturing firms adopt more and more service components in their offerings - Change process wherein manufacturing companies embrace service orientation and/or develop more and better services, with the aim to satisfy customer's needs, achieve competitive advantages and enhance firm performance - Change process whereby a manufacturing company deliberately or in an emergent fashion introduces service elements in its business model ![](media/image10.png) **Downstream is where the money is** Winning in the aftermarket **Installed base** - Ratio new sales to installed equipment - High ratio in mature industries - Example of trains: 1:22 - *Total cost of ownership* (for the customer) - Total Cost of Ownership (TCO) is a financial estimate of the direct and indirect costs of a product over its entire lifecycle -\> Initial purchase price + costs associated with acquiring, operating, maintaining, and disposing of the product. **Servitization in practice** - Phillips -- signify - Selling light as a service - Pay-per-Lux at Schiphol airport - Buying light, not light fixtures/fittings - Caterpillar - Cat product link solution - Manufacturer of construction and mining equipment - Remote equipment monitoring solution service - Updates on the location of equipment - Preventative maintenance monitoring of components - Alstom -- Train life services (TLS) - Manufacturer of high-speed trains, trams, metros, etc. - Prioritizes performance for its clients, based on the principle of "lost customer hours" - Financial penalties - Condition-based maintenance ROLE OF CUSTOMERS **Customers in Servitization** Customer versus provider perspective - Involving customers finds roots in innovation and general marketing concepts - New product development/new service development - Voice of Customer (VoC) - Preparedness and willingness of customers is important: - Adopting solutions - Sharing knowledge and skills - Integrating resources **Approaching Servitization and collaboration** Different value propositions 1. Customer that wants to do it themselves 2. Customer that wants us to do it with them 3. Customer that wants us to do it for them ![](media/image12.png)**Passive and active customers** - Less engaged due to lack of time, money, or incentives - Little human-to-human interaction → higher technology-mediated interaction - Remote monitoring of machinery is a provider-dominated service - Willingness to change internal processes and routines to support provider - Ability to share operational information, providing feedback and using self service options **Provider-customer relationships** - Roles in provider-customer relationships need redefinition due to the nature of co-creating services in servitization - Roles provide actors with a set of behavioral rules - Absence of clear roles leads to ambiguity - Developing effective exchanges within relationships becomes increasingly difficult - Examples of customer roles: - Co-diagnoser - Co-designer - Co-implementor Lecture 6 --------- **What are the constructs/variables which influence social media use (example YouTube)** - Information search - Entertainment **Similar exam question** Explain concretely what's a determinant **4 elements of a research paper by Kronemann** - Research objective: can be found in the abstract will be elaborated in the introduction - Unit of analysis: (in the group assignment develop a conceptual model) - Ensuring: quality and avoid bias - Final: screening and selection Validity: the representation of what you're actually testing - Internal validity: showing the validity you're actually doing the test - External validity: when you're not testing you want someone else to test it. **Exam model will be presented (similar to Kronemann)** - Structure - Show topics which were discussed in the literature review - As a result, you know what to expect in the paper - How many Independent Variable: 4 (3 on the left and 1 in the middle) - Dependent variable: 2 (middle and outcome) - Moderators: 0 - Mediators: attitude towards the conversational agent it's mediating anthropomorphism, privacy concerns, and personalisation. In order to claim/test mediator. Antro intention. First their needs to be a direct relationship; otherwise you cannot mediate ![](media/image14.png)**Deduction vs Induction** [5 sequential stages of deductive research process] 1. Deduce a hypothesis from a theory 2. Express the hypothesis in operational terms 3. Test the operational hypothesis 4. Examine the specific outcome 5. If necessary, adjust theory in light of findings **Practice Exam Questions- (NOTE THIS IS BASED ON PREVIOUS YEARS ARTICLES)** ![](media/image16.png) [Methodology] ![](media/image18.png) [Explaining models] ![](media/image20.png) **Open question** Very soon you will be starting with your master studies and your master thesis will be the highlight of this journey. The MSc BA programme aims to equip students with the necessary competences to conduct high-quality and impactful research, followed by the call of the Dutch Research Council to strengthen the reputation of Dutch science in the world. Q3.1 Mention an example of relevant (a) individual-level variable, AND (b) organisational-level variable, AND (c) eco-system-level variable. Please also provide a short operationalisation to each of these variables. That is, how each of these three variables can potentially be measured. Q3.2 Develop one (1) example of relevant hypothesis with a causal effect by using one of the characteristics that you mentioned in the answer above **Q3.1** \(a) Individual-level variable: **Example:** Job Satisfaction **Operationalization:** Measure job satisfaction on a scale from 1 to 5 through a survey where employees rate their overall satisfaction with their current job, including factors like workload, recognition, and work-life balance. \(b) Organizational-level variable: **Example:** Organizational Culture **Operationalization:** Use a validated organizational culture assessment tool that measures key aspects such as innovation, teamwork, and leadership style within the organization. Responses could be collected through surveys or interviews with employees. \(c) Ecosystem-level variable: **Example:** Industry Competitiveness **Operationalization:** Develop an index that combines factors like market concentration, technological innovation, and regulatory environment to quantify the competitiveness of the industry. Data can be gathered from industry reports, market analysis, and expert opinions. **Q3.2** **Hypothesis:** *\"Increased job satisfaction among employees (individual-level variable) is positively associated with a more innovative organizational culture (organizational-level variable), which, in turn, leads to higher industry competitiveness (ecosystem-level variable).\"* **Explanation:** The hypothesis suggests that as job satisfaction (individual-level) increases, it will have a causal effect on fostering a more innovative organizational culture (organizational-level). In turn, this innovative culture is expected to contribute positively to the industry competitiveness (ecosystem-level). This hypothesis implies a cascading effect where improvements at the individual and organizational levels are hypothesized to impact the broader ecosystem **\ ** **Q3.3 Please concisely elaborate your ideal master thesis project that you would be willing to conduct in order to strengthen the reputation of Dutch science in the world. Specifically, please mention:** **A) What is the relevant research problem of your master thesis** **B) Which data analysis technique would you choose, and clearly explain WHY** **C) What is the main expected finding of your master thesis?