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Gamification for Community Building: Click to Shake Hands
AI Processed Human Approved

What drives users to stay engaged with location-based games and build real-world communities?

Faisal Al Thani, Chadi Aoun, Savanid Vatanasakdakul
Gamification for Community Building: Click to Shake Hands
Pacific Asia Conference on Information Systems (PACIS) · 2025
Key findings
  • Formulates a novel theoretical framework connecting technology adoption, gamification mechanics, external social influences, and intrinsic motivations to app stickiness and community engagement.
  • Identifies key drivers such as location incentives, perceived ease of use, reward value, achievement, network influence, herd behavior, social motive, and sense of belonging.
  • Establishes an empirical research design targeting over 1,000 active Pokémon GO players using Partial Least Squares Structural Equation Modeling (PLS-SEM).
  • Provides preliminary theoretical grounding for designing mobile gamified systems that promote social sustainability and persistent physical-world social interactions.
What it means for you
  • CIO / IT Executive: On Monday morning, allocate budget to pilot a location-based gamified application in one of our company's facilities, specifically focusing on fostering cross-departmental collaboration by integrating 'location incentives' for shared project spaces.
  • IT Manager: On Monday morning, schedule a meeting with your development team to discuss how to integrate elements like 'reward value' and 'achievement' into our existing internal communication platform to encourage more active participation and knowledge sharing.
  • Business Strategist: On Monday morning, initiate a brainstorming session with your marketing and product teams to identify opportunities to leverage 'network influence' and 'herd behavior' in upcoming product launches or community outreach initiatives, perhaps by creating exclusive early-access groups.
  • Researcher: On Monday morning, refine the survey instrument to include questions specifically measuring 'sense of belonging' and 'social motive' as distinct constructs, and prepare to recruit participants from diverse community groups for your next study.
  • Policymaker: On Monday morning, direct your staff to research successful public policy initiatives that have used location-based incentives or gamification to encourage citizen engagement with local services or community events, and identify potential applications for our jurisdiction.
Community Engagement, Gamification, Location-based games, Stickiness, Technology Adoption, Social Interaction
Social Commerce in Qatar: Consumer Characteristics and Emergent Engagement
AI Processed Human Approved

Who is buying through social media in Qatar, which platforms do they use, and how often?

Savanid Vatanasakdakul, Chadi Aoun, Faiq Defiandry
Social Commerce in Qatar: Consumer Characteristics and Emergent Engagement
Americas Conference on Information Systems (AMCIS) · 2025
Key findings
  • Most social commerce users in Qatar engage in s-commerce purchases 1-3 times per month (82%), with an additional 18% purchasing 4-7 times monthly.
  • Instagram emerged as the leading platform for s-commerce in Qatar (46%), followed by WhatsApp (20%), TikTok (14%), and Facebook (9%).
  • Female consumers (56%) and younger demographics aged 20-24 (48%) constitute the largest user segments engaging in social commerce.
  • The findings highlight the significant impact of local culture, digital infrastructure, and localized content preferences in driving social commerce diffusion.
What it means for you
  • CIO / IT Executive: On Monday morning, initiate a strategic review of existing IT infrastructure and security protocols to ensure they can adequately support and protect the growing volume of social commerce transactions, with a specific focus on enhancing mobile payment gateway integrations and data privacy measures for platforms like Instagram and WhatsApp.
  • IT Manager: On Monday morning, task your team to audit the current analytics tracking for social media platforms, particularly Instagram and WhatsApp, to ensure accurate capture of user engagement and conversion data related to social commerce activities, and set up dashboards to monitor these metrics.
  • Business Strategist: On Monday morning, draft a proposal to develop targeted social commerce campaigns on Instagram, prioritizing visually appealing content and influencer collaborations, specifically aimed at the 20-24 female demographic, mirroring their current high engagement in this space.
  • Researcher: On Monday morning, begin outlining a follow-up research proposal focusing on qualitative insights into *why* Qatari consumers, particularly younger females, prefer Instagram for social commerce, exploring themes like trust, authenticity, and localized content.
  • Policymaker: On Monday morning, request a briefing from relevant ministries to understand current regulations regarding digital marketing and e-commerce on social media platforms in Qatar, and identify any gaps that might need to be addressed to foster responsible growth of social commerce.
Social Commerce, Social Media, Consumer Behavior, Technology Adoption, Qatar
Social mobile analytics and cloud computing: a big data analytical approach
AI Processed Human Approved

What are the main benefits, challenges, and technical solutions when combining social, mobile, analytics, and cloud technologies?

