ABSTRACT Entrepreneurship in emerging economies is shaped by persistent institutional voids, resource scarcity, and volatile performance feedback. This study examines how entrepreneurs learn under such constraints by mobilizing two interrelated mechanisms: problemistic search and bricolage. Drawing on multiple qualitative case studies of first‐generation entrepreneurs in India, we trace how performance shortfalls trigger problemistic search that expands entrepreneurs' cognitive and solution spaces, while bricolage enables the recombination of available resources into workable practices. Our findings show that learning does not occur through either mechanism in isolation; rather, entrepreneurs cycle between feedback‐driven search and resource recombination, gradually stabilizing new routines and locally viable knowledge. By theorizing problemistic search as a knowledge exploration process and bricolage as knowledge recombination in action, the study advances a knowledge‐centric view of entrepreneurial learning under constraint. The paper contributes to the behavioral theory of the firm by contextualizing problemistic search in institutionally weak settings, extends knowledge management scholarship by foregrounding informal and practice‐based learning, and offers process insights for entrepreneurs navigating resource‐constrained environments.

ABSTRACT The expansion of the knowledge‐based economy has driven rapid growth in the literature on knowledge management (KM) and intellectual capital (IC), covering topics such as learning, business, and innovation. Scholars recognize knowledge as a significant resource that can foster innovation to attain a competitive advantage. However, literature on the role of entrepreneurial orientation (EO) and learning orientation (LO) in enhancing the impact of both KM and IC on innovation is still limited and fragmented. Hence, a theoretical gap in understanding this role limits both theoretical advancement and practical application. Addressing this problem is essential to developing a comprehensive model of how organizations transform knowledge into innovation and ultimately competitive advantage. The study provides empirical evidence on these relationships through a quantitative approach, utilizing a cross‐sectional survey. The sample comprised employees working in the private sector, N = 97. The analysis employed Partial Least Squares Structural Equation Modelling (PLS‐SEM) with SmartPLS to test the hypothesized relations, and IBM SPSS Statistics to conduct descriptive analyses. The findings affirm that EO is a key driver of innovation, emphasizing its central role in channeling knowledge into innovation.

ABSTRACT This research analysed the mediating role of organisational creativity in the relationship between the three dimensions of social capital and entrepreneurial opportunities recognition, by drawing on resource‐based view and, in particular, on social capital theory. The study was conducted within the Spanish hotel sector, focusing specifically on Spanish hotel chains. The results confirmed that organisational creativity mediates the positive influence of structural, relational, and cognitive social capital on entrepreneurial opportunities recognition. This paper contributes to the field of corporate entrepreneurship empirically developing and testing more sophisticated relationships between social capital and opportunities recognition which are explained by creativity. This is a significant contribution because most studies about corporate entrepreneurship focus on examining only the direct relationship. We advance in the literature by examining creativity in firms as well. Research on this topic has focused mainly on the individual's perspective, whereas analysis at the organisational level is more recent. In addition, we contribute by analysing all dimensions of social capital. This is an important step given the paucity of research on social capital's relational and cognitive dimensions.

ABSTRACT The study examined the role of knowledge sharing with suppliers as one knowledge management (KM) practice and the challenges associated with implementing sustainable green supply chain management (GSCM) to achieve environmental sustainability. A mixed‐methods approach was used to draw on the views of management supervisors in an industrial gases company. The findings contribute to the KM literature by highlighting the most significant obstacles to implementing GSCM in businesses operating in the industrial gases industry, including the increasing costs of green raw materials and the need for government support. This study offers theoretically and empirically grounded insights for managers and policymakers related to implementing sustainable GSCM practices in organizational and industrial contexts within the field of KM. To the best of the author's knowledge, this study is the first in the field of KM to offer a practical framework for how knowledge sharing enables sustainable GSCM practices.

ABSTRACT This study aims to investigate the mediating effect of a family firm's entrepreneurial orientation on the association between knowledge sharing and its competitive performance. With the use of partial least squares‐based structural equation modelling, the hypotheses put out for this research were evaluated on the data gathered from 351 sample family business units operating in India. The results of this research indicate that the relation between a family firm's competitive performance and its explicit and implicit knowledge sharing is partially mediated by the entrepreneurial orientation of the proprietor. This study, which examines how the transformation of explicit and tacit knowledge into a family firm's competitive performance, with the moderating effect of entrepreneurial orientation on this connection, may be regarded as a pioneering work.

