Artificial Intelligence (AI) Capabilities, Trust and Open Source Software Team Performance

Abstract

In recent years, Artificial Intelligence (AI) has become a key element in digital platforms for improving performance. Despite vast body of knowledge it is yet unclear on how AI can be successfully integrated into platforms and what are the key mechanisms that drive the performance in digital platforms such as open source. To investigate this phenomena a survey has been conducted to understand how AI capabilities (i.e., capabilities associated with AI resources/usage) on Open Source Software (OSS) team performance. The analysis highlights the role of trust in driving OSS team performance and suggests that designers need to pay more attention to cognition when dealing with AI technologies and opportunities.

Appendix

1.1 AI Infrastructure Capability

Relative to other open source projects, please evaluate your open source projects’ AI (artificial intelligence) infrastructure capabilities (for example bots, recommendations etc.) in the following areas 1–7 scale (1 = strongly disagree, 7 = strongly agree).

• AIIC1: Bot services (for example chatbots, trouble shooting, automation services etc.) are helpful in navigating my open source work.

• AIIC2: I like the recommendations and automatic notifications features.

(Source: Lu, Y., & K. (Ram) Ramamurthy (2011). Understanding the link between information technology capability and organizational agility: An empirical examination. MIS quarterly, 931–954).

1.2 AI Business Spanning Capability

Relative to other open source projects, please evaluate your open source projects’ AI management capability in responding to the following on a 1–7 scale (1 = poorer than most, 7 = superior to most).

• AIB1: Developing a clear vision on how AI contributes to business value.

• AIB2: Integrating open source project planning and AI planning effectively.

• AIB3: Enabling functional areas and general management’s ability to understand the value in AI investments.

• AIB4: Establishing an effective and flexible AI planning process and developing a robust AI plan.

(Source: Lu, Y., & K. (Ram) Ramamurthy (2011). Understanding the link between information technology capability and organizational agility: An empirical examination. MIS quarterly, 931–954).

1.3 AI Proactive Stance

Relative to other open source projects, please evaluate your open source projects’ capability in acquiring, assimilating, transforming, and exploiting AI knowledge in the following areas on a 1–7 scale (1 = strongly disagree, 7 = strongly agree).

• AIPS1: We constantly keep current with new AI innovations.

• AIPS2: We are capable of and continue to experiment with new AI as necessary.

• AIPS3: We have a climate that is supportive of trying out new ways of using AI.

• AIPS4: We constantly seek new ways to enhance the effectiveness of AI use.

(Source: Lu, Y., & K. (Ram) Ramamurthy (2011). Understanding the link between information technology capability and organizational agility: An empirical examination. MIS quarterly, 931–954).

1.4 Affective Trust

Each of the statements below refers to how the participants in your open source project(s) feel about each other. Please indicate the extent to which you agree or disagree with each statement about the group using the following scale (1 = strongly disagree, 7 = strongly agree).

• AT1. Members of the team have made considerable emotional investments in our working relationships.

• AT2. Members of the team have a sharing relationship with each other. We can freely share our ideas, feelings, and hopes.

• AT3. On this team we can talk freely with each other about difficulties we are having and know that others will want to listen.

• AT4. Members of the team would feel a sense of loss we could no longer work together.

• AT5. If a member for this group shared problems with other members, they would respond constructively and caringly.

Source: Stewart, K. J., & Gosain, S. (2006). The impact of ideology on effectiveness in open source software development teams. Mis Quarterly, 291–314.

1.5 Cognitive Trust

Each of the statements below refers to how the participants in your open source project(s) feel about each other. Please indicate the extent to which you agree or disagree with each statement about the group using the following scale (1 = strongly disagree, 7 = strongly agree).

• CT1: Members of the team know that everyone on the team approaches their work with professionalism and dedication.

• CT2: Given the track records of the team members, we see no reason to doubt each other’s competence and preparation for a job.

• CT3: Members of the team believe they will be able to rely on other members of the team not to make a job more difficult by careless work.

• CT4: Members of the team are concerned with monitoring each other’s work*.

• CT5: Members of the team believe that other members should be trusted and respected as coworkers.

• CT6: Members of the team consider each other to be trustworthy.

Source: Stewart, K. J., & Gosain, S. (2006). The impact of ideology on effectiveness in open source software development teams. Mis Quarterly, 291–314.

1.6 OSS Team Performance

Each of the statements below refers to how well your open source project(s) are positioned in the following activities. Please indicate the extent to which you agree or disagree with each statement about the group using the following scale (1 = strongly disagree, 7 = strongly agree).

• TP1: Our open source team effectively used its resources.

• TP2: Our open source team was within the proposed budget.

• TP3: Our open source team was within the proposed time-schedule.

• TP4: Our open source team was able to meet its goals.

• TP5: Our open source team was able to respond quickly to problems.

Source: Kostopoulos et al. (2012): Structure and Function of Team Learning Emergence: A Multilevel Empirical Validation. Journal of Management, Vol. 39, No. 6, pp. 1430–1461.