- > To analyze our sample of 67 papers, we applied a coding scheme we derived in the first review we conducted in 2014. This previous analysis had suggested that (1) some of the design principles focused attention on usersâ use of artifacts; (2) some talked mainly about the artifacts and little about the users; and (3) the remainder attended to both (i.e., focused on both artifact and action). We used this simple coding scheme as the basis for our analysis. iâm not sure what this actually achieved. Surely there were other dimensions along which different design theory papers differentiated? why are the only three possibilities and â the only three possible interesting pieces of data to come from each of these papers â whether they are about artifact rules, user rules, or both?
All Highlights
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- > the design of IT artifact involves interpreting and converting design principles of the theory into artifact features.
Page 19 - > We term the features designed specifically to instantiate design principles of a design theory focal features.
Page 19 - > Cases 1 and 2 show that instantiating abstract design principles requires making implementation decisions that cannot be determined from the principles alone. Additional knowledge must be brought to bear by a practitioner to complete the project. In the Appendix, we provide a detailed analysis to show that in real-world development following a design theory, design of focal features involves a series of transformations of the design principles, in which each iteration brings additional knowledge (from outside the design theory). The mapping between design principles and focal features is not 1:1 as there can be many ways to instantiate a principle (e.g., collecting information âin terms of attributesâ in Case 1), each leading to different outcomes â a concept known as multifinality (Kruglanski et al., 2013; Prat et al., 2015).
Design prescriptions have multifinality
Page 19 - > As there may be many ways of manifesting an abstract principle and no specific guidance on how to select the best design choices, the question arises whether and to what extent outcomes are contingent on specific focal features. In some cases, the eventual design might produce the predicted outcome, but in others it might not. Table 2 illustrates this for Case
Page 20 - > 2, showing that the same design principle was converted into focal features in multiple ways â all assumed to be consistent with the principle â resulting in different outcomes
This example also points to a âimplementers me make mistakesâ dimension of DTI. In this case I wouldnât count melting ice underneath a polar bear as âdirect feedbackâ, it is really more like a causal illustration of the effect of energy consumption.
Edit: the authors discuss this later
Page 20 - > Design principles may be orthogonal â meaning that the focal features derived from one principle do not interact with any focal features derived from another. Alternatively, design principles might be oblique â in this case design features derived from one design principle might interact with design features derived from a second one. This means the complexity of instantiating multiple principles, each of which may be operationalized in several ways via different focal features, can be very high and instantiation of one principle may interfere with another. Such interactions might either strengthen or weaken effects on outcomes of interest.
Page 21 - > Thus, a challenge is providing effective support and guidance for practitioners to instantiate design principles into appropriate focal features such that the predicted outcomes occur. Accordingly, we propose Dimension 1.1 (Focal features) of DTI as indeterminacy when designing focal features based on design principles of the design theory.
Page 21 - > The need to make IT artifacts work requires the practitioner to develop features that relate to requirements other than the design principles or other components of the design theory. We term these auxiliary features. These features are commonly needed to ensure good design (Baskerville, Kaul, & Storey, 2018), provide generally expected functionality, physical infrastructure, or comply with legal, cultural or ethical norms (assuming these are beyond the scope of a given theory).
Page 22 - > It is possible that, even when all focal features are instantiated properly, the presence of auxiliary features may mitigate or even reverse the âdesiredâ effects stipulated by the design theory. Lukyanenko et al. (2015, 2014) view this as a threat to instantiation validity â ensuring that an artifact designed to instantiate a theory (e.g., for the purpose of behavioral theory testing or development of an IT artifact based on a design theory) not only faithfully operationalizes the focal theory, but is also free of confounds due to the presence of additional features necessary to make the artifact work.
Page 22 - > As discussed before, an IT artifact is a complex and open system. This implies it may not be reducible to the sum of its focal and auxiliary features. Instead, it may have emergent features â elements of form and behavior that emerge from the complex interaction between its focal and auxiliary features (Prat, Comyn-Wattiau, & Akoka, 2014). Following Prat et al. (2014), we argue DSR research needs to consider both individual IT features and an IT artifact as a whole.
Page 23 - > we propose Dimension 1.3 (Emergent features) of DTI as indeterminacy in ensuring any emergent features of the artifact accord with the design theory and do not prevent the attainment of the target outcome(s).
Page 24 - > design theories typically do not specify the full causal chains linking the artifact to the outcomes. They routinely omit potentially pertinent moderator and mediator constructs and their interrelationships (e.g., when a mediator is moderated by another variable, see Tams, Legoux, & Leger, 2018). A moderating construct is a construct that influences the direction or magnitude of the relationship between the antecedent and outcome constructs.
Page 25 - > antecedent and outcome constructs A mediating construct, on the other hand, is one assumed to stand between the
Page 25 - > In sum, to increase the likelihood of a desired outcome following the instantiation of a design theory into an artifact, the causal chains connecting the artifact to the outcomes in the deployment setting need to be well-understood and managed. Lack of guidance on how to do this creates ambiguity and uncertainty in practice.
Page 25 - > we propose Dimension 2.1 (Causality) of DTI as indeterminacy when deploying the artifact in the specific real-world context to ensure that the target outcomes are attained.
Page 26 - > DSR lacks the practice of sharing measurement instruments and making them publicly available for practitioners. As a result, a practitioner might reach incorrect conclusions following the deployment of the artifact design based on the design theory.
Page 26 - > we define Dimension 2.2 (Measurement) of DTI as indeterminacy in ensuring that the outcomes attained are properly measured and valid conclusions are reached.
Page 26 - > in an artifact, specification of causality, and measurement. it involves uncertainties related to additional features of the artifact, the interaction of features that we do not conceptualize DTI as merely a challenge of translating design principles. Rather, DTI is a multidimensional problem. These dimensions (summarized in Table 3) show
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- > What the above attitudes and behaviors all have in common is that they are deviations from accepted methods for learning and growth.