** A. Social media marketing has been an upcoming phenomenon for the last decade, and businesses come up with more innovative ways to sell their products to customers, but this use of SMM could have serious effects on impulsive buying behaviour. People might not even notice how much they are purchasing, just because of SMM. These purchases are not thought through and are made out of impulsive decisions. I would like to research the effects of social media marketing on impulsive buying behaviour. B. I would firstly, like to collect the data through a quantitative method, such as surveys. No, to analyse this data, I would like to use descriptive statistics to draw conclusions from this data [A) Relevant Research Problem:] **Research Problem:** Investigating the Impact of Sustainable Business Practices on Organizational Performance in the Dutch Manufacturing Sector. **Rationale:** The research aims to address the growing importance of sustainability in business and its potential impact on organizational performance. This is crucial for both academic understanding and practical implications for Dutch manufacturing firms. [B) Data Analysis Technique:] **Data Analysis Technique:** Structural Equation Modeling (SEM). **Why SEM:** SEM is chosen due to its ability to analyze complex relationships among multiple variables simultaneously. It allows for the examination of both direct and indirect effects, making it suitable for exploring the intricate interplay between sustainable business practices and various dimensions of organizational performance. SEM is well-suited for this study as it accommodates latent constructs and provides a holistic understanding of the relationships involved. [C) Main Expected Finding:] The main expected finding of the master thesis is to identify a positive and significant relationship between the adoption of sustainable business practices and enhanced organizational performance in the Dutch manufacturing sector. The study aims to provide empirical evidence supporting the idea that integrating sustainability into business strategies can lead to improved financial, environmental, and social performance for Dutch manufacturing firms. This finding is expected to contribute valuable insights for both academia and industry, reinforcing the reputation of Dutch science in the global research community and emphasizing the importance of sustainable practices in business. []{#_Toc168912620.anchor} Lecture 7 --------- **Difference between methodology and methods** - **A methodology** is the rationale for the research approach/design, and the lens through which the analysis occurs. - Put differently, a methodology describes the general research strategy that outlines the way in which research is to be undertaken - *E.g., quantitative, or qualitative?* - **A method** is simply the tool used to answer your research questions - How, in short, you will go about collecting and analysing your data. - *Large-scale survey or small-scale interviews* - *Statistical analysis (e.g., regression) or coding scheme (e.g., the Gioia method)* **Research design as a plan for your study** Framework or a plan for: - Sampling process - Measurement - Data collection A properly developed research design helps minimise bias and give confidence in the results. [Criteria: reliability & validity] - Reliability: consistent measures (same results, same questions main general conditions remain the same get the same results). Use established scales helps - Validity: whether the results represent what they represent to measure **Units of Analysis** Who or what is being studies (most often individuals & collections of individuals) - Individuals (customers, students, donors, voters) - Groups (households, couples, friendship groups) - Organizations (firms, NGOs) - Social interactions (likes, photo comments, FB shares) - Purchase behaviour - Innovation performance (patent applications, new product introductions) - Venture growth (in terms of human, financial, and innovative resources) **Design plan of a research project --** *Checklist* 1. Define the purpose of your project -- exploratory (how questions), descriptive (what questions), or explanatory (explain relationships between variables, quantitative)? 2. Specify the meanings of each concept you want to study -- conceptualization. 3. Select a research method. 4. Determine how you will measure the results -- operationalization. 5. Determine whom or what to study -- population and sampling. - Population - Sampling 6. Collect empirical data -- observations. 7. Process the data. 8. Analyse the data. 9. Report your findings -- application. **Different purposes of scientific research** - Exploratory research - New areas - Generates initial ideas about a phenomenon - Test feasibility of further research - Descriptive research - Observations and detailed documentation - What? Where? When? - Explanatory research - Explanations of observed phenomena, problems, or behaviors - Why? How? - Causal factors and outcomes **Research Onion** Will be elaborated below **Research Philosophies** Positivism vs Interpretivism - **Positivism:** assumes that reality exists independently of the thing being studied. - In practice this means that the meaning of phenomena is consistent between subjects (Newman, 1998). - **Interpretivism** suggests that the inherent meaning of social phenomena is created by each observer or group (Astlundet al. ,2011). - In this philosophy, one can never presume that what is observed is interpreted in the same way between participants. - The key approach is to examine differences and nuances in the respondents' understanding. ![](media/image22.png)**Induction and Deduction approach** [Induction] - Theory building - Infer theoretical concepts and patterns from what we see **Experimental research** (experiment & survey) in a nutshell - Random assignment to experimental and control groups - Pre-testing in both groups - Treatment (condition): IV manipulated; all other variables held constant - Post-testing in both groups - Compute and analyse **group differences** - *Manipulation: Manipulating the independent variable allows you to determine whether it has an influence on the dependent variable.* - **Within-subjects design:** The same participant tests all conditions corresponding to a variable - **Between-subjects design:** Different participants are assigned to different conditions corresponding to a variable **Experiments** *Strengths* - Primary tool to study causal relationships - Isolation of experimental variable's impact over time - Replication - Scientific rigor *Weaknesses* - Artificiality of laboratory settings - Ethical considerations - Typically involves deceiving subjects - Intrusive nature, risk of inadvertently causing damages to subjects **Survey research** - Deductive approach - Units of analysis = respondents - Respondents provide data for analysis by responding to a survey - Self-reported information - Questionnaires are commonly used in surveys - Online surveys, telephone surveys (CATI) Cross-sectional survey: *Collecting large amounts of data in an economical way* - Large samples, original data, measuring attitudes and orientations - Quantitative data (closed-ended questions, sometimes combined with open-ended questions (qualitative)) - Data collection on **multiple cases at a single point** in time - Quantitative or quantifiable data on **multiple variables** - Statistical analysis to detect patterns and associations **Guidelines for asking questions** - Select appropriate question forms (order of measurement). - Make items clear. - Avoid double-barrelled questions. - Respondents must be competent to answer. - Respondents must be willing to answer. - Questions should be relevant. - Short items are best. - Avoid biased items and terms. **Rules for online surveys** - DO ask for permission. - DO offer to share select result with respondents. - DO use consistent wording. - DO use simple language. - DON'T force excessive scrolling. - DO plan time and day of initial mailing. - DO be aware of technical limitations. - DO test incentives, rewards, and prizes. - DO limit studies to less than 15 minutes. **Survey** *Strengths* - Useful in describing large populations - Standardized questions - Easily distributed online *Weaknesses* - Weak approximation of complex issues due to item standardization - Seldom deal with context of social life - Artificial - Self-reported - Weak on validity **Research choices** - *Mono method*: Single data collection technique and analysis - *Multi-method*: Multiple data collection techniques - Qualitative or quantitative - Primary + secondary data - *Mixed-methods*: - Multiple data sources - Qualitative and quantitative analysis **\ ** **Time horizon/dimension** *Cross-sectional vs Longitudinal design* - ![](media/image24.png)Cross-Sectional Study: Observations represent a single point in time, a cross section of a population. - Leverage the differences between individuals - Longitudinal Study: Involves collection of data at different points in time. - Explore the differences over time - *Trend Study* -- a characteristic of some population is monitored over time. - *Cohort Study* -- a specific subpopulation, or cohort, is studied over time. - *Panel Study* -- data are collected from the same set of people at several points in time. - *Panel mortality* -- The failure of some panel subjects to continue participating in the study. Triangulation: Triangulation in scientific research refers to the use of multiple methods, data sources, theories, or investigators to cross-verify and validate research findings. It enhances the credibility, validity, and reliability of the results by providing a more comprehensive understanding of the research problem. **END OF RESEARCH ONION** **Which research design is the best?