IB Theory in IS - Scientific Research Methods - 2024-2025

Theory in IS Scientific Research Methods for Business Informatics 2024-2025 Joris Hulstijn Department of Information and Computing Sciences Utrecht University [email protected] November 14, 2024 Theory in IS “Nothing is practical as a good theory” (Lewin 1945) theory practice What is the...

Theory in IS Scientific Research Methods for Business Informatics 2024-2025 Joris Hulstijn Department of Information and Computing Sciences Utrecht University [email protected] November 14, 2024 Theory in IS “Nothing is practical as a good theory” (Lewin 1945) theory practice What is the role of theory in IS? - What is the role of theory in any scientific field? - How to characterize IS? Gregor, S. (2006). The Nature of Theory in Information Systems MIS Quarterly 30(3): 611-642. questions? Categorization of theory types: − Analysis, Explanation, Prediction, EP and Design and Action? Do you agree? Is there an overlap? Are you missing any types of theories? 2 Abstract form and strucure epistemology taxonomy central goals: - analysis, - explanation, - prediction, - prescription types of theory: - for analyzing - for explaining - for predicting - for expl and pred - for design and action 3 Position this article - Author: Shirley Gregor IS 1: accounting information systems, - Context: university, land, background US, IS in business schools, - Journal: MISQ journals basket of 6 or later 8: MISQ, ISR, - Year: 2006 JMIS, JAIS, EJIS, ISI + JSIS, JIT, Conferences: HICSS, ECIS, ICIS, … - Type of article => methods from social sciences - Type of research - Research contribution IS 2: database theory, knowledge based - Demonstrated by … systems, software production, Europe, IS in CS department, - What are alternatives? journals: many (no list) EJIS, AIJ, ACM, IEEE, Conferences: CAISE, RE, ICSOB, … => methods from computer science, philosophy, mathematics 4 Need for Theory in IS? International Journal of Information Management Special issue: CFP Theory building in Information Systems with Big Data-driven Research While big data-driven The availability of andstudies accessare increasingly to big data has gaining changed, popularity as digitalwithin IS research, transformation they rarely initiatives are introspect increasinglywhy a phenomenon maturing globally,isassisted better explained by a theory by the growth and limit the of computational analysis to(Grover capabilities what iset happening al., 2020). by merely Whilst datamining relevant availability anddata. Manyused access suchtostudies be a try to collect major datafor challenge andinformation showcase applications of data science and visualization of unstructured, large volumes of data by systems (IS) research, the current abundance of big data has now resolved this considerably. demonstrating sentiment analysis, text mining, networks, and communities, without significant The theoretical building blocks of IS research come mainly from management theory, contribution to the theoretical context within which the problem is situated (Grover, 2020). Such organization studies do nottheory, attemptbehavioural to explaintheory, computer phenomenon why a particular science theories, and systems is witnessed theory and the data(Barki, Rivard and Talbot, descriptions 1993). Apart rarely contributes from theory towards the core computer building. Thus,science theories, such studies havethea weak other related theories enable connection IS researchers with the to explain relevant theories and IThow users interact artefacts, with technology paying greater attention toartefacts data within individual,and collection organizational, social, analysis (Grover and2020). et al., political contexts and Furthermore, sincethe theimpact of such isinteraction. data collection often dated, Theorysuch studies building, lose timeliness however, seems to and do not have attempt been to explain disrupted by thecausality current (Grover, trends in 2020). big data- driven research, whereby the essence of contributing to theory is increasingly seen to be lacking at all levels of analysis. 5  mathematics Characterizing IS Computer Science economics Management theory Systems theory philosophy sociology Organization theory Behavioural psychology theory … explain how users interact with technology artefacts within individual, organizational, social, and political contexts and the impact of such interaction 6 Theories in IS Theorizeit wiki (Larsen and Eagle 2015) 7 Characterizing IS “An information system (IS) can be defined technically as a set of interrelated components that collect, process, store, and distribute information to support decision making and control in an organization.” (Laudon and Laudon 2014) Study IS artefact in context sources decision problem Context control Organization Users Technology process Society distribute Laws collect store retrieve DB representation 8 Discussing