ABSTRACT Organisations struggle to optimise human–AI collaboration in knowledge‐intensive decision‐making. This paper proposes the Trust–Complementarity Model of Collective Intelligence (TCM‐CI), explaining how calibrated trust and complementary capability utilisation drive superior organisational performance. Through systematic synthesis of human–AI interaction and knowledge management research, we identify three core mechanisms: (1) calibrated trust maximises collective intelligence by balancing appropriate reliance with necessary oversight, (2) complementarity–trust interaction determines optimal performance when high capability utilisation combines with appropriate trust levels and (3) dynamic feedback loops create reinforcing organisational learning cycles. The framework provides practical guidance for executives designing human–AI teams, developing trust calibration training, and establishing performance metrics. By integrating psychological trust factors with cognitive capability optimisation, this model offers actionable insights for knowledge management practitioners implementing AI‐augmented decision systems while advancing theoretical understanding of human–AI collaboration effectiveness.

ABSTRACT Digitally supported continuous improvement (CI) in manufacturing SMEs often stalls not because tools fail, but because governance decisions are made under low visibility, mixed signals, and uneven trace quality. Existing instruments can position an organization or report activity and outcomes, yet they rarely bind minimal, auditable evidence to an explicit next move. This paper introduces the Positioning and Governance Compass (PG‐Compass), a low‐burden decision overlay that converts a fixed Minimal Evidence Packet into exactly one risk‐aware governance move—ADVANCE, HOLD, REPAIR, or RESTART—paired with a short Decision Record for decision provenance. PG‐Compass enforces conservative defaults: it caps confidence and blocks escalation under severe credibility constraints (e.g., trace‐integrity/backfilling risk or control‐framing risk) and routes to HOLD/REPAIR when signals are incomplete or contradictory. The artifact is grounded in routine dynamics and socio‐technical thinking, treating routine–trace mismatches and off‐ledger work as governance‐relevant signals. Evaluation uses an independent multi‐rater protocol on transcript‐derived evidence packets (nine interview cases) to assess decision consistency and auditability, without making performance or outcome claims.

ABSTRACT Small and medium‐sized enterprises (SMEs) often face unique challenges in integrating advanced digital technologies due to limited resources and organizational constraints. While much attention has been given to large firms' adoption of big data analytics (BDA) and artificial intelligence (AI), less is known about how SMEs can leverage these tools effectively. This study addresses the research question of how SMEs, especially SaaS B2B, can transform marketing processes through advanced data analytics, large language models (LLMs), and generative AI. Drawing on a qualitative case study of a SaaS company, this research proposes a framework for integrating web mining and LLMs into a semantic analysis process to improve client acquisition by better targeting potential clients, i.e., leads. It optimizes lead qualification and facilitates the replication of successful cases by automating data collection, generating tailored marketing content, and creating customized commercial proposals. Based on the business analytics success model (BASM) framework, our study investigates the organizational changes necessary to embed innovative data‐driven business processes into existing workflows through process mapping. Key stakeholders evaluate the proposal via interviews, which discuss the barriers to aligning and opportunities associated with adopting AI‐driven solutions and emphasize the importance of technological capabilities with business goals. The findings reveal that for SMEs, adopting advanced analytics is contingent upon overcoming resource constraints through targeted organizational adjustments, such as incorporating dominant logic identification into strategic planning. This research contributes theoretically by customizing the BASM for SaaS SMEs and offering a roadmap for implementing AI‐driven marketing solutions, demonstrating how SMEs can enhance operational efficiency and achieve competitive advantages, even in resource‐constrained environments.

ABSTRACT Knowledge management is the structured supervision of an organization's knowledge resources to generate value and accomplish its targeted objectives. It is comprised of systems, processes, and strategies to maintain and improve the creation, storing, evaluation, and dissemination of knowledge. The study was aimed to explore the knowledge management practices at the University of Malakand. The researcher adopted a descriptive research design for the study. A qualitative research approach was used to collect and analyze data. A purposive sampling method was employed to select participants of the study. Fourteen heads of departments were purposively selected as participants of the study who were most likely to provide relevant and insightful information regarding knowledge management practices. Data were collected through interviews consisting of open‐ended questions. The collected data were analyzed using the thematic analysis model. The results revealed that knowledge management practices at the University of Malakand predominantly rely on traditional methods. While some technological tools are in use, they are limited and not fully optimized. The study concludes that to enhance knowledge management effectiveness, the University of Malakand should adopt more progressive, technology‐driven practices. Integrating modern KM tools can significantly contribute to improved institutional performance, collaboration, and knowledge sharing across departments.