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Use Cases

Examples of Knowledge Extraction and Synthesis Analyses

This appendix illustrates possible analyses of digitized knowledge, i.e., potential use cases of the search and analysis app (Analysaurus).

Querying is based on openCypher (Francis et al. 2018), an industry standard for querying graph databases[1], and R is used for further processing of query results.

Element Level: At the element level, we can measure the semantic similarity of elements using NLP methods. For example, Table A.1 shows three different semantic similarity measures of concepts based on their labels (Song et al. 2021b).

Concept 1Concept 2String-based(Cosine; Jaccard)Corpus-based(Latent semantic analysis)Knowledge-based (Wordnet)
Internet privacy concernsUser privacy concerns0.6670.9991
Information privacy concernsPrivacy concern0.4080.9981
RisksCosts00.9980.167
TrustWork overload000.133
CostsBenefits00.6150.308
Table A.1: Semantic Similarity of Concepts based on Their Labels

In a body of literature, it is quite common to have multiple definitions for the same concept. However, when knowledge is digitized, concept similarity computation is possible (see Table A.2 for the example of trust). 

The similarity of concept definitions can be computed using NLP tools such as BERT. Inspection of Table A.3 reveals that definition 3 has the highest average similarity score and the most similar definitions are 1 and 5 (bold), while the least similar are 1 and 7, and 5 and 7 (italics).

DefinitionPublication
1We define trust as the subjective assessment of one party that another party will perform a particular transaction according to his or her confident expectations, in an environment characterized by uncertainty.Ba, S., & Pavlou, P. A. (2002). Evidence of the Effect of Trust Building Technology in Electronic Markets: Price Premiums and Buyer Behavior. MIS Quarterly, 26(3), 243.
2Trust is argued to be rooted in perceptions of teammates’ ability, benevolence, and integrity (Jarvenpaa et al. 1998). Ability refers to the aptitude and skills that enable an individual to be perceived as competent by teammates (Jarvenpaa et al. 1998; Mayer et al. 1995). Benevolence refers to the extent to which an individual is believed to be willing to help teammates beyond personal motives or individual gain. 1995). Integrity refers to the extent to which an individual is believed to adhere to a set of principles thought to make her dependable and reliable.Piccoli, & Ives. (2003). Trust and the Unintended Effects of Behavior Control in Virtual Teams. MIS Quarterly, 27(3), 365.
3Trust is defined as the buyer’s intentions to accept vulnerability based on her beliefs that the transaction will meet her confident expectations.Pavlou, Liang, & Xue. (2007). Understanding and Mitigating Uncertainty in Online Exchange Relationships: A Principal-Agent Perspective. MIS Quarterly, 31(1), 105. 
4The user beliefs in the recommendation agents’ competence, benevolence, and integrity. The beliefs that 1) the recommendation agent has the ability, skills, and expertise to perform effectively 2) the recommendation agent cares about the user and acts in the user’s interest 3) the recommendation agent adheres to a set of principles (e.g., honesty and promise keeping) that the user finds acceptable,Xiao, & Benbasat. (2007). E-Commerce Product Recommendation Agents: Use, Characteristics, and Impact. MIS Quarterly, 31(1), 137. 
5Trust reflects one party’s belief that its requirements will be fulfilled through future actions undertaken by the other party.Goo, Kishore, Rao, & Nam. (2009). The Role of Service Level Agreements in Relational Management of Information Technology Outsourcing: An Empirical Study. MIS Quarterly, 33(1), 119. 
6Trust is conceptualized as a single variable and refers to general confidence in the website.Cyr, Head, Larios, & Pan. (2009). Exploring Human Images in Website Design: A Multi-Method Approach. MIS Quarterly, 33(3), 539. 
7The extent to which a buyer perceives in a seller’s ability (i.e., skills, competencies, and characteristics in seller his/her products online), integrity (adhering to a set of principles that the buyer finds acceptable), and benevolence (i.e., doing good toward the buyer).Ou, C. X., Pavlou, P. A., & Davison, R. M. (2014). Swift Guanxi in Online Marketplaces: The Role of Computer-Mediated Communication Technologies. MIS Quarterly, 38(1), 209–230.
Table A.2: Definitions and Defining Publications for Trust

Definition1234567
1
20.60
30.800.63
40.570.760.63
50.810.610.770.57
60.590.500.570.490.46
70.380.570.520.760.380.45
Means0.620.610.660.630.600.510.58
Table A.3: Concept Similarity for Trust Definitions

Element-Relationship Level: Visualizations can be generated to focus on a particular element and its role. For example, the antecedents of trust can be seen in Figure A.1.

Figure A.1: Antecedents of Trust

All paths starting with an element, ending with an element, or between two elements can be detected (see Table A.4). 

Trust -> Perceived information asymmetry -> Perceived uncertainty -> Purchase intentions -> Actual purchases
Trust -> Information security concerns -> Perceived uncertainty -> Purchase intentions -> Actual purchases
Effective use of feedback system -> Presence -> Trust
Effective use of instant messenger -> Interactivity -> Trust
Satisfaction -> User control -> Trust
Behavior control -> Vigilance -> Trust
Incongruence -> Salience -> Trust
Table A.4: Causal Paths Starting or Ending with Trust

Social network analysis can holistically assess the importance of an element in a knowledge network (see Table A.5). Specifically:

A high Betweenness centrality suggests that an element is an influential mediator.

A high Closeness centrality aggregates the most direct causal influence that an element has on other elements in a knowledge network. 

A high Outdegree centrality of an element in a knowledge network indicates that an element is a fundamental antecedent. 

A high Indegree centrality score signals that an element is a commonly accepted consequent. 

ConceptBetweenness centralityCloseness centralityOutdegree centralityIndegree centrality
Trust7260.381761
Perceived uncertainty1260.2624
Mutual dependence810.282816
Product expertise650.3199
Purchase intentions440.2112
Harmonious conflict resolution400.281510
Perceived information asymmetry380.3413
Fears of seller opportunism380.3413
Information privacy concerns380.2913
Information security concerns380.2913
Table A.5: Concept Network Centrality Measures

Model Level: Similarity can be computed for any pair of causal models for a domain of interest. Figure A.2 shows two privacy models and their conceptual isomorphism score, a measure between 0 and 1 (Song et al. 2021b).

Figure A.2: Similar Models for Privacy (Song et al. 2021a)

Theory Level: Theories guide knowledge creation. For the MISQ trust curation (Söllner et al. 2016), there is a wide variety of theoretical foundations, which could suggest that there is little coalescence around theories for explaining trust formation (see Table A.6).

TheoryFrequency
Agency theory3
Technology acceptance model2
Social exchange theory1
Social capital theory1
Theory of planned behavior1
Theory of reasoned action1
Social presence theory1
Theories of human information processing1
The theory of interpersonal similarity1
The theories of trust formation1
The theories of satisfaction.1
Innovation diffusion theory1
Incomplete contract theory1
Relational exchange theory1
Visual rhetoric theory1
Message exchange theory1
Adaptive structuration theory1
Media synchronicity theory1
Table A.6: Theories Referenced in the MISQ Trust Curation