Multidisciplinary Engineering Science Open

This qualitative study aimed to explore how engineers, data scientists, and environmental specialists collaborate in the development of sustainable technologies in Brazilian organizational and project contexts. A qualitative descriptive design was used to examine professional experiences of interdisciplinary collaboration in sustainable technology development. Data were collected through semi-structured interviews with 27 participants from Brazil, including engineers, data scientists, and environmental specialists involved in renewable energy, circular economy, water management, environmental monitoring, smart-city, and climate-analytics projects. Participants were selected purposively to ensure disciplinary diversity and direct experience in collaborative sustainability-oriented technology projects. Interviews were conducted online and face-to-face, lasted between 45 and 75 minutes, and continued until theoretical saturation was reached at the twenty-fourth interview, followed by three confirmatory interviews. Audio recordings were transcribed verbatim and analyzed using thematic analysis supported by NVivo software. Five main categories were identified: shared sustainability problem framing, translation across disciplinary languages, data-mediated integration, negotiation of technical-environmental trade-offs, and organizational conditions for collaborative innovation. Participants described collaboration as most effective when teams moved beyond sequential expert contribution and developed shared problem definitions, common indicators, and iterative feedback loops. Data dashboards, environmental impact models, prototypes, and regulatory matrices functioned as boundary objects that helped professionals coordinate without eliminating disciplinary differences. However, collaboration was constrained by incompatible timelines, different evidentiary standards, fragmented data ownership, and tensions between engineering feasibility, algorithmic performance, environmental integrity, and commercial pressure. Sustainable technology development depends not only on advanced technical expertise but also on structured interdisciplinary collaboration. The findings suggest that engineers, data scientists, and environmental specialists need shared evaluative frameworks, translational routines, participatory data governance, and organizational support to integrate environmental knowledge into technology design. Strengthening these collaborative capabilities can improve the relevance, legitimacy, and sustainability performance of emerging technologies.

This study aimed to explore how professionals involved in large-scale engineering projects understand, organize, and sustain stakeholder engagement across complex project environments in Hungary. A qualitative research design was adopted to obtain an in-depth understanding of stakeholder engagement practices from the perspectives of professionals directly involved in large-scale engineering projects. Data were collected through semi-structured interviews with 24 participants from Hungary, including project managers, engineering leads, procurement specialists, public-sector representatives, environmental and social impact experts, and community liaison officers. Participants were selected through purposive sampling to ensure variation in role, sector, and professional experience. Interviews continued until theoretical saturation was reached, with no substantially new codes emerging after the twenty-first interview and three additional interviews conducted to confirm saturation. Interviews were audio-recorded with consent, transcribed verbatim, anonymized, and analyzed using thematic analysis. NVivo software was used to organize transcripts, support systematic coding, compare categories, and develop higher-order themes. The analysis generated four main categories: dynamic stakeholder identification and prioritization, communication as a mechanism of alignment, trust and legitimacy in contested project environments, and institutional-contractual constraints on engagement. Participants described stakeholder engagement as a continuous and adaptive process rather than a fixed procedural requirement. Effective engagement depended on early mapping of formal and informal actors, transparent communication, responsiveness to local concerns, and the ability to negotiate competing expectations among government bodies, contractors, communities, consultants, and end users. However, fragmented governance, political pressure, rigid contractual arrangements, and delayed communication often reduced the quality of engagement. Stakeholder engagement in large-scale engineering projects is best understood as a relational, political, and iterative process embedded in technical, institutional, and social complexity. The findings suggest that successful engagement requires more than compliance-based consultation; it requires continuous stakeholder intelligence, credible communication, trust-building, and flexible governance mechanisms that allow project teams to respond to emerging concerns throughout the project life cycle.

This study aimed to explore how knowledge is shared, interpreted, and transformed among members of multidisciplinary engineering research teams in Muscat, Oman. A qualitative exploratory design was employed to examine the lived and practice-based experiences of researchers, engineers, project coordinators, and technical specialists working in multidisciplinary engineering research teams. Twenty-five participants were recruited through purposive sampling from university-based laboratories, research centers, and industry-linked engineering projects in Muscat. Data were collected exclusively through semi-structured interviews lasting 45–75 minutes. Interviews focused on participants’ experiences of sharing technical knowledge, coordinating expertise, managing disciplinary differences, using digital platforms, and overcoming barriers to collaboration. Sampling continued until theoretical saturation was reached after 22 interviews, followed by three confirmatory interviews. All interviews were audio-recorded with consent, transcribed verbatim, anonymized, and analyzed using thematic analysis supported by NVivo software. The analysis generated five main categories: boundary translation and shared technical language, relational trust and psychological safety, digital and informal knowledge infrastructures, leadership-enabled expertise coordination, and tensions created by time pressure, ownership, and institutional silos. Participants described knowledge sharing as more than information exchange; it involved translating assumptions, negotiating meanings, clarifying dependencies, and developing shared problem representations. Effective knowledge sharing occurred when teams created psychologically safe spaces, used visual and digital artifacts, recognized distributed expertise, and established routines for cross-disciplinary sensemaking. Barriers included disciplinary jargon, publication competition, fragmented documentation, uneven participation, and insufficient time for reflective integration. Knowledge sharing in multidisciplinary engineering research teams is a socially embedded, technically mediated, and leadership-dependent process. The findings suggest that successful collaboration requires shared language, trust, documentation discipline, boundary-spanning leadership, and institutional incentives that reward collective knowledge creation. Strengthening these conditions can improve research integration, innovation quality, and team learning in engineering research environments.