** *Well, it depends* - What is the research phenomenon? - What are the research questions? - A research design is rarely perfect -- understand the limitations of the data collection and methods. - Mixed-method and multi-method studies help overcome the limitations of single-method designs **Exercise** - Research Question A (RQA): What is the effect of providing advanced services on the financial performance of servitizing firms? - A quantitative approach is most suitable for RQA - Research Question B (RQB): How do customers participate in the co-creation of services? - A qualitative approach is most suitable for RQB. (in qualitative research you don't have a dependent variable, because you don't have a model) []{#_Toc168912621.anchor}**Scholarly journal article review:** *One example of quantitative research* Experience the process of deductive quantitative research: - Positioning of research - Conceptualize main constructs - Formulate hypotheses - Quantitative research design - Concluding quantitative research REVIEW QUESTIONS ABOUT TODAY'S SESSION - What is the difference between methodology and methods? - What are different quantitative and qualitative research methods that you can choose when planning research projects? - What are the layers of the so-called research onion and their respective contents? - What are examples for the structure, methods, analyses, and results of a common quantitative research paper? Paper summary ============= Article 1 Lean startup: operationalizing... -------------------------------------------- ### Introduction 1\) Introducing the concept & creating urgency - What is lean start-up (1st paragraph) - Describing popularity of LS in education and practice 2\) Identifying the research gap - Acknowledging previous literature (rooting LS in different research streams) - Lack of quantitative research 3\) Formulating aim / RQ - Conceptualize LS as a capability - RQ1: Develop LSC operationalization (i.e., measurement instrument) - RQ2: Investigate performance implications of LS ### Theory 1\) What is lean start-up (not) - Method for opportunity exploration (Bakker & Shepherd, 2017) - Dimensions of lean start-up (experimentation, theorizing, learning, iterative) - Is not design thinking, agile development, or scaling Lean Startup (LS) is a toolset for opportunity exploration (Bakker & Shepherd, 2017) that emphasizes iterative experimentation and early customer insight. LS is a toolset for experimental entrepreneurship, which is defined as approaches to the entrepreneurial process that emphasize **experimentation**. LS is more than experimentation. For example, it also contains activities such as generating early customer insights, learning, and iteration. LS is an iterative process of theorizing, experimentation, and learning. LS is not design thinking, agile development, or scaling. 2\) Hypothesizing LSC performance implications - Hypothesis: The higher the degree of LSC, the higher the performance. - Provides multiple arguments why we expect this effect (grounded in literature) - Contingencies (moderators/moderating variables ) - Uncertainty (market + technological) - Degree of innovativeness - Type of business (B2B or B2C) ### Methods 1. Research design (operationalization) - Mixed methods (interviews + survey) - Sampling: Software ventures in Berlin - Analysis: open coding + EFA / CFA 2. Testing LSC and performance relationship - Dependent variable: project performance - Independent variables (+ measurement instrument) - Control variables (age, venture stage) - Analysis: moderated regression analysis ### Results: LSC -\> performance Afbeelding met tekst, nummer, schermopname, Lettertype Automatisch gegenereerde beschrijving *The main hypothesis is confirmed (3 stars). Market uncertainty and technology uncertainty do NOT influence the hypothesis* ### Discussion: What is lean startup? We go beyond previous qualitative approaches that used the single facet of "experimentation" and show that LSC is a bundle of several capabilities." 1. Customer insight\ Capability to understand customers deeply 2. Hypothesis testing\ Capability to formulate hypotheses about the venture and its environment 3. Iterative experimentation\ Capability to run several experiments on BM elements 4. Validation\ Capability to use data to monitor the impact of decisions 5. Learning\ Capability to use new information to update beliefs and actions ### Discussion: how does LSC affect performance? 1. Entrepreneurs benefit from lean startup capabilities 2. Market uncertainty, technological uncertainty, and the degree of innovativeness do not influence the LSC-performance relation- ship, so benefit does not depend on these contingencies. 3. Lean start-up is more effective in the context of customer heterogeneity (mixed B2B & B2C variable) ### Limitations: - First, since the study focused on software startups, the results might not apply to other fields. It\'s important to see if the findings are relevant for other types of businesses. Researchers should also check if the results hold true in different locations. - Long-term studies are needed to show if LS truly causes better performance, not just that there is a connection. Researchers should also test how the iterative learning process in LS works in practice. Cross-sectional -- causality (longitudinal research) - it would be useful to study how LS works with other development methods like design thinking and agile development, so integrate design thinking & agile development in research. Article 2 An agile co-creation process -------------------------------------- ### Introduction - "We cannot come to the customer with a total solution; rather, we must work together in an agile way to progressively address the customer's needs as they evolve." - Digital servitization, important to adopt and invest in, but challenging to implement - Leads to a digitalization paradox: - Increasing revenues from digital services fail to deliver greater profits because of spiraling cost increases - To address this paradox: - Co-creation as suggested per other studies: Collaborative activities by parties involved in direct interactions that aim to contribute to value for one or both parties - Gap/research question: - Little is known about how firms actually use agility and how value co-creation can be better organized and managed in digital servitization → How can firms co-create digital service innovations with their customers to cope with the digitalization paradox and reap the benefits of digital servitization? - Two contributions to the digital servitization literature 1. Understand how to organize agile and cost-effective processes for value co-creation in digital servitization 2. Understand how to manage co-creation of value with the customer by involving numerous cross-functional actors - HOW do they contribute? - This study contributes to the growing body of literature on value co-creation and digital servitization of manufacturing industries by providing an in-depth account of how value co-creation processes in digital servitization unfold between providers and customers. ### Theoretical background 1. **Digital servitization and the digitalization paradox** Digital servitization: the transformation in processes, capabilities, and offerings within industrial firms and their associate ecosystems to progressively create, deliver, and capture increased service value arising from a broad range of enabling digital technologies. Digitalization allows companies to move from product-focused models to service-oriented offerings, creating more value for customers and differentiating from competitors. It changes how companies interact with customers, leading to co-created value and new revenue streams by integrating products, services, connectivity, and data analytics. However, digital servitization also presents challenges. The rapid pace of digital innovation can overwhelm traditional companies, requiring new approaches to managing customer relationships and co-creating services. This can lead to a digitalization paradox where increased revenue from digital services does not necessarily result in higher profits due to rising costs. Advanced digital solutions require significant investment and maintenance, driving up service costs. 