Farhad Ali, Habib Ullah Khan, Shah Nazir
Social mobile analytics and cloud computing: a big data analytical approach
2026
Key findings
  • Identified main collaborative entities within SMAC ecosystems, including smartphones, cloud platforms, crowd participants, and environmental sensors.
  • Summarized key organizational and societal benefits such as enhanced decision-making, operational cost reduction, improved productivity, and expanded social engagement.
  • Classified primary adoption challenges, focusing on data privacy, security vulnerabilities, scalability limits, energy consumption, and system interoperability.
  • Compiled existing technical solutions from literature, highlighting techniques such as attribute-based encryption, anonymization methods, and hyper-heuristic frameworks.
What it means for you
  • CIO / IT Executive: On Monday morning, initiate a cross-departmental working group focused on assessing our current SMAC infrastructure against the identified adoption challenges (privacy, security, scalability, energy, interoperability) using the research's findings as a framework, and schedule an initial meeting for next week.
  • IT Manager: On Monday morning, begin a review of our existing data anonymization protocols and encryption techniques, cross-referencing them with the 'existing technical solutions' highlighted in the research to identify immediate gaps or opportunities for enhancement within our current cloud and mobile analytics platforms.
  • Business Strategist: On Monday morning, schedule a 30-minute brainstorming session with your core team to identify one specific business process that could significantly benefit from SMAC integration, and during that session, identify the most critical adoption challenge from the research that needs to be addressed first for this specific process.
  • Researcher: On Monday morning, create a detailed spreadsheet to categorize the 75 papers reviewed in this study by the specific SMAC component (Social, Mobile, Analytics, Cloud), the primary adoption challenge addressed, and the technical solution proposed, to facilitate further meta-analysis.
  • Policymaker: On Monday morning, draft a request for a preliminary impact assessment on user privacy and cybersecurity risks associated with the widespread adoption of SMAC technologies within our jurisdiction, drawing upon the challenges identified in the research as key areas to investigate.
SMAC Framework, Big Data, Cloud Computing, Social Media Analytics, Mobile Computing, Data Security
An Empirical Investigation of Cloud Computing and Environmental Performance of Nations: Implications for Shared Responsibility in Cloud Computing
AI Processed Human Approved

How does cloud computing affect energy use and carbon emissions across host and user countries?

Jooho Kim, Jiyong Park, Jason Dedrick
An Empirical Investigation of Cloud Computing and Environmental Performance of Nations: Implications for Shared Responsibility in Cloud Computing
Journal of the Association for Information Systems · 2025
Key findings
  • Access to cloud services generally enhances environmental efficiency by reducing national energy and carbon intensities associated with IT services.
  • Countries hosting cloud data centers experience higher local energy and carbon intensities due to heavy physical infrastructure demands.
  • Neighboring countries benefit from green cloud IT services without incurring adverse environmental costs on their local IT capital.
  • A shared responsibility model based on multilayered technology architecture is proposed to divide environmental accountability between cloud providers and users.
What it means for you
  • CIO / IT Executive: On Monday morning, initiate a review of our current cloud infrastructure's geographical distribution and identify which of our cloud services are hosted in countries that are net importers of cloud services (i.e., benefiting from efficiency gains) versus countries that are net hosts of data centers. Then, begin drafting an internal memo to your team outlining the need to assess the environmental footprint of our chosen cloud providers, specifically focusing on the geographical location of their data centers.
  • IT Manager: On Monday morning, compile a list of all cloud services currently utilized by your department and identify the primary cloud provider(s) for each. Then, locate and review the public documentation of these providers regarding their data center locations and any publicly available environmental sustainability reports or initiatives.
  • Business Strategist: On Monday morning, begin by identifying which key business processes or customer-facing services are heavily reliant on cloud computing. Then, investigate if these services are currently being delivered from cloud infrastructure located in countries that are significant importers of cloud services or from countries that are major hosts of data centers, and flag this for further cost-benefit analysis that includes potential environmental externalities.
  • Researcher: On Monday morning, start by drafting an email to your colleagues or supervisor proposing a follow-up study that quantifies the specific cross-border environmental impacts for a subset of countries using the methodologies outlined in this paper. Include a brief outline of the data you would need to gather, such as national IT service import/export data and detailed data center location information for key cloud providers.
  • Policymaker: On Monday morning, draft a preliminary policy brief for your relevant committee or ministry that summarizes the key findings of this research regarding the uneven distribution of environmental burdens from cloud computing. Specifically, highlight the need for international dialogue on shared responsibility and propose an initial step to investigate incorporating environmental footprint data into public procurement policies for cloud services.
Cloud Computing, IT Capital, IT Services, Digital Responsibility, Shared Responsibility, Sustainability
Location Analytics in Information Systems: Opportunities for Research and Teaching
AI Processed Human Approved

How can location analytics be integrated into academic research and teaching to meet modern business needs?

Michael A. Erskine, James B. Pick, Asish Satpathy, Andrés Díaz López, Avijit Sarkar, Namchul Shin
Location Analytics in Information Systems: Opportunities for Research and Teaching
Communications of the Association for Information Systems · 2024
Key findings
  • Demonstrates how incorporating spatial methodologies (such as spatial autocorrelation and geographically weighted regression) can address complex societal 'wicked problems' like climate change, network disruption, and privacy.
  • Provides a strategic roadmap for integrating location analytics into undergraduate and graduate IS curricula to prepare students for high-demand business roles.
  • Outlines actionable steps to bridge academia and industry, including collaborative case study development, special journal issues, and enhanced faculty training in geospatial tools.
What it means for you
  • CIO / IT Executive: On Monday morning, schedule a 30-minute meeting with your Head of Data Science and Head of Business Intelligence to discuss a pilot project integrating a basic spatial analysis layer (e.g., visualizing customer locations on a map) into one of your existing dashboards for a specific business unit.
  • IT Manager: On Monday morning, identify one internal IT project or operational challenge (e.g., network outage analysis, data center resource allocation) that could potentially benefit from a spatial perspective, and research one readily available open-source geospatial tool (like QGIS) for initial exploration.
  • Business Strategist: On Monday morning, review your current customer segmentation data and identify 2-3 key business questions that might be better answered by understanding the geographical distribution of your customers (e.g., optimal new store locations, targeted marketing campaigns by region).
  • Researcher: On Monday morning, identify one of your current research projects and brainstorm 2-3 ways that incorporating spatial data or analysis (e.g., geographic clustering of events, spatial dependencies) could strengthen your theoretical contributions or add methodological rigor.
  • Policymaker: On Monday morning, contact a representative from a local urban planning or environmental agency to inquire about any existing spatial datasets related to community infrastructure, resource distribution, or environmental impact that might be publicly accessible.
Location Analytics, Spatial Analysis, IS Curriculum, Research Methodologies, Geographic Information Systems, Decision Support Systems
Securing Generative AI Systems: Threat-Centric Architectures and the Impact of Divergent EU–US Governance Regimes
AI Processed Human Approved