Page 4 - > Review the list of perfectionist characteristics, then take a few hours or days â however long you need â and write out how those characteristics show up in your life and work.
Page 4 - > Perfectionism is fundamentally delusional, and when its delusions are exposed to the light of day they are easily discredited.
Page 5 - > Notice, also, how a shame-free examination of the causes of her underproductivity enables her to easily problem-solve.
Page 6 - > Self- compassion calls it as it sees it â and with much more accuracy than perfectionism.
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- > Overidentification with the work. So when it goes well, you’re on the top of the world, but when it goes badly (which a perfectionist think is always happening) you’re down in the dumps. This is an exhausting cycle at best; at worst it can terrorize you away from your work. Also, seeing your work as a justification, vindication, legitimization, or other personal validation.
Page 2 - > Grandiosity. Perfectionists think that things that are hard, or even impossible, for other people should be easy for them. This leads to all kind of antiproductive behaviors, including a lack of interest in planning, lack of willingness to consult mentors, and attempts to work without adequate resources.
These traits all certainly add up to perfectionism, but they likely present at different levels in everybody, leading to a variety of different types of perfectionism among different spectra.
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- > once the fabricated object leaves the factory, there is the way in which it will design the actions of its users, according to the inherent delimitations of how it can be used â here we can think of equipment, appliances and other functional objects as having âhorizons of useâ,
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- > We have already encountered the hermeneutic circle â in the example of using a machine tool wherein knowledge comes to be inscribed by being with the âdesigning-beingâ of the tool, this in turn modifying the being of the tool user. To complete this circle a third step is added â interpretation â in which the âdesigned beingâ of the user acts back upon the tool or the material being worked on, with the effect of modifying or improving the process. This ushers in the possibility of learning and change. In general terms, the hermeneutic circle is a way of explaining a structural condition of being-in-the-world. It operates in all kinds of situations, from everyday coping to more formal acts of interpretation such as historical enquiry or the reading of literary texts, which is where it first surfaced as a philosophical concern. As we have seen, Heidegger gives primacy to the significance of Daseinâs pre-ontological understanding of things â the understandings that come from being-with-things and with others rather than from introspection or from conscious acts of interpretation. Yet the commonsense model, inherited from traditional philosophy, is that interpretation comes before understanding, that it is the means toward understanding. Heidegger reverses this: âAny interpretation which is to contribute to understanding, must already have understood what is to be interpretedâ.15
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- > If your answer to Question 1 is yes and your answer to Question 2 is âsampleâ, you need ICC(2). In SPSS, this is called âTwo-Way Random.â Unlike ICC(1), this ICC assumes that the variance of the raters is only adding noise to the estimate of the ratees, and that mean rater error = 0. Or in other words, while a particular rater might rate Ratee 1 high and Ratee 2 low, it should all even out across many raters. Like ICC(1), it assumes a random effects model for raters, but it explicitly models this effect â you can sort of think of it like âcontrolling for rater effectsâ when producing an estimate of reliability. If you have the same raters for each case, this is generally the model to go with. This will always be larger than ICC(1) and is represented in SPSS as âTwo-Way Randomâ because 1) it models both an effect of rater and of ratee (i.e. two effects) and 2) assumes both are drawn randomly from larger populations (i.e. a random effects model).
Page 3 - > After youâve determined which kind of ICC you need, there is a second decision to be made: are you interested in the reliability of a single rater, or of their mean? If youâre coding for research, youâre probably going to use the mean rating.
Page 4 - > We add â,kâ to the ICC rating when looking at means, or â,1â when looking at the reliability of single raters.
Page 4 - > After youâve determined which specificity you need, the third decision is to figure out whether you need a measure of absolute agreement or consistency.
Page 4 - > If using a mean [ICC(#, k)], consistency is typically fine, especially for coding tasks, as mean differences between raters wonât affect subsequent analyses on that data.
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- > Teachers were not optimistic that the IP reforms would lead to gains in student outcomes. In the three districts, typically less than 50 percent of teachers agreed that, in the long run, students would ben- efit from the teacher-evaluation system, and that percentage declined over the course of the reform.
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- > If you look at a forest from far enough away, it can appear to be a single, unified whole. But this is a misperception, and in fact there is nothing more there than the
Page 3 - > trees that make up the forest.
Page 3 - > the analogy is a false one, because atoms, unlike the trees that make up a forest, are not observable. In the case of the forest, it makes sense to say that we are really perceiving trees, and simply mistaking them for some larger whole. We do, after all, really see the trees. But in the case of an object like a pot, it does not make sense to say that we are really perceiving atoms, and simply mistake them for a pot.
Page 3 - > wholes have causal powers and properties that are irreducible to the sum of the powers and properties of the parts.
Page 3 - > Uddyotakara illustrates this idea by noting that âyarn is different from the cloth made from it, since the two have different causal capacitiesâ (p. 107).
Page 3 - > He also argues that yarn must be different from the cloth made from it insofar as the former is a cause of the latter (Ibid.). And he distinguishes this cause from the clothâs âother causes,â such as âthe weaverâs loom.â
Page 3 - > Here we might seem to have an implicit distinction between what Aristotelians call material cause (the yarn) and efficient cause (the weaverâs loom). But NyaĚya-VaisĚesĚŁika speaks of a thingâs âinherence causeâ rather than material cause, i.e. that in which the qualities of the composite inhere. And the notion of an inherence cause is broader than that of material cause, since it can include things other than matter (e.g. a location).
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- > Billable rates, estimates and other features are defined on the project level.
Page 1 - > Can be associated with multiple projects.