2. **Co-creation in digital servitization relationships** In digital servitization, value is created together by the customer and the provider. The provider facilitates value, while the customer actively participates in creating it. This collaborative process is essential for innovating and offering successful digital services. Companies need to work with customers and other partners to co-create digital service innovations. - Co-creation changes the interaction between providers and customers from simple transactions to relationship-based collaborations. Embracing co-creation means understanding how customers use and combine resources, shifting the provider\'s role from merely facilitating to actively creating value. However, this can lead to complexities and potential issues, such as unclear roles and value destruction. 3. **Agile co-creation processes in digital servitization** Traditional methods for developing new products and services are becoming outdated due to rapid changes in technology and markets. Those methods are too rigid for today\'s dynamic and innovative projects in digital servitization. Agility: The ability to accommodate and adapt to changes in a dynamic environment. Inspired by the software industry, agile project management offers the flexibility needed to avoid the pitfalls of rapid digitalization. Key principles: 1. Risk minimization through short iterations of defined deliverables 2. Direct co-creative communication with partners in the development process. This approach helps companies respond quickly to customer feedback and changes. Value co-creation with customers is essential in agile innovation but often overlooked. Digital technologies blur traditional boundaries, requiring organizations to collaborate closely with customers and other companies. This involves multiple roles and functions working together to adapt to new digital opportunities and stimulate joint learning. ### Methods - Research approach - Data collection - Interview guide - Data triangulation - Data analysis ![](media/image26.png)**Data analysis model** ### ### Findings **Identifying the Causes of the Digitalization Paradox** The digitalization paradox is the dilemma where firms can\'t recover their investments in developing digital service innovations despite higher revenues. This paradox is caused by two main factors: 1. *Overestimating Revenue Streams:* 2. *Unexpected Increase in Delivery Costs:* **Agile Co-Creation Process Phases for Developing Digital Services** The process involves five phases and relies on an iterative, agile approach with short cycles of planning and execution, involving feedback from both customers and operations. 1. *Identifying Digitalization Needs* 2. *Ensuring Digitalization Value Prioritization* 3. *Micro-Service Development* 4. *Implementing Micro-Services* 5. *Evaluating Micro-Service Benefits* This structured yet flexible approach allows firms to address the digitalization paradox by ensuring that investments in digital services lead to sustainable and scalable value creation. **Agile co-creation process for digital servitization: A micro- service innovation approach** The model created for agile co-creation in digital servitization enables providers and customers to focus on one need at a time. Key principles: 1. Incremental micro-service investments 2. Sprint-based micro-service development 3. Micro-service learning by doing Afbeelding met tekst, schermopname, Lettertype, logo Automatisch gegenereerde beschrijving ### Contributions - Theoretical contributions 1. Developed an empirically grounded agile co-creation process model for digital service innovation in the context of digital servitization 2. Identified key roles/activities across multiple organizational levels of both providers and customers to co-create value in digital servitization 3. Contributes by advancing our understanding of the causes of the digitalization paradox. - Managerial implications 1. Encouraging companies to develop digital services in small, quick cycles, following a micro-service innovation approach. 2. Increase multirole participation in digital servitization and encouraging involving various departments in the co-creation process. 3. Suggests that companies should build new skills gradually and develop digital servitization capabilities progressively. Article 3 Business models ------------------------- ### Business Models Business model definition "A business model is a simplified and aggregated representation of the relevant activities of a company. It describes how marketable information, products and/or services are generated by means of a company\'s value-added component. In addition to the architecture of value creation, strategic as well as customer and market components are taken into consideration, in order to achieve the superordinate goal of generating, or securing the competitive advantage. To fulfill this latter purpose, a current business model should always be critically regarded from a dynamic perspective, thus within the consciousness that there may be the need for business model innovation, due to internal or external changes over time." Can generate competitive advantage for existing and new businesses **Business model components** - Strategic components - Strategy model - Resources model - Network model - Customer & market components - Customer model - Market offer model - Revenue model - Value creation components - Manufacturing model - Procurement model - Financial model ![](media/image28.png) **Examples of business model components** - Razor-razor blade model A dependent good is sold at a low price and a paired consumable good generates the profits. - Rolls-Royce jet engines and maintenance Question: Which of the previously mentioned business model components is the most relevant in the two examples above?\ → Answer: Revenue model Business model canvas - Design or architecture of a firm's value creation, delivery, and capture The activity system design framework **Framework provides insight by** - Giving business model design a language, concepts, and tools - Highlighting business model design as a key managerial/entrepreneurial task - Emphasizing system-level design over patrial optimization **Design elements** - Content: what activities should be performed? - Structure: how should they be linked and sequenced? - Who should perform them, and where? **Design themes** - Novelty: adopt innovative content, structure, or governance - Build in elements to retain business model stakeholders, e.g. customers - Complementarities: bundle activities to generate more value - Efficiency: reorganize activities to reduce transaction costs Article 4 Harnessing the potential of AI ---------------------------------------- ### Introduction **A significant question or claim** It is increasingly evident that the use of AI in government initiatives has become a necessity due to the rapid advancement of technology and the availability of exponential enhancement of data However, as with other technologies, studies document that AI adoption by both the Indian government and the private sector faces significant barriers before any public value can be realized [Research Questions]: - RQ1. How can the use of AI-enabled services by different government departments foster the satisfaction of citizens? - RQ2. Can the depth assimilation and breadth assimilation of AI-enabled government services impact the operational and strategic public services being delivered to citizens? - RQ3. Is there any moderating impact of risk factors that may influence the quality of AI-enabled services and public values? **A position in the academic debate** Extant literature discusses the applications of AI and associated technological aspects. Few studies, however, examine the impact and challenges of AI applications faced by government sectors. **An explanation of the research method or approach (identify key constructs)** To validate the model, a survey questionnaire was developed and administered to government agencies in India that have adopted different services which use AI technology. All items were measured on a 5-point Likert scale ranging from 1 = Strongly Disagree (SD) to 