How can organizations effectively secure generative AI systems against unique threats under differing international regulations?

Vijay Kanabar and Kalinka Kaloyanova
Securing Generative AI Systems: Threat-Centric Architectures and the Impact of Divergent EU–US Governance Regimes
2026
Key findings
  • High-impact generative AI risks are structural properties inherent to architectural design rather than isolated, patchable bugs.
  • Effective risk mitigation requires a layered defense-in-depth model that combines traditional cybersecurity controls with AI-specific mechanisms such as context separation and tool sandboxing.
  • Transatlantic regulatory divergence establishes EU frameworks as the de facto technical baseline for multinational deployments, driving global architectural convergence.
What it means for you
  • CIO / IT Executive: On Monday morning, initiate a review of the current generative AI system architecture and identify which of the five layers (e.g., prompt engineering, model, data, tools, infrastructure) represent the highest structural risk based on the research's threat taxonomy. Prioritize a pilot project to implement context separation and tool sandboxing for a critical generative AI application.
  • IT Manager: On Monday morning, schedule a team meeting to map the specific adversarial techniques identified in the research (e.g., prompt injection, data poisoning, model evasion) to the current generative AI tools and integrations in use. Document which of these techniques pose the most significant structural risk to your deployed systems.
  • Business Strategist: On Monday morning, begin researching the EU's AI Act and its implications for generative AI deployments. Identify how the research's findings on divergent EU-US governance regimes suggest that adopting EU-like technical security baselines will be crucial for global market access and competitive advantage.
  • Researcher: On Monday morning, focus on replicating the research's methodology by attempting to map a specific novel adversarial technique against a widely used generative AI reference architecture, documenting any emergent failure modes observed and comparing them to the research's findings.
  • Policymaker: On Monday morning, analyze the comparative governance analysis presented in the research, specifically focusing on how the EU's framework is driving global architectural convergence. Draft a memo outlining potential legislative actions to align US generative AI security regulations with the EU's technical baseline to foster transatlantic interoperability and responsible innovation.
Generative AI Security, Cybersecurity Architecture, AI Governance, EU AI Act, NIST AI RMF, OWASP LLM Risks, MITRE ATLAS
STORYTELLING IS NOT JUST FOR CHILDREN: USING AN AI CHATBOT TO ENGAGE UNIVERSITY STUDENTS
AI Processed Human Approved

How can storytelling AI chatbots be used to engage students in learning about social engineering?

Mark Freeman
STORYTELLING IS NOT JUST FOR CHILDREN: USING AN AI CHATBOT TO ENGAGE UNIVERSITY STUDENTS
AIS SIGED International Conference on Information Systems Education and Research (SIGED) · 2025
Key findings
  • The study demonstrated that co-designing AI chatbot prompts with students created effective 'teachable moments' for understanding social engineering tactics and data governance.
  • Analysis of student interactions revealed diverse engagement approaches, ranging from friendly small talk to strategic IT troubleshooting and phishing tactics.
  • Key strategies were established for integrating a storytelling culture in higher education, including establishing a shared language, providing appropriate digital resources, and fostering collaborative environments.
  • Essential ethical guidelines were identified, emphasizing the necessity of robust data privacy, algorithmic bias mitigation, and content boundaries in educational AI applications.
What it means for you
  • CIO / IT Executive: On Monday morning, initiate a departmental meeting to discuss the potential integration of AI-powered scenario-based learning tools, like the one described in the research, to enhance student engagement in cybersecurity concepts and explore pilot program feasibility.
  • IT Manager: On Monday morning, schedule a brainstorming session with your team to identify specific social engineering concepts within your curriculum that could be effectively simulated using an interactive AI chatbot and begin researching available AI chatbot platforms or development frameworks.
  • Business Strategist: On Monday morning, analyze how the described AI chatbot's engagement model, which fosters 'teachable moments' through interactive storytelling, could be adapted to train employees on emerging cybersecurity threats or compliance protocols, identifying potential ROI.
  • Researcher: On Monday morning, review the ethical guidelines identified in the research (data privacy, bias mitigation, content boundaries) and begin designing a framework for evaluating the ethical implications of deploying AI-driven educational tools in your next study.
  • Policymaker: On Monday morning, draft a preliminary proposal for a grant or funding initiative that supports the development and implementation of innovative, AI-enhanced educational technologies in higher education, specifically mentioning the potential for improving cybersecurity education through interactive learning.
Storytelling, Artificial Intelligence Chatbot, Design Science Research, Scenario Based Learning, Social Engineering, Active Learning
Role of Information Systems in Empowering Innovation Networks
AI Processed Human Approved

How can leaders set up and manage shared technology tools to support collaborative innovation across partner companies?