Theory (p 611, 612) consider the questions that arise about the bodies of knowledge or theories in a discipline. - Domain questions: what phenomena are of interest? Core problems? Boundaries? IT artefact? Social behaviour? Both intertwined! Sociomateriality (Orlikowski 2007;2010) - Structural or ontological questions: what is theory? what forms do contributions take? concepts, categories, hypotheses, explanations, causal laws, design patterns? “used rather broadly … conjectures, models, frameworks or body of knowledge” (614) - Epistemological question: how is theory constructed? Can can scientific knowledge be acquired? How is it tested? What criteria are used to judge soundness and rigour? hence this course  - Socio-political questions: Who are the stakeholders? Agreement? Where and by whom has theory been developed? Social, ethical, political issues? role of university 9 Examples of use of Theory? prescriptive / descriptive / testable 10 Different Perspectives Philosophy of Science (Popper): theory provides explanations, predictions and must be testable. (falsifiable) Scientific theories are universal statements. Like all linguistic representations they are systems of signs or symbols. Theories are nets cast to catch what we call ‘the world’; to rationalize, to explain and to master it. We endeavour to make the mesh even finer and finer. Logical Positivism. Vienna Circle. Verification principle: only assertions that are in principle verifiable by observation or experience can convey factual information. NB. what about mathematics; deduction? Interpretivist tradition (e.g. Habermas). History; sociology. “Hermeneutics” “Phenomenology” Understanding the complex world of lived experience from the point of view of those who live it. … the world of lived reality ….situation specific meanings … 11 Different Perspectives (2) necessary / contingent Herbert Simon (1969): Sciences of the artificial “How could one construct an empirical theory? I thought I began to see in the problem of artificiality an explanation of the difficulty that has been experienced in filling engineering and other professions with empirical and theoretical substance distinct from the substance of their supporting sciences. Engineering, medicine, business, architecture and painting are concerned not with the necessary but with the contingent—not with how things are but with how they might be—in short, with design. => Later developed into design science (Hevner et al 2008; Wieringa 2014). 12 Generalization (p 616) Natural sciences: strictly universal statements (really?). Jumping spider anecdote “What is it like to be a bat?” (Nagel 1974; cited by Denett in The mind’s I ) Perception reasoning capabilities construct how we represent the world. Social sciences: always relative to a context. Interpretation Objective versus subjective. Problem with subjectivism: relative truth. Not possible to say: “that is wrong!” both in facts or in ethical issues (social values) Intersubjective (Habermas): enough to establish a common ground. 13 Causality ( p 617) David Hume: defines cause in the following two ways: (D1) An object precedent and contiguous to another, and where all the objects resembling the former are placed in like relations of precedency and contiguity to those objects that resemble the latter. (D2) An object precedent and contiguous to another, and so united with it, that the idea of the one determined the mind to form the idea of the other, and the impression of the one to form a more lively idea of the other. (Treatise 1.3.14.31; SBN 170) [Enc of Philosophy] Nothing else! We ’read’ causality into a sequence of events, to understand it. Kant: categories ~ capacity of the mind to understand world (like time, space) Precedent: before (in time, space) Contiguous: touching, next to (in time, space) 14 Causality (p 617) Riddle of induction: how do we know that the sun will rise tomorrow? E1, E2, …, En => En+1 Causality (approaches) Regularity: when cause happens, effect will happen (law of nature) Counterfactual: without cause, effect would not have happened e.g. but-for clause in legal reasoning Probabilistic: cause increases likelihood of effect Manipulation / teleological: event produced at will NB. Is not a causal explanation. Eng: because = (1) motivation [ human goal ] , (2) cause [ natural science ] NL: (1) omdat ~ reden, (2) doordat ~ oorzaak 15 Explanation (p 617) A particular fact or event is explained by pointing out the scientific law that governs its occurrence. (Hempel) To explain something is merely to show how to derive it in a logical argument from premises that include a covering law. in S, why E? L: F => E, S |= F / E Asymmetry problem (length of flagpole explains shadow and vice versa) Explanation is a communicative process 16 Aside: nature of explanation … Nomological-deductive theory: based on ‘laws of nature’ (Hempel 1965) Why does a log of wood float in water? m Something to be explained A situation; facts; interpretation A law of nature; rule; principle 17 Explanations Something to be explained | situation | some regularity. Causal explanation: state or event | situation | law of nature Why do migrating geese form a V-shape? Motivation: human action | situation + goal | practical reasoning rules Why does that driver brake? Abduction. Mechanism: state or event | situation + objective | design principles Why can’t I save this file? ? 