5 = Strongly Agree (SA). This study used the Partial Least Square (PLS)-Structural Equation Modeling (SEM) technique to test the hypotheses. The PLS-SEM technique involves quantification of the responses received in the survey. We contacted the Ministry of Electronics and Information Technology, the National Informatics Center (NIC) and the Unique Identification Authority of India (UIDAI), Government of India, amongst other government agencies. Hence, we considered 315 usable replies with 21 questions for the analysis. ### Literature review - *Importance of Data Analytics:* Governments use data analysis to make informed decisions and improve public satisfaction. AI technology enhances data analysis capabilities, enabling cost-effective and efficient decision-making across various government sectors. - *Barriers to AI Adoption***:** Despite its potential benefits, government agencies face challenges in deploying and developing AI technology, hindering its full-scale assimilation into all sectors. The assimilation process involves stages such as evolution, adoption, and deployment. - *Assimilation Gap***:** An assimilation gap exists between AI implementation and its full utilization, which needs to be addressed for governments to capitalize on the benefits of AI technology. - *Depth and Breadth of AI Assimilation***:** The depth of AI assimilation refers to the vertical impact of AI technology usage in government initiatives, while the breadth refers to the opportunities for government agencies to use AI technology. - *Public Value of AI***:** Effective use of AI enhances public value by improving operational and strategic public services for citizens. However, AI applications also pose security and privacy risks, which governments must address. ### Theoretical Background, conceptual model & hypotheses **Theoretical Background:** - *IT Assimilation Theory:* Recognizes the impact of IT-related performance on organizations. It emphasizes the simultaneous use of breadth and depth assimilation of suitable technology for better operational and strategic performance. - *Public Value Theory***:** Focuses on enhancing effective public services to maximize citizen satisfaction and minimize expenditure, providing inputs to government agencies on policy implementation. **Proposed Conceptual Model:** - A combination of IT assimilation and public value theories explains how effective use of AI technology by government agencies influences public service quality and citizen satisfaction. - The model integrates constructs from both theories: assimilation depth and breadth of AI-enabled services, operational and strategic public services for citizens, and citizen satisfaction. **Hypotheses** 1. Assimilation depth of AI-enabled services (ADES) positively affects operational public service for citizens (OPSC).. 2. Assimilation breadth of AI-enabled services (ABES) positively affects operational public service for citizens (OPSC). 3. Assimilation depth of AI-enabled services (ADES) positively and significantly impacts strategic public service for citizens (SPSC). 4. Assimilation breadth of AI-enabled services (ABES) positively and significantly impacts strategic public service for citizens (SPSC). 5. Operational public service for citizens (OPSC) positively influences strategic public service for citizens (SPSC) 6. Provision of strategic public service for citizens (SPSC) significantly and positively impacts citizen satisfaction (CS). 7. a\. Risk factors act as a moderator to impact the relationship between the assimilation depth of AI-enabled services (ADES) and operational public service for citizens (OPSC). Mediator = indirect ### Methodology - Survey questionnaire development: survey based on the conceptual model, included 21 questions covering depth and breadth of AI service assimilation, operational and strategic public service for citizens, and citizen satisfaction. 5-point Likert Scale. - Pre-Pilot Phase: A pre-pilot phase involved administering the questionnaire to 15 government agencies for review and refinement. - Main survey: survey was conducted among government agencies in India. ### Results **Measurement Model and Discriminant Validity Test** - Content validity was assessed using loading factors. - Internal consistency, reliability, convergent validity, and multicollinearity were verified using Cronbach's alpha, Composite Reliability, Average Variance Extracted, and Variance Inflation Factor, respectively. - Discriminant validity was confirmed through Fornell and Larcker criteria and Heterotrait--Monotrait correlation ratio test. **Common Method Variance (CMV):** - Harman one-factor test was conducted to check for CMV, showing no significant bias in the collected data. **Moderator Analysis (Multi Group Analysis):** - Risk factors, including data privacy, security-related risks, and others, were analyzed as moderators impacting the relationship between assimilation and public value. - Multi-Group Analysis (MGA) was conducted to assess the effects of risk factors on the relationships. **Structural Model:** - Structural model analysis tested hypotheses using bootstrapping procedure. - Path coefficients were computed to evaluate the significance of relationships between constructs. - ADES and ABES significantly and positively affected OPSC and SPSC. - OPSC had a significant positive effect on SPSC. - SPSC significantly influenced citizen satisfaction (CS). - The effects of the moderator (risk factor) on the relationships (H1, H2, H3, H4) were significant. - ADES and ABES explained 42.8% of the variation in OPSC and 56.2% of the variation in SPSC. - SPSC accounted for 72.7% of the predictive power of the model. Overall, the results support the hypotheses and indicate the significant impact of AI assimilation on public service quality and citizen satisfaction, moderated by various risk factors ### Discussion - The analysis shows that the assimilation of AI-enabled services in government agencies impacts both operational and strategic public services for citizens, consistent with previous studies and supported by IT assimilation and public value theories. - Operational public service for citizens (OPSC) significantly influences strategic public service for citizens (SPSC), leading to higher citizen satisfaction. - The study emphasizes the importance of utilizing AI potential in government agencies while addressing citizens\' security and privacy concerns. - Moderating effects of risk factors on the relationships between AI assimilation and public value were explored through graphical representation, confirming significant impacts. **Theoretical Contributions:** - The study contributes to understanding how AI-enabled government services enhance citizen satisfaction, bridging the gap in literature regarding IT assimilation post-adoption stage in government organizations. - It develops an integrated theoretical model combining IT assimilation and public value theories, considering risk factors as a moderator. **Practical Implications:** - Breadth and depth assimilation of AI technology in government services affect operational and strategic public service for citizens, emphasizing the importance of starting with simpler tasks before transitioning to more complex ones. - Recommendations are provided for government agencies to systematically implement breadth assimilation followed by depth assimilation to improve operational and strategic performance for citizens. **Limitations and Future Research Directions:** - The study\'s limitations include its focus on India and the use of cross-sectional data, suggesting future research to explore longitudinal dynamics and consider data from multiple countries for a broader perspective. ### Conclusion - While AI technology has been successfully implemented in the private sector, its adoption in governmental agencies lags behind. - This study explores how AI technology, through breadth and depth assimilation, influences operational and strategic public services, ultimately impacting citizen satisfaction positively. - The study considers risk factors such as flawed algorithms and system malfunctioning, which pose privacy and security concerns. - The statistically validated model demonstrates high predictive power (72.7%) and serves as a baseline for governments to harness AI technology for creating public value. - Key findings include the impact of both depth and breadth assimilation of AI technology on operational and strategic performance