Sven-Volker Rehm, Lakshmi Goel, Iris Junglas
Role of Information Systems in Empowering Innovation Networks
MIS Quarterly Executive · 2015
Key findings
  • Identifies five key IS functionalities critical for innovation networks: collaborative work environments, knowledge maps, knowledge spaces, innovation procedure toolkits, and social media platforms.
  • Reveals that network success stems from pragmatically using a diverse set of accessible IS tools to form a shared infrastructure rather than relying on a single complex system.
  • Delivers nine practical guidelines for CIOs to manage IS infrastructure across the co-shaping, co-performing, and co-opting phases of open innovation networks.
What it means for you
  • CIO / IT Executive: On Monday morning, initiate a review of our existing IS tools to identify at least three readily accessible platforms (e.g., cloud-based project management, secure file sharing, internal wiki) that can be quickly configured to support a collaborative work environment for our next cross-company project, prioritizing ease of adoption for all partners.
  • IT Manager: On Monday morning, assess the user-friendliness and integration capabilities of our current knowledge management system and identify any gaps. Then, plan a quick training session for a pilot group of users demonstrating how to tag and categorize project-related documents to build a rudimentary 'knowledge map' for our innovation network.
  • Business Strategist: On Monday morning, draft a brief outline of key innovation challenges and opportunities for our next external collaboration, and then identify which of the five identified IS functionalities (collaborative work environments, knowledge maps, knowledge spaces, innovation procedure toolkits, and social media platforms) would be most impactful in addressing these specific challenges, focusing on pragmatic tool selection.
  • Researcher: On Monday morning, begin cataloging and categorizing the publicly available 'innovation procedure toolkits' or best practice guides that could be shared or adapted for our current project, focusing on those with clear, step-by-step instructions that can be easily integrated into a shared digital space.
  • Policymaker: On Monday morning, draft a short proposal for an initiative that encourages or provides resources for SMEs to adopt cloud-based collaborative platforms, specifically highlighting how these platforms can support cross-organizational knowledge sharing and innovation within their respective industries.
Open Innovation, Innovation Networks, Information Systems Infrastructure, Small and Medium Enterprises, Collaborative Work Environments, Medical Device Industry
Using Information Systems in Innovation Networks: Uncovering Network Resources
AI Processed Human Approved

How can collaborating companies use shared information systems to align resources and develop new products?

Sven-Volker Rehm, Lakshmi Goel, Iris Junglas
Using Information Systems in Innovation Networks: Uncovering Network Resources
Journal of the Association for Information Systems · 2017
Key findings
  • Inter-organizational information systems are essential for enabling networking capabilities and constructing unique, shared resource constellations at the network level.
  • Four core informational purposes of information systems—representation, sharing, architecture, and reach—facilitate the cross-boundary resource alignment process.
  • Creating IS-embedded network resources enables collaborating firms to establish sustainable competitive advantages that extend beyond individual firm boundaries.
What it means for you
  • CIO / IT Executive: On Monday morning, schedule a meeting with key innovation network partners to initiate discussions on standardizing or enhancing inter-organizational information system capabilities for resource visibility and alignment. Identify one critical shared process in an ongoing innovation project and task your team with exploring how a new information system feature could better represent and share partner resources for that process.
  • IT Manager: On Monday morning, review the existing inter-organizational information systems used by your company in innovation networks. Identify one area where information sharing or representation of partner capabilities is currently fragmented or manual, and develop a concrete proposal for a pilot project to improve this using a specific IS feature (e.g., a shared dashboard for resource availability).
  • Business Strategist: On Monday morning, identify one ongoing collaborative innovation project and map out the key physical, human, and organizational resources currently being leveraged from your network partners. Then, identify specific gaps in visibility or alignment of these resources and brainstorm one practical way an inter-organizational information system could be adapted or enhanced to address these gaps.
  • Researcher: On Monday morning, begin drafting a qualitative interview guide for your next set of case studies, focusing on questions that explore how specific inter-organizational information system features (representation, sharing, architecture, reach) enable or hinder the alignment of physical, human, and organizational resources for new product development.
  • Policymaker: On Monday morning, research existing government grants or incentives that support the adoption and development of inter-organizational information systems for small and medium-sized enterprises involved in collaborative innovation. Draft a one-page summary of potential policy interventions to encourage such adoption for a discussion with relevant industry stakeholders.
Network Resource, Dynamic Capability, Networking Capability, Resource-based View, Innovation Network, New Product Development
Turning Tacit Knowledge into Dynamic Strategies Through Collective Inferencing
AI Processed Human Approved

How can managers use generative AI to turn expert insights into practical strategies for digital platforms?

