Drawing on the combined strengths of Conservation of Resources (COR) theory and Information Processing Theory (IPT), we explored the relationship between technological turbulence and knowledge hiding, with employees’ awareness of AI as a key mediating factor. We also examined the dual moderating effects of hierarchical flatness in an organization and change leadership to test the theoretical synergy of COR and IPT. Data were collected from 315 women-owned manufacturing firms and analyzed through PLS-SEM. The findings reveal that employees’ AI awareness mediates the link between technological turbulence and knowledge-hiding behaviors. A flatter organizational structure aids in spreading AI awareness among employees, while effective change leadership serves as a crucial moderator, reducing knowledge hiding by influencing employee behavior. The survey highlights that increased AI awareness can lead to fears about AI's impact on job security, driving knowledge hiding. However, skilled leadership can mitigate this by promoting innovation and encouraging collaboration with external partners. This study provides original insights by developing and empirically validating a model of knowledge hiding in response to technological turbulence in women-owned manufacturing sectors in Punjab, Pakistan. It deepens the understanding of knowledge management by examining the interplay between organizational flatness and AI integration, areas often overlooked in existing literature.
AbbassHA (2019) Social integration of artificial intelligence: functions, automation allocation logic and human-autonomy trust. Cognitive Computation11(2): 159–171.
2.
AinNUAzeemMUSialMH, et al. (2022) Linking knowledge hiding to extra-role performance: the role of emotional exhaustion and political skills. Knowledge Management Research & Practice20(3): 367–380.
3.
AnandPHassanY (2019) Knowledge hiding in organizations: everything that managers need to know. Development and Learning in Organizations: An International Journal33(6): 12–15.
4.
Arias-PérezJVélez-JaramilloJ (2022) Understanding knowledge hiding under technological turbulence caused by artificial intelligence and robotics. Journal of Knowledge Management26(6): 1476–1491.
5.
ArslanACooperCKhanZ, et al. (2022) Artificial intelligence and human workers interaction at team level: a conceptual assessment of the challenges and potential HRM strategies. International Journal of Manpower43(1): 75–88.
6.
AutryCWGraweSJDaughertyPJ, et al. (2010) The effects of technological turbulence and breadth on supply chain technology acceptance and adoption. Journal of Operations Management28(6): 522–536.
7.
BaduEEMarkweiED (2005) Internet awareness and use in the University of Ghana. Information Development21(4): 260–268.
8.
BagSPretoriusJHCGuptaS, et al. (2021) Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities. Technological Forecasting and Social Change163: 120420.
9.
BodlajMČaterB (2019) The impact of environmental turbulence on the perceived importance of innovation and innovativeness in SMEs. Journal of Small Business Management57: 417–435.
10.
BoehmerNSchinnenburgH (2023) Critical exploration of AI-driven HRM to build up organizational capabilities. Employee Relations: The International Journal45(5): 1057–1082.
11.
BroughamDHaarJ (2018) Smart technology, artificial intelligence, robotics, and algorithms (STARA): employees’ perceptions of our future workplace. Journal of Management & Organization24(2): 239–257.
12.
BustilloAPimenovDYMiaM, et al. (2021) Machine-learning for automatic prediction of flatness deviation considering the wear of the face mill teeth. Journal of Intelligent Manufacturing32(3): 895–912.
13.
CanhotoAIClearF (2020) Artificial intelligence and machine learning as business tools: a framework for diagnosing value destruction potential. Business Horizons63(2): 183–193.
14.
ChatterjeeSChaudhuriRThrassouA, et al. (2021) Antecedents and consequences of knowledge hiding: the moderating role of knowledge hiders and knowledge seekers in organizations. Journal of Business Research128: 303–313.
15.
ChenIJPaulrajALadoAA (2004) Strategic purchasing, supply management, and firm performance. Journal of Operations Management22(5): 505–523.
16.
ChinWWNewstedPR (1999) Structural equation modeling analysis with small samples using partial least squares. Statistical Strategies for Small Sample Research1(1): 307–341.
17.