of government services. - The study emphasizes the importance of using appropriate AI algorithms in government services to mitigate risks and enhance citizen satisfaction. Article 5 Impact of Online vs Offline acculturation --------------------------------------------------- ### Introduction Consumer acculturation, the process of learning a new culture, has been studied primarily offline among ethnic minority consumers. - Offline Acculturation Drivers: Studies show that language use and social interactions influence acculturation outcomes. - Role of Acculturation Agents: Individuals or institutions influencing consumer behavior play a crucial role in acculturation outcomes. - Shift to Social Media: With the rise in social media use among ethnic minorities, scholars argue that online platforms promote assimilation and integration by facilitating connections beyond ethnic communities. - Language Choice on Social Media: Ethnic minorities face choices regarding language use on social media, which can impact their integration into the broader culture. - Research Gap: While digital technologies shape acculturation experiences, research rarely considers how offline and online interactions jointly influence consumer behavior. - Role of Education: Education level may influence how ethnic minorities acculturate offline and how they make consumption choices. - Research Questions: The study aims to investigate how online acculturation affects offline acculturation and subsequent purchase intentions among ethnic minority consumers. It also explores the moderating role of education level. ### Literature review Enculturation and Acculturation: Enculturation is learning one\'s heritage culture, while acculturation is adopting a new culture through interaction with culturally different people. Social Media: Social media refers to internet-based applications allowing the creation and exchange of user-generated content, facilitating communication and relationship-building. - *Role of Social Media in Ethnic Consumer Behavior*: Social media enables ethnic minorities to connect and form relationships with in-group and out-group members, potentially influencing their acculturation orientations and consumption choices. **Acculturation-Friendship**: Social interactions, particularly friendships, influence acculturation preferences and consumption behavior. Social media facilitates maintaining existing relationships and building new ones, impacting cultural processes. - *Role of Social Interactions***:** Interaction with host culture individuals influences acculturation preferences and behaviors. Social media provides opportunities for engagement and connection, shaping cultural processes. - *Adaptation to Host Culture***:** Individuals valuing acculturation prioritize social interactions with the host culture, leading to behavioral changes aligned with the host culture\'s norms and values. - *Motivational Factors in Social Media Use***:** Social support is a primary motivation for using social networking sites, with culturally distinct user groups showing differences in motivational factors. **Acculturation-Language Use**: Ethnic minority consumers, often bilingual, choose to communicate in their native or host country language on social media. Language use influences cultural identity and acculturation processes, reflecting preferences for home or host culture. - *Bilingualism among Ethnic Minority Consumers***:** Ethnic minorities often communicate in both their native and host country languages on social media, reflecting their cultural identities. - *Impact of Language Use***:** Language preferences on social media influence cultural identities and acculturation processes, reflecting engagement with home or host culture. ### Research model & hypotheses development **Conceptual Framework**: - *Based on Literature*: The framework integrates insights from studies on consumer behavior, digital acculturation, social media use, friendship orientations, language use, and the role of education. - *Influence of Acculturation and Enculturation*: These cultural orientations influence purchase intentions and are affected by language preferences and social media interactions. - *Role of Education*: Education potentially moderates the impact of language use and friendship on acculturation, enculturation, and purchase intentions. **Research Model**: - Online Acculturation: The study examines how friendship orientation and language use online affect acculturation/enculturation preferences and purchase intentions. - Moderating Role of Education: Education level is proposed to moderate these relationships, potentially amplifying the effects of language use and friendships on cultural orientations and purchase intentions. **Hypotheses Development**: 1. Friendship Orientation: - Negatively impacts enculturation (H1a). - Positively impacts acculturation (H1b). 2. Language Use: - Negatively impacts enculturation (H2a). - Positively impacts acculturation (H2b). 3. Acculturation/Enculturation and Purchase Intentions: - Both enculturation and acculturation positively impact purchase intentions (H3a, H3b). **Education as a Moderator**: - Education moderates the relationships between friendship orientation/language use and enculturation/acculturation, and between these cultural orientations and purchase intentions (H4). **Key Points**: - Social media and language use play critical roles in shaping cultural orientations and consumer behaviors. - Higher education levels are associated with greater acculturation and influence the impact of social media interactions on consumer behavior. ### Methodology - Sample and data collection: located in the Netherlands, focused on Turkish-Dutch community ### Data analysis and findings **Techniques Used**: - Confirmatory Factor Analysis (CFA) - Multi-Group Analysis - Structural Equation Modelling (SEM) **Single-Group Confirmatory Factor Analysis**: - The measurement model fit well (χ²/df = 2.283, AGFI = 0.861, CFI = 0.944, IFI = 0.944, RMSEA = 0.029). **Single-Group Structural Equation Analysis**: - Confirmed the proposed factor structure fits well (χ²/df = 2.450, CFI = 0.936, AGFI = 0.854, RMSEA = 0.05). **Multi-Group Analysis**: - Groups based on education: secondary (Group 1), higher education (Group 2), and doctorate/PhD (Group 3). - Validated the measurement model for each group. - Established convergent validity and acceptable measurement model validity. **Multi-Group Structural Equation Analysis**: - Tested model fit across groups, showing configural and metric invariance. - Fit statistics were satisfactory for all groups, supporting the factor structure\'s consistency. **Hypotheses Testing**: - H1a: Friendship orientation negatively affects enculturation. - H1b: Friendship orientation positively affects acculturation. - H2a: Language use negatively affects enculturation. - H2b: Language use positively affects acculturation. - H3a: Enculturation positively affects purchase intentions. - H3b: Acculturation positively affects purchase intentions. **Moderating Role of Education Level**: - Friendship Orientation and Enculturation: Not significant across education groups. - Friendship Orientation and Acculturation: Significant, strongest in Group 3. - Language Use and Enculturation: Negative, strongest in Groups 1 and 2. - Language Use and Acculturation: Stronger in Group 1, non-significant in Groups 2 and 3. - Enculturation and Purchase Intentions: Significant, strongest in Group 2. - Acculturation and Purchase Intentions: Significant, strongest in Group 1. **Conclusion**: Education level moderates the relationships between friendship orientation, language use, enculturation, acculturation, and purchase intentions. ### Discussion **General Findings** - *Online Acculturation*: This study explores how language use and friendship orientation on social media affect acculturation, which in turn influences consumer behavior and purchase intentions. - *Influence of Online Networks*: Friends on social media significantly influence consumers, encouraging bonding and sharing, which leads to acculturation through these online interactions. - *Impact on Purchase Intentions*: Both enculturation (maintaining heritage culture) and acculturation (adapting to the mainstream culture) positively impact purchase intentions. **Single Group Findings** - *Language and Friendship*: Using mainstream language and having friends from the mainstream culture boost acculturation but reduce