Monitoring Commitments in People-Driven Service Engagements

Kalia, Anup K.; Motahari-Nezhad, Hamid Reza; Bartolini, Claudio; Singh, Munindar P.

doi:10.1109/scc.2013.62

Cited by 17 publications

(9 citation statements)

References 17 publications

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“…In the first phase, subjects read 33 emails selected from the Enron email corpus [28,29] and provided a trust value ranging from 0 to 1 between the senders and receivers of email. The emails were selected on the basis of their containing sentences that indicate commitment creation, satisfaction, or violation-such sentences having been identified using Kalia et al 's [11] method. We augmented the dataset with 28 synthetic sentences indicating commitment satisfaction or violation, which do not occur frequently in the corpus.…”

Section: Discussionmentioning

confidence: 99%

“…Identify commitment operations from the interactions. For emails, using Kalia et al 's [11] trained classifier; for games, we find these in the chat interface and analyze them manually. Table 2 shows examples of commitment operations identified in emails and chats, respectively.…”

Section: Evaluation Processmentioning

confidence: 99%

“…In contrast, we understand trust and commitment in probabilistic terms, considering the outcomes of specific commitments and their effect on the trust relationships between the concerned parties. We conduct two empirical evaluations, respectively, on emails (automatically analyzed using Kalia et al 's approach [11]) and via the Colored Trails cooperation game [12] (analyzed manually). We show how to train the model parameters so as to capture a user model indicating each user's propensity to trust given commitment outcomes.…”

Section: Introductionmentioning

confidence: 99%

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Güven: estimating trust from communications

2016

Self Cite

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The extent to which an agent trusts another naturally depends on the outcomes of their interactions. Previous computational approaches have treated the outcomes in a domain-specific way. Specifically, these approaches focus on the mathematical aspect and assume that a positive or negative experience can be identified without showing how to ground the experiences in real-world interactions, such as emails and chats. We propose Güven, an approach that relates trust to the domain-independent notion of commitments. We consider commitments since commitment outcomes can be associated with experiences and a large body of works exist on commitments that include commitment representation and semantics. Also, recent research shows that commitments can be extracted from interactions, such as emails and chats. Thus, we posit Güven can provide an useful basis to infer trust between agents from their interactions. To evaluate Güven, we conducted empirical studies of two decision contexts. First, subjects read emails extracted from the Enron dataset (and augmented with some synthetic emails for completeness), and estimated trust between each pair of communicating agents. Second, the subjects played the Colored Trails game, estimating trust in their opponents. Güven incorporates a probabilistic model for trust based on commitment outcomes; we show how to train its parameters for each subject based on the subject's assessments. The results are promising, though imperfect. Our main contribution is to launch a research program into computing trust based on a semantically well-founded account of interpersonal interactions.

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Section: Discussionmentioning

confidence: 99%

Section: Evaluation Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Güven: estimating trust from communications

2016

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“…For instance, Kalia et al . (2013) use NLP in order to identify messages concerning tasks and commitment. They parse verbs and sentences in order to identify tasks and track messages between senders and receivers.…”

Section: Text Analysis For Cop Performance Evaluationmentioning

confidence: 99%

Systematic community of Practice activities evaluation through Natural Language Processing: application to research projects

Goepp

Matta

Caillaud

et al. 2019

AIEDAM

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Community of Practice (CoP) efficiency evaluation is a great deal in research. Indeed, having the possibility to know if a given CoP is successful or not is essential to better manage it over time. The existing approaches for efficiency evaluation are difficult and time-consuming to put into action on real CoPs. They require either to evaluate subjective constructs making the analysis unreliable, either to work out a knowledge interaction matrix that is difficult to set up. However, these approaches build their evaluation on the fact that a CoP is successful if knowledge is exchanged between the members. It is the case if there are some interactions between the actors involved in the CoP. Therefore, we propose to analyze these interactions through the exchanges of emails thanks to Natural Language Processing. Our approach is systematic and semi-automated. It requires the e-mails exchanged and the definition of the speech-acts that will be retrieved. We apply it on a real project-based CoP: the SEPOLBE research project that involves different expertise fields. It allows us to identify the CoP core group and to emphasize learning processes between members with different backgrounds (Microbiology, Electrochemistry and Civil engineering).

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“…As noted from our experiences with IT services sales domain, those domains that have to deal with large amounts of (unstructured) data to understand the process updates over different channels to dynamically adapt business processes. We have taken the first step in this direction by processing sales people interactions over email and chat to automatically identify process tasks, and any update to them, reported in [12]. We envision a self-learning, cognitive sales process that is fully instrumented with probes and data collected from both internal and external sources.…”

Section: Future Bpm Platforms For Supporting Analytics-driven Bpmmentioning

confidence: 99%

Towards Cognitive BPM as the Next Generation BPM Platform for Analytics-Driven Business Processes

Nezhad

Akkiraju

2015

Business Process Management Workshops

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Abstract. Human-centric business processes in the enterprise are knowledgeintensive, and rely on human judgment for decision making. This reliance impacts the integrity, uniformity, efficiency and consistency of the process enactment over time. We describe our experience and challenges in driving the adoption of business process management tools in the sales process at a large IT services provider. We argue that in knowledge-driven and human-centric activities such as IT services sales, current technologies do not meet the needs. They do not support data capture and analytics around process and using it to reason about and take action on the process. Process definition and enactment, both, needs to be dynamic, flexible and adaptive based on the understanding of the state of affairs. We present a vision and a framework for a cognitive BPM system where the knowledge management, user interaction and analytics are employed to dynamically assess and proactively adapt the process based on the results of data analytics and continuous learning from the actions taken.

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Monitoring Commitments in People-Driven Service Engagements

Cited by 17 publications

References 17 publications

Güven: estimating trust from communications

Güven: estimating trust from communications

Systematic community of Practice activities evaluation through Natural Language Processing: application to research projects

Towards Cognitive BPM as the Next Generation BPM Platform for Analytics-Driven Business Processes

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