This study aimed to explore how artificial intelligence is being integrated into multidisciplinary engineering workflows and how engineers perceive its effects on collaboration, design decision-making, professional roles, and organizational readiness. A qualitative descriptive design was employed using semi-structured interviews with 24 engineering professionals working in multidisciplinary engineering organizations in Muscat, Oman. Participants included civil, mechanical, electrical, software, systems, and project engineers, as well as engineering managers with direct experience of AI-supported tools in design, simulation, project coordination, documentation, quality assurance, or decision support. Participants were recruited purposively to capture variation in engineering discipline, professional experience, and organizational role. Data collection continued until theoretical saturation was reached. Interviews were audio-recorded, transcribed verbatim, anonymized, and analyzed using thematic analysis. NVivo software was used to organize transcripts, code interview data, compare patterns across disciplinary groups, and develop final themes. Four main categories emerged from the analysis: AI as a boundary-spanning coordination mechanism, AI-enabled acceleration of design exploration and technical decision-making, human oversight and trust in AI-generated outputs, and organizational readiness for sustainable AI integration. Participants described AI as useful for connecting fragmented disciplinary knowledge, reducing repetitive documentation work, supporting early-stage design alternatives, improving simulation preparation, and facilitating cross-functional communication. However, they also emphasized concerns regarding data quality, model explainability, accountability for engineering decisions, software interoperability, and uneven AI literacy across teams. The integration of AI into multidisciplinary engineering workflows is not merely a technical transition but a socio-technical transformation that reshapes collaboration, professional judgment, and organizational learning. Sustainable AI adoption requires high-quality engineering data, transparent governance, domain-specific validation, continuous professional training, and human-centered workflow redesign.

This study aimed to explore the major barriers that hinder the implementation of sustainable engineering solutions in multidisciplinary project contexts from the perspectives of engineering professionals, project managers, sustainability consultants, and public-sector technical experts in Tehran. A qualitative descriptive design was adopted. Data were collected through semi-structured interviews with 24 participants selected through purposive maximum-variation sampling from engineering consulting firms, construction and infrastructure companies, environmental advisory units, energy-related organizations, and municipal technical bodies in Tehran. Participants had direct experience in designing, managing, evaluating, or implementing sustainable engineering solutions across multidisciplinary projects. Interviews continued until theoretical saturation was reached, which occurred after the twenty-first interview; three additional interviews were conducted to confirm conceptual adequacy. Interviews were audio-recorded with permission, transcribed verbatim, anonymized, and analyzed using thematic analysis. NVivo software was used to organize transcripts, develop initial codes, compare patterns across participant groups, and construct final categories. Analysis generated five main categories of implementation barriers: fragmented disciplinary logics and weak cross-functional communication; economic and procurement constraints; regulatory ambiguity and institutional inertia; limited sustainability knowledge, data, and lifecycle assessment capacity; and stakeholder misalignment, risk perception, and weak accountability. Participants emphasized that sustainable engineering solutions often fail not because of technical impossibility, but because engineering, financial, managerial, legal, and social actors evaluate project success through different criteria. High initial costs, short-term budgeting, uncertain incentives, fragmented approval procedures, limited access to reliable lifecycle data, and insufficient client demand weakened implementation. Sustainable engineering implementation in multidisciplinary contexts requires more than technical design competence. It depends on integrated governance, lifecycle-based evaluation, interdisciplinary communication, supportive regulations, financial incentives, stakeholder engagement, and organizational accountability. The findings suggest that sustainable engineering should be embedded into project planning, procurement, education, and performance evaluation systems rather than treated as an optional design feature.