18  Miller 2019 Everyday explanations Miller (2019): everyday explanations are contrastive—people do not ask why event A happened, but rather why event A happened instead of some event B (counterfactual) selected (in a biased manner) — humans select one or two causes from an infinite number of causes (parsimony) probabilities probably don’t matter — referring to probabilities in explanation is not as effective as referring to causes. The most likely explanation is not always the most convincing explanation (narrative; rhetoric) explanations are social — transfer of knowledge, presented in interaction, based on the explainer’s beliefs about the explainee’s beliefs (partial; interactive) So, explanations are contextual. 19 Taxonomy of theory types (p 620) 20 Components of Theory For all theories Means of representation: notation, mathematics, diagrams, … Constructs: phenomena of interest, units of analysis Statements of relationship: associative, compositional, directional, conditional, causal, … Scope: degree of generality For some theories (depending on purpose) Causal explanations Testable propositions / hypotheses Prescriptive statements: how to … 21 Literature review But, tells us more about MISQ and ISR. 22 Interrelationships weak But … EP already involves explanation and prediction Are they theory types or are they scientific tasks? 23 Discussion Do some theory types belong to particular research paradigms? No! Joris: actually yes. See e.g. examples on page 632-633 Is one type of theory more valuable than another? No. Should the word theory be used for all five classes of theory? Yes. “We would like writers to feel free to use theory whenever they are theorizing. Modesty is all very well, but leaning over too far backward removes a good word from currency.” (Weick 1995, p 386) Should one type of theory precede the other? Yes, see Figure 1. Are these five theory types unique to IS? No. 24 Conclusions Q1. What is the role of theory in any scientific field? Q2. How to characterize IS? We need theory in IS, to interpret and give meaning to findings in “Big data” Traditional positivism versus interpretativist tradition. Both are needed. Tasks: analysis, explanation, prediction, E&P, design and action (prescription) A theory consists of: representation, constructs, relationships, scope (all) + causal explanations and predictions, testable propositions/hypotheses, prescription (how to) IS is a multidisciplinary field; theory from neigbour disciplines. Theorizeit Wiki IS as a field studies the relationship between the IT arefact and the social use. A central notion is that of representation. Examples: Digital History (critical), Sia and Soh (ontology), Orlikowski (entanglement) 25 Next week Empirical Research Strategies Wohlin et al 2017; Ch. 2.1 - 2.7, Ch5 Homework: Read Sia and Soh (2007) and Orlikowski (2007) again, but now focusing on the research approach. Make a summary of the research steps. How do Sia and Soh (2007) and Orlikowski (2007) achieve ‘relevance’? How do Sia and Soh achieve ‘rigour’? And Orlikowski? 26 Examples 27 Example: misalignment in ERP -- To customize or not? Sia & Soh 2007 ERP is essentially about integration and standardization. So despite ‘best practices’ built into ERP modules by vendors, there are always going to be situations in which the ERP system and business needs. do not fit. What to do? Business fit ? Business needs ERP system needs 1. Business adjust. Adopt ‘vanilla’. Limited configuration possible 2. ERP adjust. Customization. But, changes are hard to maintain and expensive. Siew Kien Sia and Christina Soh (2007) An assessment of package-organisation misalignment: institutional and ontological structures. European Journal of Information Systems. 16: 568-583 28 Sia and Soh: ontology and misalignment Sia & Soh 2007 In IS, an ontology represents knowledge as a set of concepts within an application domain, and their relationships. An ontology is used to reason about entities in a domain. An ontology represents the ‘meaning’ of symbols (semantics). Principle: for an information system to be stable, its representation structure must have a ‘good’ mapping to the real world it seeks to represent. (good ‘fit’) So … from an ontology perspective, misalignments are instances where aspects of the real world or business needs are not adequately represented by the model implicit in the package. 