enculturation. Ethnic minority consumers heavily rely on their online social networks for cultural orientation. - *Cultural Impact on Purchases*: Cultural orientations significantly influence purchase decisions, with both acculturation and enculturation affecting intentions to buy. **Moderating Role of Education** - *Education as a Moderator*: Education level influences how friendship orientation and language use affect acculturation and enculturation. For instance, highly educated individuals are more influenced by mainstream culture online and more likely to acculturate offline. - *Unexpected Findings*: In some cases, the expected relationships between cultural orientation and acculturation/enculturation were not significant, especially among highly educated individuals, suggesting a need for further research. **Theoretical Contributions** - *Link Between Online and Offline Acculturation*: This study highlights how online behaviors (language use, friendship orientation) influence offline acculturation and consumer decisions. - *Education-Specific Analysis*: The moderating role of education provides deeper insights into the acculturation process and consumer behavior, suggesting that education level significantly impacts these dynamics. **Managerial Implications** - *Targeting Ethnic Minorities*: Marketers should use social media to engage ethnic minorities by focusing on language and friendship orientation to influence both online and offline acculturation. - *Language Preferences*: Public and private firms should consider consumers\' language preferences in social media interactions to better target their marketing strategies. **Limitations and Future Research** - *Sample Size and Tie Strength*: The study had a small sample size for less educated groups and did not consider the strength of social ties. Future research should address these limitations. - *Further Exploration Needed*: Future studies should investigate how web advertising and social media use influence consumption patterns and brand relationships among ethnic minority consumers. ### Conclusion This study shows how education levels affect the influence of friendship and language on cultural adaptation among ethnic minorities: *Low Education*: - Friendship helps maintain their own culture (enculturation) but doesn\'t help adapt to the host culture (acculturation). - Language use is important for both maintaining their own culture and adapting to the host culture. *Higher Education*: - Friendship helps them adapt to the host culture but doesn\'t affect maintaining their own culture. - Language use only helps them maintain their own culture, not adapt to the host culture. Overall, the study highlights the importance of education in understanding how ethnic minorities interact with and adapt to the host culture through social media, influencing their purchase decisions. Article 6 How AI encourages consumers to share secrets ------------------------------------------------------ ### Introduction AI is becoming a major player in our digital society, influencing industries like retail, e-commerce, and marketing. One common AI application in marketing is conversational agents that interact with people like humans. However, AI raises privacy concerns as it collects and analyzes vast amounts of personal data, which worries consumers and regulators. Privacy is crucial because data is essential for AI\'s success, but businesses risk losing consumer trust if they can\'t protect this data. While consumers benefit from AI\'s personalized services, they also worry about privacy. This study explores how AI affects consumers\' willingness to share personal information, balancing personalization benefits against privacy risks. Using theories like the personalization-privacy paradox (PPP) and privacy calculus theory (PCT), the study proposes a model to understand how AI\'s human-like traits, personalization, and privacy concerns impact consumer attitudes and data sharing. **Research objective**\ This paper aims to explore the overall research question "How can artificial\ intelligence (AI) influence consumer information disclosure?". It considers how anthropomorphism of AI, personalization and privacy concerns influence consumers' attitudes and encourage disclosure of their private information. **Unit of analysis**\ This paper develops a conceptual model based on and presents seven research\ propositions (RPs) for future research. Kronemann et al 2022 ### Literature review Research on how consumers interact with AI, especially in marketing and services, is becoming more important. Companies use AI chatbots for customer service, which changes how they interact with customers. However, studies on AI\'s impact are still limited. **Key themes from existing research include:** - *Adoption and Use of AI* - *Anthropomorphism of AI* - *Benefits of AI* - *Privacy and Security Concerns* **Research gap** Most studies focus on how consumers use and adopt AI, but there\'s a lack of critical examination of data privacy and the need for personal information for AI to work effectively. Addressing these privacy concerns is crucial for the successful integration of AI. ### Theoretical background and research propositions **Theoretical Background: PPP and PCT** 1. *Personalisation-Privacy Paradox and Privacy Calculus Theory* In Western societies, privacy and data security concerns are rising as consumers become more aware of their data being watched and potentially misused. Consumers struggle with the trade-offs between the benefits of new technology and the risks to their privacy, known as the Personalization-Privacy Paradox (PPP). This paradox highlights the balance consumers must find between the advantages of personalization and the exploitation of their private information by marketers. The technology\'s opaque nature often makes this evaluation difficult. The Privacy Calculus Theory (PCT) further explains that consumers weigh the risks to their privacy against the benefits they receive, such as improved experiences and discounts. PCT assumes consumers are rational and seek control over their private information. For this study, PCT will be used to understand how consumers evaluate personalized AI interactions versus privacy risks, and how this affects their attitudes and willingness to share information. 2. *Proposed Conceptual Model and Research Propositions* A conceptual research model is proposed, integrating PCT and anthropomorphism, to examine how consumers interact with AI-based digital assistants and disclose information amidst privacy concerns. The model includes seven RPs, discussed below. **Anthropomorphism of Artificial Intelligence** Anthropomorphism, giving AI human-like traits, can influence consumer behavior. Companies use anthropomorphism to make AI more relatable and engaging. However, its effects are debated: it can either enhance engagement or cause discomfort (uncanny valley effect). This study assumes that anthropomorphism can encourage consumers to share more information by triggering social responses. Thus, it proposes: - *RP1:* Anthropomorphism of AI positively affects consumers\' willingness to disclose information. - *RP2:* Anthropomorphism of AI positively affects consumers\' attitudes towards the digital assistant. **Privacy Concerns** Privacy concerns arise when individuals feel they lack control over their personal data. High privacy concerns negatively impact consumers\' willingness to share information and their attitudes towards digital assistants. Studies show that privacy concerns deter online participation and data sharing. Therefore, it is proposed: - *RP3:* Privacy concerns negatively affect consumers\' intentions to disclose information. - *RP4*: Privacy concerns negatively affect consumers\' attitudes towards the digital assistant. **Personalisation** AI agents like Siri offer personalized experiences, meeting consumer demands for relevant content. Personalization improves user experience and can lead to positive consumer reactions. However, excessive personalization can raise privacy concerns. This study hypothesizes: - *RP5:* Personalization positively affects consumers\' intentions to disclose information. - *RP6:* Personalization positively affects consumers\' attitudes towards the digital assistant. **Attitude towards AI-Based Digital Agents** According to the Theory of Planned Behavior, attitudes influence behavioral intentions. This study suggests that consumers\' attitudes towards AI-based