ChoiMJKimSParkH (2018) Empirical study on the factors influencing process innovation when adopting intelligent robots at small-and medium-sized enterprises—the role of organizational supports. Information9(12): 315.
18.
ChuiMFranciscoS (2017) Artificial intelligence the next digital frontier. McKinsey and Company Global Institute47(3.6): 6–8.
19.
ConnellyCEZweigD (2015) How perpetrators and targets construe knowledge hiding in organizations. European Journal of Work and Organizational Psychology24(3): 479–489.
20.
CoupeT (2019) Automation, job characteristics and job insecurity. International Journal of Manpower40(7): 1288–1304.
21.
DenglerKMatthesB (2018) The impacts of digital transformation on the labour market: substitution potentials of occupations in Germany. Technological Forecasting and Social Change137: 304–316.
22.
DhamijaPChiariniAShaplaS (2023) Technology and leadership styles: a review of trends between 2003 and 2021. The TQM Journal35(1): 210–233.
23.
DumasCBeineckeRH (2018) Change leadership in the 21st century. Journal of Organizational Change Management31(4): 867–876.
24.
ErkutluHChafraJ (2018) Despotic leadership and organizational deviance: the mediating role of organizational identification and the moderating role of value congruence. Journal of Strategy and Management11(2): 150–165.
25.
FaulFErdfelderEBuchnerA, et al. (2009) Statistical power analyses using G* Power 3.1: tests for correlation and regression analyses. Behavior Research Methods41(4): 1149–1160.
26.
FornellC.LarckerD. F. (1981) Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research18(1): 39–50.
27.
GalbraithJK (1973) Power and the useful economist. American Economic Review63(1): 1–11.
GhasemaghaeiMTurelO (2021) Possible negative effects of big data on decision quality in firms: the role of knowledge hiding behaviours. Information Systems Journal31(2): 268–293.
30.
GillR (2002) Change management–or change leadership?Journal of Change Management3(4): 307–318.
31.
HairJFRisherJJSarstedtMRingleCM (2019) When to use and how to report the results of PLS-SEM.European Business Review31(1): 2–24.
32.
HenselerJRingleCMSarstedtM (2015) A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science43: 115–135.
33.
HeroldDMFedorDBCaldwellS, et al. (2008) The effects of transformational and change leadership on employees’ commitment to a change: a multilevel study. Journal of Applied Psychology93(2): 346–357.
34.
HiggsMRowlandD (2011) What does it take to implement change successfully? A study of the behaviors of successful change leaders. The Journal of Applied Behavioral Science47(3): 309–335.
35.
HobfollSE (1989) Conservation of resources: a new attempt at conceptualizing stress. American Psychologist44(3): 513–524.
36.
HopfKMüllerOSholloA, et al. (2023) Organizational implementation of AI: Craft and mechanical work. California Management Review66(1): 23–47.
37.
HuangCJ (2010) Corporate governance, corporate social responsibility and corporate performance. Journal of Management & Organization16(5): 641–655.
38.
HuangMHRustRT (2018) Artificial intelligence in service. Journal of Service Research21(2): 155–172.
39.
HuangXKristalMMSchroederRG (2008) Linking learning and effective process implementation to mass customization capability. Journal of Operations Management26(6): 714–729.
40.
HuangXKristalMMSchroederRG (2010) The impact of organizational structure on mass customization capability: a contingency view. Production and Operations Management19(5): 515–530.
41.
HungKPChouC (2013) The impact of open innovation on firm performance: the moderating effects of internal R&D and environmental turbulence. Technovation33(10-11): 368–380.
42.
HuangYBasuCHsuMK (2010) Exploring motivations of travel knowledge sharing on social network sites: an empirical investigation of US college students. Journal of Hospitality Marketing & Management19(7): 717–734.
43.
HuoWCaiZLuoJMenCJiaR (2016) Antecedents and intervention mechanisms: a multi-level study of R&D team's knowledge hiding behavior. Journal of Knowledge Management20(5): 880–897.
44.
HuoBWangBLiZ (2024) How to deal with technological turbulence for improving innovation performance. Technology Analysis & Strategic Management36(3): 549–562.