Sven-Volker Rehm
Turning Tacit Knowledge into Dynamic Strategies Through Collective Inferencing
MIS Quarterly Executive · 2025
Key findings
  • Formulates a four-step Collective Inference Method involving differential analysis, landscape mapping, dynamic modeling, and scenario synthesis.
  • Demonstrates how generative AI can structure messy stakeholder narratives into functional modules while mitigating hallucination risks.
  • Validates the approach within the cplace platform ecosystem, resulting in 59 actionable management measures that effectively address governance, customization, and user empowerment.
What it means for you
  • CIO / IT Executive: On Monday morning, initiate a pilot project with your innovation team to test the 'differential analysis' step of the Collective Inference Method on a small, contained platform ecosystem. Gather 2-3 key internal stakeholders with diverse operational roles and feed their raw, unstructured operational challenges into a generative AI tool to identify initial tacit knowledge patterns.
  • IT Manager: On Monday morning, schedule a 30-minute informal discussion with two of your most experienced support technicians. Ask them to describe in detail a recent complex troubleshooting scenario where they had to 'go beyond the documentation' to resolve the issue. Record their responses, focusing on the 'why' and 'how' of their intuitive decisions.
  • Business Strategist: On Monday morning, identify one critical strategic decision your organization is facing that has significant ambiguity or conflicting stakeholder perspectives. Document the perceived goals and constraints of three different key stakeholder groups involved in this decision in their own words, without trying to structure them yet.
  • Researcher: On Monday morning, select one publicly available case study of a failed platform strategy and manually extract all unstructured stakeholder feedback, forum discussions, or public comments related to its challenges. Categorize these snippets into broad areas of operational friction or unmet needs.
  • Policymaker: On Monday morning, identify a specific public service delivery challenge where fragmented stakeholder input leads to inefficient or contradictory policy outcomes. Gather and print out the last 5 public consultation submissions from different community groups on this issue, without any initial analysis.
Collective Inferencing, Tacit Knowledge, Generative AI, Platform Governance, Ecosystem Management, Dynamic Modeling
Location Analytics in Information Systems: Opportunities for Research and Teaching
AI Processed Human Approved

How can business schools integrate location analytics into their curriculum and research to meet industry demand?

Michael A. Erskine, James B. Pick, Asish Satpathy, Andrés Díaz López, Avijit Sarkar, and Namchul Shin
Location Analytics in Information Systems: Opportunities for Research and Teaching
Communications of the Association for Information Systems · 2024
Key findings
  • Highlights key research opportunities where location analytics can address complex, multidisciplinary problems such as climate change, network disruptions, and location privacy.
  • Proposes a stepwise framework for business schools to integrate location analytics into IS courses, concentrations, and interdisciplinary programs.
  • Recommends action steps to strengthen industry-academic partnerships, train faculty, and promote spatial methodologies in leading IS journals.
What it means for you
  • CIO / IT Executive: On Monday morning, task your IT strategy team to identify one current business challenge (e.g., supply chain optimization, customer segmentation) that could be significantly improved by incorporating location data, and schedule a brief exploratory meeting to discuss potential pilot projects using spatial analysis tools.
  • IT Manager: On Monday morning, identify one existing data set within your organization that has inherent spatial components (e.g., customer addresses, store locations, sensor readings) and initiate a conversation with a data analyst about how to visualize and query this data using basic geospatial techniques.
  • Business Strategist: On Monday morning, review your current strategic plan and identify two key business objectives where understanding 'where' things happen or are located could provide a competitive advantage or unlock new opportunities, then brainstorm potential data sources for that 'where'.
  • Researcher: On Monday morning, identify one of your ongoing or future research projects that currently lacks a spatial dimension and draft a brief proposal outline for how integrating location analytics could address a key limitation or open new avenues of inquiry, referencing specific spatial methods mentioned in the literature.
  • Policymaker: On Monday morning, reach out to a representative from a local university's Information Systems or Geography department to inquire about potential collaborations or pilot programs for integrating location analytics into business curricula, focusing on areas relevant to local economic development or public services.
Location Analytics, Spatial Analysis, IS Curriculum, Research Methodologies, Geographic Information Systems, Spatial Business
Artificial Normality: How Conversational Agents’ Perceived Humanness Inhibits Error Attribution and Preserves Satisfaction
AI Processed Human Approved

How does making conversational agents appear more human affect customer reactions when errors occur?