29 Ontology and misalignment Sia & Soh 2007 Deep structure conveys the core meaning of the real-world system that the information system is intended to model. Bunge-Wand-Weber: the real world is Made up of things (atoms, persons, artefacts, and social systems). Things have properties (essential characteristics). Existing at certain states (conceivable or lawful ranges of values). The states of things change through transformations (business rules; operations). Example: an accounting information system represents Things: bank accounts and debtors accounts Properties: current or savings accounts, currencies, debit / credit States: outstanding amounts or balances, Transformations: transactions, rules for general ledger updates Surface structure is concerned with how real-world meanings are conveyed through the interface between the information system and its users (interaction; reporting format) => easier to change 30  Sia & Soh 2007 Typology of misalignments Imposed: because of legal requirements or essential characteristics Voluntary: company strategy; Example 1. Children hospital B specializing in maternity care. DB cannot link child to mother and vice versa. Imposed, deep. Table 3. p 573. Example: Sociomateriality (Orlikowski 2007) Starts from organization studies, as part of sociology. Technology overlooked. “Everyday organizing is inextricably bound up with materiality and [..] this relationship is inadequately reflected in organizational studies” Propose an alternative: “constitutive entanglement of the social and the material in everyday life” Method: draw upon a few empirical examples Example 1. Google Example 2. Blackberry Before: either a techno-centric perspective (functional), or human-centric perspective (sense making). Instead: the social and material are inextricably related. “There is no social that is not also material, and no material that is not also social” 32 Sociomateriality (Orlikowski 2007) Example 1. Google − Outcome of a search depends on technology (servers; algorithms; configurations), but also on collective behaviour of people (website hosts; searchers; advertisers; news sites; etc). − The social behaviour (searching) wouldn’t exist without the ranking algorithm. − The ranking algorithm wouldn’t work without the social behaviour (i.e. count links) 33 What does ‘entangle’ mean? social practice technology, material Please consider 1. an example of technology shaping social practices. the mobile telephone has made appointments more flexible public transport chipcard changes traveling (check in and out) 2. an example of social practices re-shaping (use of) technology Vinyl and record players becoming ‘hip’ again 3. an example of a sociomaterial practice (inextricably linked) mobile networks used in Africa as banks for micro payments Facebook developed into a news site with political influence 34 Sociomateriality (Orlikowski 2007) Example 2. Blackberry − “Viewing professionals as ‘using’ their BlackBerrys to communicate significantly overlooks how their communication practices have been substantially reconfigured through their engagement with BlackBerrys.” − “There are not many people here who don’t check their BlackBerry every seven or eight minutes.... There aren’t many people you can email that you won’t hear back from right away.” − “In general... people’s expectation levels have gone up... People presume that it’s fairly easy to reach you 24/7. So I think you have a lesser degree of sensitivity just sending an email.” − “But at what point of your day does the workday end? This tool makes it difficult for that workday to end. I mean, there’s no doubt that my day doesn’t really come to an end until I go to bed.” 35 Sociomateriality Conclusions: The performativity of the BlackBerrys is sociomaterial, shaped by the particular contingent way in which the BlackBerry service is designed, configured, and engaged in practice. For example, the ‘push email’ capability inscribed into the software running on the servers has become entangled with people’s choices and activities to keep devices turned on, to carry them at all times, to glance at them repeatedly, and to respond to email regularly For example, […] we see how the researcher’s Google search is constituted by the performativity of computers, networks, software, algorithms, directories, databases, and infrastructure, as these are enacted by the human agencies entailed in their design, construction, and operation. The resulting sociomaterial assemblage that delivers the search results to our researcher is both emergent and contingent. 