45.
IramTAshfaqBBilalAR, et al. (2024) Does a high-performance human resource practice stimulate employee creativity: a moderated mediation model. FIIB Business Review: 23197145241229256.
46.
IramTBilalARAhmadZ, et al. (2022) Building a conscientious personality is not sufficient to manage behavioral biases: an effective intervention for financial literacy in women entrepreneurs. Business Perspectives and Research: 22785337221114675.
47.
IramTBilalARLatifS (2021) Is awareness that powerful? Women’s financial literacy support to prospects behaviour in prudent decision-making. Global Business Review25(5): 1356–1381.
48.
IramTBilalARSaeedT, et al. (2023a) Nurturing green creativity in women intrapreneurs through green HRM: testing moderated mediation model-a step toward Saudi Vision 2030. Kybernetes25(1): 93–118.
49.
IramTBilalARAhmadZ, et al. (2023b) Does financial mindfulness make a difference? A nexus of financial literacy and behavioural biases in women entrepreneurs. IIM Kozhikode Society & Management Review12(1): 7–21.
50.
JahanzebSDe ClercqDFatimaT (2021) Organizational injustice and knowledge hiding: The roles of organizational dis-identification and benevolence. Management Decision59(2): 446–462.
51.
KarmegamDMappillairajuB (2023) Information extraction using a mixed method analysis of social media data: a case study of the police shooting during the anti-Sterlite protests at Thoothukudi, India. Information Development39(4): 739–749.
52.
KellyCJKarthikesalingamASuleymanM, et al. (2019) Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine17: 1–9.
53.
Kumar JhaJVarkkeyB (2018) Are you a cistern or a channel? Exploring factors triggering knowledge-hiding behavior at the workplace: evidence from the Indian R&D professionals. Journal of Knowledge Management22(4): 824–849.
54.
LiJJBonnMAYeBH (2019) Hotel employee’s artificial intelligence and robotics awareness and its impact on turnover intention: the moderating roles of perceived organizational support and competitive psychological climate. Tourism Management73: 172–181.
55.
LingmontDNAlexiouA (2020) The contingent effect of job automating technology awareness on perceived job insecurity: exploring the moderating role of organizational culture. Technological Forecasting and Social Change161: 120302.
56.
Losada-OtaloraMPeña-GarcíaNSánchezID (2020) Interpersonal conflict at work and knowledge hiding in service organizations: the mediator role of employee well-being. International Journal of Quality and Service Sciences13(1): 63–90.
57.
LoureiroSMCGuerreiroJTussyadiahI (2021) Artificial intelligence in business: state of the art and future research agenda. Journal of Business Research129: 911–926.
58.
LozadaNArias-PérezJPerdomo-CharryG (2019) Big data analytics capability and co-innovation: an empirical study. Heliyon5(10): e02541.
59.
MaLZhangXDingX (2020) Enterprise social media usage and knowledge hiding: a motivation theory perspective. Journal of Knowledge Management24(9): 2149–2169.
60.
MakariusEEMukherjeeDFoxJD, et al. (2020) Rising with the machines: a sociotechnical framework for bringing artificial intelligence into the organization. Journal of Business Research120: 262–273.
61.
MatsunagaM (2022) Uncertainty management, transformational leadership, and job performance in an AI-powered organizational context. Communication Monographs89(1): 118–139.
62.
McGuireWJ (1968) Personality and attitude change: an information-processing theory. Psychological Foundations of Attitudes171: 196.
63.
MithasSChenZLSaldanhaTJ, et al. (2022) How will artificial intelligence and Industry 4.0 emerging technologies transform operations management?Production and Operations Management31(12): 4475–4487.
64.
MouakketSAboelmagedM (2023) Factors influencing green information technology adoption: a multi-level perspective in emerging economies context. Information Development39(4): 699–719.
65.
NamT (2019) Technology usage, expected job sustainability, and perceived job insecurity. Technological Forecasting and Social Change138: 155–165.
66.