Alfred Benedikt Brendel, Sascha Lichtenberg, Fabian Hildebrandt & Alan R. Dennis
Artificial Normality: How Conversational Agents’ Perceived Humanness Inhibits Error Attribution and Preserves Satisfaction
Journal of Management Information Systems · 2026
Key findings
  • Greater perceived humanness in conversational agents preserves perceptions of situational normality when minor errors occur.
  • Preserved situational normality reduces users' tendency to attribute error cause to the agent, thereby safeguarding overall service satisfaction.
  • Errors made by humanlike conversational agents are processed similarly to errors made by human service agents, demonstrating that humanlike design can alleviate algorithm aversion.
What it means for you
  • CIO / IT Executive: On Monday morning, allocate budget to pilot the integration of more human-centric design principles (e.g., nuanced language, empathetic phrasing) into our core customer-facing conversational agent, focusing on areas where process errors are most common, to preemptively reduce user error attribution and maintain satisfaction.
  • IT Manager: On Monday morning, task your development team to review our current conversational agent's dialogue logs for instances of process errors and identify the top 3 most frequent error types to begin redesigning their responses with more humanlike cues (e.g., slight pauses, conversational fillers) to normalize the situation for users.
  • Business Strategist: On Monday morning, initiate a cross-functional workshop with marketing and product development to explore how to strategically position our conversational agents' perceived humanness in customer communications, emphasizing its role in creating a seamless experience even when minor technical glitches occur.
  • Researcher: On Monday morning, begin designing a follow-up study that experimentally manipulates specific humanlike conversational features (e.g., varying levels of politeness, acknowledgment of difficulty) within a simulated error scenario to quantify their direct impact on error attribution and situational normality, building upon the findings of Artificial Normality.
  • Policymaker: On Monday morning, convene a small working group to draft guidelines for the ethical development of conversational agents, recommending that AI design standards encourage the incorporation of humanlike qualities to enhance user experience and mitigate negative reactions to inevitable system errors, rather than solely focusing on pure efficiency.
Chatbot, Chatbot errors, situational normality, anthropomorphism, humanness, error attribution, user satisfaction
On the Potential of Business Process Management for Digital Entrepreneurship: Findings from a Literature Review
AI Processed Human Approved

How can growing digital startups balance structured business processes with the flexibility needed to scale?

Tobias Wuttke, Thomas Haskamp, Michael Perscheid, Falk Uebernickel
On the Potential of Business Process Management for Digital Entrepreneurship: Findings from a Literature Review
Forty-Fourth International Conference on Information Systems · 2023
Key findings
  • Reconciles BPM and digital entrepreneurship literature by highlighting synergies such as process modularity, agility, and customer orientation.
  • Identifies key tensions between traditional process structures and the dynamic operational flexibility required by digital ventures.
  • Proposes three main research avenues: applying ambidextrous BPM in digital ventures, treating digital venturing as a business process, and developing capabilities to balance flexibility and structure.
What it means for you
  • CIO / IT Executive: On Monday morning, initiate a review of our current core digital processes (e.g., customer onboarding, product deployment) to identify specific components that could be modularized to allow for independent updates and scaling without disrupting the entire workflow.
  • IT Manager: On Monday morning, schedule a 30-minute meeting with a key team member from sales or customer support to identify one friction point in their daily workflow that could be addressed by a small, agile process improvement, rather than a large, monolithic system change.
  • Business Strategist: On Monday morning, dedicate an hour to mapping out the critical decision-making points in our growth strategy and outline potential process 'switches' or 'modes' that can be activated or deactivated based on market feedback or scaling needs, embodying ambidextrous BPM.
  • Researcher: On Monday morning, begin by identifying three specific digital venture growth challenges (e.g., rapid customer acquisition, international expansion) and conceptualize how treating the *process of venturing itself* as a business process (i.e., defining, measuring, and iteratively improving the venture creation and growth cycle) could be studied.
  • Policymaker: On Monday morning, draft a brief (one-page) outline of potential policy levers (e.g., startup support programs, digital skills training) that could foster the development of 'capabilities to balance flexibility and structure' within digital entrepreneurial ecosystems, focusing on practical implementation.
business process management, digital entrepreneurship, digital venturing, ambidextrous BPM, process innovation, literature review
Building the Processes Behind the Product: How Digital Ventures Create Business Processes That Support Their Growth
AI Processed Human Approved

How do fast-growing digital ventures build and adapt their internal business processes to support growth?

Tobias Wuttke, Thomas Haskamp, Michael Perscheid, Falk Uebernickel
Building the Processes Behind the Product: How Digital Ventures Create Business Processes That Support Their Growth
Business & Information Systems Engineering · 2024
Key findings
  • Identified four distinct patterns of business process change in digital ventures: creating minimum viable processes, creating encapsulated processes, creating centralized control flow integrations, and creating centralized data flow integrations.
  • Revealed that digital ventures typically start with flexible, human-orchestrated processes supported by low-specificity tools before migrating to specialized enterprise systems to boost efficiency.
  • Showed how proxy communication tools and centralized analytics systems are used to reduce system complexity and eliminate data silos in maturing ventures.
What it means for you
  • CIO / IT Executive: On Monday morning, initiate a review of our current proxy communication tool usage and explore options for centralizing our analytics systems to proactively address potential data silos as we scale.
  • IT Manager: On Monday morning, identify one key operational process (e.g., sales lead qualification) that is currently human-orchestrated and explore if a 'minimum viable process' could be documented and supported by a low-specificity tool for immediate iteration.
  • Business Strategist: On Monday morning, map out the current flow of data for our top 2 growth-driving products and identify one area where centralized data flow integration could significantly reduce friction and improve speed to market.
  • Researcher: On Monday morning, begin compiling a list of digital ventures that have recently experienced rapid growth and categorize their initial process creation patterns based on the four distinct patterns identified in the research (MVP, encapsulated, centralized control, centralized data).
  • Policymaker: On Monday morning, task a working group to investigate how current regulatory frameworks might be hindering the adoption of flexible, technology-enabled business processes in rapidly scaling digital ventures.
Business process management, Digital entrepreneurship, Digital ventures, Business process change, Enterprise systems
What Does ChatGPT Know About Information Systems?
AI Processed Human Approved

How well does ChatGPT perform on information systems course questions and professional certification exams?