36 37  (Davis, 1989) Example: TAM Technology Acceptance Model (Davis 1989); UTAUT (Venkatesh et al 2003) What causes people to accept or reject information technology? (p 320) Perceived usefulness: “degree to which a person believes that using a particular system would enhance his or her job performance” Perceived ease of use: “degree to which a person believes that using particular system would be free of effort” perceived usefulness Intention to use usage perceived ease of use 38 Theoretical Foundations Literature review: a specific ‘story’ summarizing trends and theories performance dimension most predictive (Schultz and Slevin 1975) self-efficacy theory cost-benefit paradigm (benefit ~ job performance, cost ~ effort) adoption of innovations evaluation of information reports channel disposition model non-MIS studies Claim: they all amount to the same thing (convergence of findings) 39 Research Steps Scale development and pre-test (10-item scales) Study 1. N = 120 at IBM Toronto; Questionnaire rating usefulness and easy of use using 10 questions each, rated on 7-point scales ) Factor analysis (Table 6) followed by scale refinement 10 => 7 Relationship to ‘use’ : all correlations were significant Study 2. N = 40; similar to lab tests Factor analysis (Table 7): factors fit Stronger correlations 40 What factors contribute to PU,PEOU? Factor analysis: do the questions represent real factors, that contribute to the constructs? 41 Conclusions “One of the most significant findings is the relative strength of the usefulness- usage relationship, compared to the ease of use-usage relationship” (p 334). “The regression results suggest that ease of use may be an antecedent to usefulness, rather than a parallel direct determinant of usage”. … “results are consistent with an ease of use usefulness usage chain of causality”. (p 334). perceived usefulness usage perceived ease of use 42 42 Criticism on TAM (1) Why use questionnaires? Why not study real usage? Perform series of experiments to determine what features of a system make it usable! (2) Theory of reasoned action (attitude intention action) provides a very poor explanation. No environment. Attempts, not actual usage. Human Computer Interaction: cognitive theories about usability usability = effectiveness (relative to task) efficiency = task success duration/ nr. of steps / costs easy to lean, remember, explain (3) Why study individual users? Companies buy software! Procedures make it obligatory! There may not be enough budget. Groups of friends may influence decision. Etc. (4) Assumes a single decision. In practice, decision is repeated and based on previous experiences with the system, and habit. (feedback control) 43 Unified Theory of Acceptance and Use of Technology Venkatesh et al (2003) “Seven constructs appeared … in one or more of the individual models. Of these,.. four constructs will play a significant role as direct determinants of user acceptance and usage behavior: performance expectancy, effort expectancy, social influence, and facilitating conditions...., attitude toward using technology, self-efficacy, and anxiety are theorized not to be direct determinants of intention. Can still adjust. Add e.g. trust … key moderators (gender, age, voluntariness, and experience).” (p 447) 44 Example: Interpretist Tradition and Digital History Digital History: study of digitized historical data, using IT tools and techniques. Digital Hermeneutics: critically examine the search algorithm, the sources, analytics tools, and resulting interpretations and narratives of a historical debate (Andreas Fickers) close reading – distant reading 45  Data Science: pipeline or dialogue? “Conversing” with Qualitative Data Visualization merge & clean Conditions, to report record interprete RawData Raw Data answer E events / states questions, Sources focus select test debates query instrument 46 References Rens Bod (2010) A New history of the humanities, Oxford University Press. Davis, F. D. (1989). "Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology." MIS Quarterly 13(3): 319-340. Fickers, A. and J. Tatarinov (2022). Digital History and Hermeneutics. De Gruyter. Gregor, S. (2006). The Nature of Theory in Information Systems MIS Quarterly 30(3): 611-642. Hempel, C. G. (1965). Aspects of Scientific Explanation and Other Essays in the Philosophy of Science. New York, The Free Press. Larsen, K. R., Eargle, D. (Eds.) (2015). Theories Used in IS Research Wiki. Laudon, K. C. and J. P. Laudon (2014). Management Information Systems, Pearson. Lewin, K. (1945) The Research Centre for Group Dynamics at Massachusetts Institute of Technology, Sociometry (8): 126-135. Miller, T. (2019). Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence 267 (2019) 1–38 267: 1-38. Orlikowski, W. J. (2007). Sociomaterial Practices: Exploring Technology at Work. Organization Studies 28(9): 1435– 1448. Sia, S. K. and C. Soh (2007). An assessment of package-organisation misalignment: institutional and ontological structures. European Journal of Information Systems 16: 568-583. Simon, H. A. (1996). The Sciences of the Artificial. Cambridge: MA, MIT Press. Venkatesh, V., M. G. Morris, G. B. Davis and F. D. Davis (2003). "User acceptance of information technology: Toward a unified view." MIS Quarterly 27(3): 425–478. 47