OnyenekeGBAbeT (2021) The effect of change leadership on employee attitudinal support for planned organizational change. Journal of Organizational Change Management34(2): 403–415.
67.
OztemelEGursevS (2020) Literature review of Industry 4.0 and related technologies. Journal of Intelligent Manufacturing31(1): 127–182.
68.
PanWZhangQTeoTS, et al. (2018) The dark triad and knowledge hiding. International Journal of Information Management42: 36–48.
69.
PanichayakornTJermsittiparsertK (2019) Mobilizing organizational performance through robotic and artificial intelligence awareness in mediating role of supply chain agility. International Journal of Supply Chain Management8(5): 757–768.
70.
PaekSSchuckertMKimTTLeeG, (2015) Why is hospitality employees' psychological capital important? The effects of psychological capital on work engagement and employee morale. International Journal of Hospitality Management50: 9–26.
71.
PengH, (2013) Why and when do people hide knowledge?. Journal of Knowledge Management17(3): 398–415.
72.
PimenovDYBustilloAWojciechowskiS, et al. (2023) Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review. Journal of Intelligent Manufacturing34(5): 2079–2121.
73.
PodsakoffPMMacKenzieSBLeeJYPodsakoffNP, (2003) Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology88(5): 879.
74.
QuaquebekeNVGerpottFH (2023) The now, new, and next of digital leadership: how artificial intelligence (AI) will take over and change leadership as we know it. Journal of Leadership & Organizational Studies30(3): 265–275.
75.
RampersadG (2020) Robot will take your job: innovation for an era of artificial intelligence. Journal of Business Research116: 68–74.
76.
RansbothamSKironDGerbertP, et al. (2017) Reshaping business with artificial intelligence: closing the gap between ambition and action. MIT Sloan Management Review59(1).
77.
RubinVLChenYThorimbertLM (2010) Artificially intelligent conversational agents in libraries. Library Hi Tech28(4): 496–522.
78.
SarstedtMHenselerJRingleCM (2011) Multigroup analysis in partial least squares (PLS) path modeling: alternative methods and empirical results. In: Measurement and Research Methods in International Marketing. Emerald Group Publishing Limited, pp.195–218.
79.
SerenkoABontisN (2016) Understanding counterproductive knowledge behavior: antecedents and consequences of intra-organizational knowledge hiding. Journal of Knowledge Management20(6): 1199–1224.
80.
SimhaAElloyDFHuangHC (2014) The moderated relationship between job burnout and organizational cynicism. Management Decision52(3): 482–504.
81.
SinghSK (2019) Territoriality, task performance, and workplace deviance: empirical evidence on role of knowledge hiding. Journal of Business Research97: 10–19.
82.
ShirishAVerstraeteNTantanOC, (2023) The impact of ICT characteristics on students' learning outcomes during COVID-19 lockdown period: The crucial role of techno eustress perception. Human Systems Management42(2): 149–162.
83.
SongMVan Der BijHWeggemanM (2005) Determinants of the level of knowledge application: a knowledge-based and information-processing perspective. Journal of Product Innovation Management22(5): 430–444.
84.
VanpouckeEVereeckeAWetzelsM (2014) Developing supplier integration capabilities for sustainable competitive advantage: a dynamic capabilities approach. Journal of Operations Management32(7-8): 446–461.
85.
WijayatiDTRahmanZRahmanMFW, et al. (2022) A study of artificial intelligence on employee performance and work engagement: the moderating role of change leadership. International Journal of Manpower43(2): 486–512.
86.
XiongCChangVScuottoV, et al. (2021) The social-psychological approach in understanding knowledge hiding within international R&D teams: an inductive analysis. Journal of Business Research128: 799–811.
87.
YaoZLuoJZhangX (2020) Gossip is a fearful thing: the impact of negative workplace gossip on knowledge hiding. Journal of Knowledge Management24(7): 1755–1775.
88.
YuanYYangLChengX, et al. (2021) What is bullying hiding? Exploring antecedents and potential dimension of knowledge hiding. Journal of Knowledge Management25(5): 1146–1169.