This study aimed to explore how innovation is managed, coordinated, and sustained in multidisciplinary engineering organizations operating in Tehran. This qualitative study was conducted using a conventional thematic analysis approach. Data were collected through semi-structured interviews with 24 participants working in multidisciplinary engineering organizations in Tehran, including senior engineering managers, R&D specialists, project managers, systems engineers, design engineers, and technical consultants. Participants were selected through purposive sampling based on direct experience with innovation projects, interdisciplinary collaboration, product or process development, and engineering project implementation. Interviews continued until theoretical saturation was achieved. Each interview lasted between 45 and 75 minutes and was audio-recorded with informed consent. The interviews were transcribed verbatim and analyzed using Braun and Clarke’s six-phase thematic analysis procedure. NVivo software was used to support data organization, coding, category development, retrieval of coded segments, and comparison of themes across participant groups. Trustworthiness was enhanced through member checking, peer debriefing, audit trail documentation, and researcher reflexivity. Data analysis generated five main categories: strategic framing of innovation, cross-disciplinary knowledge integration, structural and procedural support for innovation, leadership and cultural enablers, and barriers to implementation and commercialization. Participants described innovation as a process shaped by both technical creativity and organizational coordination. Effective innovation management required alignment between engineering expertise, market needs, resource allocation, managerial support, and interdepartmental communication. While multidisciplinary teams expanded the range of ideas and technical solutions, they also created challenges related to language differences, professional boundaries, slow decision-making, and conflict over priorities. Innovation management in multidisciplinary engineering organizations is not limited to idea generation or technical problem-solving; rather, it depends on the organization’s ability to integrate specialized knowledge, create flexible structures, support experimentation, and translate technical concepts into implementable outcomes. The findings suggest that engineering organizations should strengthen boundary-spanning roles, collaborative routines, innovation governance, and leadership practices that connect technical excellence with strategic and commercial value.

This study aimed to explore engineers’ perspectives on the ethical challenges encountered in the design, development, and implementation of emerging multidisciplinary technologies. This qualitative study was conducted using a conventional thematic analysis approach. Participants were 24 engineers working in technology-driven, research-based, and multidisciplinary organizations in Tehran, Iran. Participants were selected through purposive sampling to include engineers with direct experience in emerging technologies such as artificial intelligence, robotics, biomedical engineering, nanotechnology, smart infrastructure, renewable energy systems, and data-driven industrial systems. Data were collected through semi-structured interviews lasting 45–75 minutes. Interviews continued until theoretical saturation was reached after the twenty-first interview, followed by three additional interviews to confirm saturation. All interviews were audio-recorded with informed consent, transcribed verbatim, and analyzed using NVivo software. Coding proceeded through open coding, category development, constant comparison, and theme refinement. Credibility was strengthened through member checking, peer review, and maintaining an audit trail. Thematic analysis generated five main categories: ethical uncertainty and anticipatory responsibility; distributed accountability in multidisciplinary collaboration; privacy, data justice, and algorithmic opacity; tension between public welfare and commercial or institutional pressure; and the need for embedded ethical governance. Participants emphasized that ethical challenges often emerged before technical consequences were fully visible and were intensified by fragmented responsibility across disciplines. Engineers also reported difficulty translating abstract ethical principles into practical design decisions under time, budget, and market pressures. The findings indicate that ethical engineering in emerging multidisciplinary technologies requires more than individual professional morality. It demands anticipatory thinking, interdisciplinary accountability, transparent design processes, stakeholder inclusion, and institutional mechanisms that integrate ethics into the full technology lifecycle. Strengthening ethical governance can help engineers address uncertainty, reduce harm, and support socially responsible innovation.

This study aimed to explore how engineers working in multidisciplinary organizational environments construct, negotiate, and sustain their professional identity through daily interactions with diverse occupational groups. A qualitative research design was used to examine engineers’ lived and interpreted experiences of professional identity formation. The participants were 24 engineers working in multidisciplinary project environments in Tehran, Iran. Participants were selected through purposive sampling based on their direct involvement in projects requiring collaboration with professionals from different disciplinary backgrounds, including management, architecture, finance, information technology, environmental science, and regulatory affairs. Data were collected through semi-structured interviews lasting 45 to 75 minutes. Interviews continued until theoretical saturation was achieved. All interviews were audio-recorded with participants’ consent, transcribed verbatim, and analyzed using thematic analysis. NVivo software was used to organize transcripts, assign codes, compare categories, and develop final themes. The analysis generated five main categories: negotiating technical expertise within collaborative work, translating engineering knowledge across disciplinary boundaries, seeking recognition and legitimacy in multidisciplinary teams, integrating ethical and social responsibility into engineering identity, and developing a hybrid professional self. Participants described professional identity formation as a dynamic process shaped not only by technical competence but also by communication, boundary work, organizational recognition, and the ability to align engineering judgment with broader social, economic, and environmental expectations. The findings suggest that engineers in multidisciplinary settings form their professional identities through continuous negotiation between disciplinary expertise and collaborative adaptability. Professional identity was strengthened when engineers experienced recognition, meaningful participation, ethical agency, and opportunities to translate technical knowledge for non-engineering stakeholders. Organizations should support identity development by creating inclusive project cultures, clarifying professional roles, and valuing both technical and relational dimensions of engineering practice.