digital assistants mediate the impact of privacy concerns, personalization, and anthropomorphism on their willingness to disclose information. Therefore: - ![](media/image30.png)*RP7:* Attitude towards the digital assistant mediates the effect of privacy concerns, personalization, and anthropomorphism on consumers\' intention to disclose information. - How many Independent Variable: 4 (3 on the left and 1 in the middle) - Dependent variable: 2 (middle and outcome) - Moderators: 0 - Mediators: attitude towards the conversational agent it's mediating anthropomorphism, privacy concerns, and personalisation. In order to claim/test mediator. Antro intention. First their needs to be a direct relationship; otherwise you cannot mediate ### Discussion The literature review highlights how consumers use AI technology in various aspects of daily life, with its success relying on ample consumer data for personalized benefits. The Personalization-Privacy Paradox (PPP) and Privacy Calculus Theory (PCT) provide a basis to understand how AI influences consumer information sharing. **Anthropomorphism and Personalization:** Anthropomorphizing AI, giving it human-like traits, can improve consumer attitudes and encourage information sharing. Despite debates, studies suggest positive effects of anthropomorphism. Personalization also enhances consumer experience and promotes data sharing. **Privacy Concerns:** Privacy concerns hinder consumer evaluation of digital assistants and their willingness to share information. With AI heavily reliant on personal data, privacy becomes a critical barrier to its success. **Future Research:** The study identifies gaps in understanding the effects of AI and its potential to influence consumer information disclosure, suggesting areas for future research. ### Conclusions and future research **Conclusions**: This study enhances our understanding of how AI influences consumer information sharing, critical for AI advancements. It highlights the balance between personalization benefits and privacy concerns. As AI becomes more integrated into marketing, understanding its impact on consumer behavior is crucial. **Limitations and Future Research**: While this study offers insights, it\'s conceptual and needs empirical testing. Future research should explore the proposed model and areas like the Personalization-Privacy Paradox (PPP) in AI, consumer awareness of data protection laws like GDPR, the potential negative effects of AI on privacy, and the impact of different AI technologies on information disclosure. Article 7 Effective marketing communication ------------------------------------------- ### Introduction The internet has transformed how people connect socially, with platforms like Facebook facilitating extensive online relationships. Companies use these platforms for viral marketing, spreading messages through peer-to-peer sharing. However, research gaps exist regarding how this affects consumer attitudes. This study explores factors influencing advertising effectiveness on social networks from communication theory, advertising literacy, and social psychology perspectives. ### Literature Review and Research Hypotheses: **Theoretical Background:** - *Social Networks*: Online social networks facilitate various forms of communication and social transactions, influencing individuals\' interactions and relationships. - *Tie Strength:* Refers to the strength of interpersonal relationships within social networks, varying from strong (e.g., close friends) to weak (e.g., acquaintances). It influences communication patterns and information sharing. - *Interactivity of Advertising:* Interactive advertising allows reciprocal exchanges between senders and receivers, enhancing communication effectiveness. - *Advertising Literacy:* Refers to consumers\' ability to understand and analyze advertising messages, impacting their attitudes and behaviors towards ads. **Research Hypotheses:** 1. *Interactivity of Advertising:* - Interactive advertising formats lead to higher communication effectiveness compared to non-interactive formats. 2. *Moderating Effect of Tie Strength and Advertising Format:* - Social ties moderate the effect of advertising format on attitude towards ads and message-sharing intention. Interactive messages are more effective when delivered by weak ties. 3. *Moderating Effect of Tie Strength and Advertising Literacy:* - Social ties moderate the effect of advertising literacy on attitude towards ads and message-sharing intention. Consumers with higher advertising literacy have less intention to share ads if the message comes from weak ties. **Overview of the Research Model:** The research model depicts tie strength as a critical moderator between advertising message format, advertising literacy, and advertising effectiveness. ### Method **Participants and Procedure:** - Two experiments were conducted to test hypotheses. - Study 1 involved 100 participants who received either interactive or non-interactive ads. - Study 2 included 246 undergraduate and graduate students assigned to different conditions. - Used a design to test hypotheses involving tie strength, advertising format, and advertising literacy. - Pre-tested product relevance using 60 participants, finding coffee as the most affordable product. - Participants were informed they were interacting with friends on Facebook, then completed questionnaires. **Stimuli**: - Advertising Format: - Non-interactive ads emphasized one-way communication, while interactive ads allowed consumer interaction. - Non-interactive ads featured text and pictures, while interactive ads included a mental test/game with product information. - Tie Strength: - Strong ties were between friends who met face-to-face, while weak ties were with newly met friends. - Strong tie participants received ads from classmates with more interactions due to their teaching assistant status. - Advertising Literacy: - Adapted scales to measure skepticism and resistance towards advertising. **Dependent Variables:** - Attitude toward the ad measured favorability, interest, and impressiveness. - Sharing intention measured likelihood of sharing or posting the ad on Facebook. - Responses were on a 5-point scale from strongly disagree to strongly agree. ### Results **Manipulation Check and Reliability:** - Participants in interactive ad conditions rated interactivity higher than in non-interactive conditions. - Tie-strength manipulation confirmed successful manipulation. - Dependent variables showed appropriate reliability. **Hypotheses Test:** *Study 1:* - Interactive ads resulted in higher attitude toward the ad and intention to share compared to non-interactive ads, supporting H1a and H1b. *Study 2:* - Tie strength moderated the effect of ad type on attitude toward the ad. - Interactive ads from weak ties led to higher attitude than from strong ties, supporting H2a. - Advertising literacy also moderated the effect on attitude toward the ad. - For low literacy participants, weak ties resulted in equivalent sharing intention as strong ties, but for high literacy, weak ties led to lower sharing intention, supporting H3a and H3b. - No significant interaction for message-sharing intention. - fsQCA analysis showed that advertising format highly associates with message-sharing intentions, revealing individual case conditions that influence outcomes. ### Conclusions, Implications, and Limitations: **Discussion of the Results:** - Interactive advertising is more effective in engaging consumers. - Tie strength influences consumers\' attitude toward ads; strong ties lead to steadier attitudes. - Advertising literacy affects effectiveness; high literacy decreases attitude and sharing intention, especially with weak ties. **Managerial Implications:** - Marketers should prioritize interactive advertising to combat ad fatigue. - Consideration of tie strength is crucial for effective advertising on social networks. - Companies should tailor advertising strategies based on consumers\' advertising literacy levels. **Limitations and Future Research:** - The study\'s applicability to real-life scenarios and users\' comments on advertising could be explored. - Experiments conducted in a lab may limit external validity; future research could aim for higher ecological validity. - Other consumer traits like source credibility and expertise could be investigated for their impact on advertising effectiveness.

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