Daniel E. O’Leary, Veda C. Storey, Aaron M. French, Joseph R. Buckman, Cecil Chua, Andrew W. Green, Grace Gu, Fred Niederman, Francis Pereira, Gary Templeton, Linda Wallace
What Does ChatGPT Know About Information Systems?
Communications of the Association for Information Systems · 2025
Key findings
  • ChatGPT answered 65% to 85% of Information Systems queries correctly across all core framework areas, performing roughly at the level of an average student.
  • The model performed significantly better on university course exams than on professional certification exams, failing to reach passing scores for the PMP and Microsoft Azure Administrator tests.
  • ChatGPT achieved higher relative accuracy against class averages on closed-book exams compared to open-book or online exam environments where students had external resource access.
  • While ChatGPT displayed broad coverage across diverse IS topics, its responses favored listing structured information over demonstrating expert-level critical thinking or complex problem-solving.
What it means for you
  • CIO / IT Executive: On Monday morning, task your IT training lead to pilot a small internal workshop for a select group of IT professionals focusing on the PMP and Microsoft Azure Administrator certifications, using ChatGPT as a supplementary learning tool to identify specific knowledge gaps and assess its effectiveness in preparing for these professional exams.
  • IT Manager: On Monday morning, instruct your team members responsible for generating internal documentation or knowledge base articles to compare the accuracy and completeness of ChatGPT's initial drafts on at least two core Information Systems topics (e.g., database management, network security) against your existing, verified internal resources.
  • Business Strategist: On Monday morning, task a market analyst to explore potential use cases for ChatGPT in rapidly generating initial drafts of business process documentation or competitive analysis reports within your IS domain, noting its tendency to provide structured information over deep critical thinking.
  • Researcher: On Monday morning, design a controlled experiment to compare ChatGPT's performance on retrieving specific, factual information about IS concepts in a closed-book scenario versus an open-book scenario using publicly available academic papers, to validate the finding about its performance in different exam environments.
  • Policymaker: On Monday morning, request a briefing from your education and technology policy advisors on the implications of AI tools like ChatGPT achieving 'average student' performance in IS education, and explore potential guidelines for incorporating such tools responsibly in curriculum development and assessment.
ChatGPT, Large Language Models, Generative AI, Information Systems Competency, Artificial Intelligence in Education, Knowledge Assessment
GENAI AS A ROMANTIC RELATIONSHIP ADVISOR: EXPLORING HUMAN–AI COLLABORATION IN EMOTIONAL SUPPORT CONTEXTS
AI Processed Human Approved

What are the benefits and risks of using generative artificial intelligence for romantic relationship advice?

Anna Schätzle, Peter Buxmann
GENAI AS A ROMANTIC RELATIONSHIP ADVISOR: EXPLORING HUMAN–AI COLLABORATION IN EMOTIONAL SUPPORT CONTEXTS
ECIS 2026 · 2026
Key findings
  • Qualitative evidence supported that GenAI helps users self-clarify emotions, organize thoughts, and prepare communication strategies prior to interpersonal discussions.
  • Qualitative evidence supported that GenAI use entails risks of automation and confirmation bias, as the system tends to validate the user's one-sided, potentially biased framing of relationship conflicts.
  • Qualitative evidence supported that users often attribute unintended authority to GenAI, potentially misinterpreting its structured, polished responses as clinical diagnoses.
  • Qualitative analysis supported that GenAI is a useful complementary or preparatory tool but cannot replace professional therapy because it lacks emotional intuition, nonverbal communication perception, and dyadic mediation capabilities.
  • No statistical hypotheses were tested due to the study's exploratory qualitative nature; findings are limited by a small sample size of 18 interviewees, retrospective self-reports, a German-speaking context, and homogeneous use of ChatGPT.
What it means for you
  • CIO / IT Executive: Configure the enterprise GenAI gateway to detect personal counseling or emotional-support-seeking queries and automatically inject a system-level disclaimer redirecting employees to the company's official Employee Assistance Program (EAP) to prevent reliance on GenAI as an authoritative clinical resource.
  • IT Manager: Create and distribute a 'Balanced Prompting' template to the team, instructing them to explicitly prompt GenAI to 'act as a devil's advocate and challenge my assumptions' when using it to prepare for difficult workplace or interpersonal conversations, directly mitigating the tool's confirmation bias.
  • Business Strategist: Review the product roadmap for our wellness and conversational AI features to pivot the UX away from direct 'advice-giving' toward 'communication preparation and self-reflection' tools, integrating mandatory disclaimers that clarify the AI has no clinical diagnostic capabilities.
  • Researcher: Draft a research proposal for a pre-registered quantitative study ($N > 300$) comparing how different LLM models (e.g., ChatGPT, Claude, Gemini) handle relationship conflict prompts, specifically measuring the rate of user agreement (automation bias) versus the model's level of validation.
  • Policymaker: Draft a regulatory policy mandate requiring developers of consumer-facing GenAI tools to implement prominent 'non-clinical tool' labels and active-consent pop-ups warning users of the risks of automation bias and the lack of professional diagnostic authority in emotionally sensitive domains.
Generative Artificial Intelligence, Relationship, Counseling, Therapy, Human-AI Interaction, HAII-TIME
MANAGING ARTIFICIAL INTELLIGENCE IN PUBLIC SECTOR ORGANIZATIONS: A DYNAMIC CAPABILITIES PERSPECTIVE
AI Processed Human Approved

How can public sector organizations effectively adopt and manage artificial intelligence?

Isabella Urban, Manfred Baer, Ralf Plattfaut
MANAGING ARTIFICIAL INTELLIGENCE IN PUBLIC SECTOR ORGANIZATIONS: A DYNAMIC CAPABILITIES PERSPECTIVE
ECIS 2026 · 2026
Key findings
  • The study empirically supports that AI adoption in public organizations is hindered by high levels of technological, regulatory, and organizational uncertainties, which require proactive management.
  • The research empirically identifies and supports a framework of specific organizational microfoundations across three key dimensions: sensing (e.g., tracing employee expectations and monitoring regulatory landscapes), seizing (e.g., establishing AI governance and launching pilot projects), and transforming (e.g., active change management and promoting AI literacy).
  • The findings empirically show that employee representation bodies (such as staff councils) often play a critical, gatekeeping role that can delay or hinder AI implementation decisions.
  • The study acknowledges limitations, including its geographical focus on the German public sector and a limited observation timeframe from April 2024 to October 2025.
What it means for you
  • CIO / IT Executive: Send an email calendar invite to the chair of your organization's Employee Representation Body (Staff Council) to establish a joint, bi-weekly 'AI Governance and Alignment Committee' to co-create deployment guidelines and prevent approval bottlenecks.
  • IT Manager: Draft a 1-page project charter for a highly constrained, 4-week pilot project using a low-risk internal AI tool (e.g., automated document summarizing) to test technical capability and establish baseline performance metrics.
  • Business Strategist: Design and launch a 5-question MS Forms survey to all department heads to map their teams' current AI literacy levels and identify specific anxieties, using the results to structure next month's targeted change management plan.
  • Researcher: Draft a research proposal and reach out to two international public sector research partners to replicate this qualitative dynamic capabilities study in a non-German jurisdiction (e.g., the US or UK) to address the geographical limitations of the original study.
  • Policymaker: Create and distribute a standardized 'Public Sector AI Regulatory Compliance Checklist' to provide local agency leaders with clear, step-by-step guidance on data privacy and municipal regulations, directly reducing their regulatory uncertainty.
Artificial Intelligence, Dynamic Capabilities, Microfoundations, Multiple Case Study, Public Sector
TIME TO TURN OFF THE LIGHTS: A SUCCESS FACTOR FRAMEWORK FOR ADVANCING HYPERAUTOMATION IN ORGANIZATIONS
AI Processed Human Approved

What are the key factors needed to successfully scale automation across an organization?

Lisa Straub, Christian Zeiß, Ulvi Ibrahimli, Christoph Tomitza, Benedikt Wirsing, Axel Winkelmann
TIME TO TURN OFF THE LIGHTS: A SUCCESS FACTOR FRAMEWORK FOR ADVANCING HYPERAUTOMATION IN ORGANIZATIONS
ECIS 2026 · 2026
Key findings
  • The survey results statistically supported all 15 success factors within the framework, achieving a high average agreement score of 6.05 out of 7, with 93.2% of respondents selecting positive agreement.
  • 'Leadership Commitment' (SF3) and 'Communication and Trust Building' (SF4) received the highest levels of overall agreement, indicating strong statistical support.
  • 'Strategic Partner Collaboration' (SF15) and 'Customer Integration' (SF13) received statistically supported relevance, although they had comparatively lower overall agreement scores.
  • The study's generalizability is limited by the German context of the initial qualitative interviews, a modest quantitative survey sample size (n = 103), and its reliance on subjective assessments rather than objective performance metrics.
What it means for you
  • CIO / IT Executive: On Monday morning, schedule a 30-minute alignment meeting with business unit heads to establish a joint 'Hyperautomation Governance Board' to audit all active shadow-IT automation tools (like RPA and low-code apps) and map them against your long-term IT integration roadmap to prevent fragmented technical debt.
  • IT Manager: On Monday morning, block out two hours to audit your current automation deployment backlog and draft a standardized 'API-First Integration Checklist' that your development team must use to ensure new automations do not bypass APIs and create system incompatibilities.
  • Business Strategist: On Monday morning, select one high-volume customer-facing workflow and schedule a scoping call with a key strategic partner to co-design an integrated, automated data-sharing process, directly applying the 'Customer Integration' (SF13) and 'Strategic Partner Collaboration' (SF15) success factors.
  • Researcher: On Monday morning, draft a research proposal and outreach email to target three non-German, US-based enterprises to secure pilot sites for a comparative case study that measures objective operational KPIs (such as transaction error rates and processing speed) to validate the subjective findings of the 15-factor framework.
  • Policymaker: On Monday morning, draft an amendment to your organization's IT Procurement Policy mandating that any new business unit purchasing low-code or RPA tools must submit a 'Systemic Compatibility and Trust Impact Assessment' addressing how the tool aligns with the enterprise IT architecture and employee trust-building guidelines.
Hyperautomation, Transformation Management, Organizational Change, Success Factor Framework, Affordance Theory
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