Evidence overview · Culture, knowledge management and transition economies

How does national culture shape knowledge sharing and collaboration in transition economies? Evidence from Albania

In Albania, a post-communist transition economy, studies by Vajjhala and colleagues found a national culture that accepts hierarchy and leans collectivist, and managers who see that culture, together with low trust, shaping how employees share knowledge. A validated five-factor Hofstede-based survey of 73 Tirana business professionals estimated power distance at 78.7, individualism at 42.7, masculinity at 72.9, uncertainty avoidance at 64.6 and long-term orientation at 52.2 (Vajjhala & Strang, 2014, Cross Cultural Management). In interviews with 20 managers of medium-sized enterprises, 85% said national culture influences employee behaviour, 70% named lack of trust as a key barrier and 90% said top management support is essential (Vajjhala & Baghurst, 2014, IJKMS). Both studies are small and limited to Albania, and the related work on communities of practice and socio-cultural big data is conceptual (Vajjhala, 2016, Organizational Knowledge…) (Vajjhala & Strang, 2017, NMNC).

A summary of 4 peer-reviewed studies by Narasimha Rao Vajjhala and co-authors, set against the literature those studies cite. Each study links to its own page with the full abstract and DOI.

Key takeaways

  1. A Hofstede-based survey of 73 Tirana professionals estimated Albania's power distance at 78.7 and individualism at 42.7, a hierarchical and collectivist profile (Vajjhala & Strang, 2014, Cross Cultural Management).
  2. Contrary to expectations for post-communist societies, Albania's masculinity index was high (72.9) (Vajjhala & Strang, 2014, Cross Cultural Management)[12].
  3. Seventy percent of 20 Albanian SME managers named lack of trust as a key cultural barrier to knowledge sharing, and 80% said communism had a negative influence on Albanian culture (Vajjhala & Baghurst, 2014, IJKMS).
  4. Ninety percent of the interviewed Albanian managers said top management support is essential for knowledge-sharing initiatives, and 95% believed reward systems would motivate participation (Vajjhala & Baghurst, 2014, IJKMS).
  5. Knowledge-sharing models developed in Western economies may not fit transition economies with different cultural conditions (Vajjhala & Baghurst, 2014, IJKMS)[1].
  6. Socio-cultural 'viability' has been proposed as a big data dimension for judging whether findings generalise to the population of interest, but it has not yet been tested empirically (Vajjhala & Strang, 2017, NMNC).

Context: national culture, knowledge sharing and transition economies

Transition economies are countries moving from a centrally planned, often communist, system toward a free-market economy. Vajjhala and Baghurst note that most knowledge management research in these countries has applied models and theories developed in Western countries or Japan[1], and that post-communist societies can face a culture of non-sharing and mistrust[2]. Lack of trust between knowledge providers and receivers is a recognised barrier to knowledge sharing in firms[3], and knowledge sharing is described as a social-exchange process subject to both organisational and national cultural influences[4][5] (Vajjhala & Baghurst, 2014, IJKMS).

Measuring national culture raises its own problems. Vajjhala and Strang point out that the well-known culture models cover only some countries, that Albania's GLOBE data predate its emergence as a transition economy, and that culture profiles from the 1980s may need revalidation where countries moved from communism to democracy (Vajjhala & Strang, 2014, Cross Cultural Management). They also note criticisms that Hofstede's data came from a single corporation (IBM) over 1967-1972[14] and that such dimensions describe country-level rather than individual-level value structures[15].

What the studies found: Albania's national culture profile and knowledge-sharing barriers

Vajjhala and Strang surveyed 73 business professionals in Tirana using a five-factor multicultural instrument adapted from Hofstede. Exploratory factor analysis reduced 55 items to 35, and confirmatory factor analysis supported the five-factor model (CFI = 0.94, TLI = 0.93, SRMR = 0.05, RMSEA = 0.05; overall Cronbach's alpha = 0.76). The resulting indexes were PDI = 78.7, ICI = 42.7, MFI = 72.9, UAI = 64.6 and LTI = 52.2. Albania was closest to China on power distance, similar to Turkey on collectivism, closer to the Czech Republic and Slovakia on uncertainty avoidance, and had least in common with the USA. Contrary to the authors' hypothesis, masculinity was high rather than low (Vajjhala & Strang, 2014, Cross Cultural Management).

Vajjhala and Baghurst interviewed 20 mid- or top-level managers from ten medium-sized Albanian enterprises in ICT, food processing, banking and insurance, construction and tourism. Six themes emerged. Seventeen managers (85%) said national culture influences employee behaviour, 16 (80%) said communism had a negative influence on Albanian culture, 14 (70%) cited lack of trust and 12 (60%) said most Albanians hesitate to share knowledge. Eighteen (90%) said top management support is essential, 19 (95%) believed reward systems would motivate participation, 9 (45%) reported perceived risks such as fear of losing one's position, 14 (70%) had observed age-based clustering, and 13 (65%) said their firms lacked or underused information systems for knowledge sharing (Vajjhala & Baghurst, 2014, IJKMS).

A book chapter by Vajjhala extends this to communities of practice (CoPs) in SMEs. It argues that CoPs can help resource-constrained SMEs improve efficiency and productivity, that their success in transition economies depends partly on social and cultural factors, and it proposes a framework for CoPs in transition economies. This is a conceptual contribution without empirical data (Vajjhala, 2016, Organizational Knowledge…).

How the findings fit the wider cross-cultural and knowledge management literature

Some results match earlier work and some do not. The knowledge-sharing study places its findings alongside literature holding that national culture shapes how effectively knowledge is shared and that people in high power-distance cultures hesitate to share across hierarchical levels[7], that low power-distance cultures are more conducive to knowledge sharing[8], and that people in collectivist cultures tend to share within, but not outside, their group[4]. Its finding on organisational culture is consistent with work identifying organisational culture as the most significant factor in knowledge management[6], and its information-systems finding with evidence that SMEs struggle to fund and maintain knowledge management systems[9] (Vajjhala & Baghurst, 2014, IJKMS).

For the national culture indexes, Vajjhala and Strang report agreement with GLOBE on high uncertainty avoidance for Albania, but a higher power distance than GLOBE indicated[10]. Their collectivist, hierarchical profile matches the GLOBE description of the Eastern European cluster, except that masculinity was higher than the gender egalitarianism reported for that cluster[11]. The high masculinity also contradicts the view that post-communist societies are usually feminine[12]; the authors attribute it to Balkan neighbours and 500 years of Ottoman rule. A study using the Trompenaars-Hampden-Turner framework reached similar conclusions on collectivism and past-present orientation for Albania and its Adriatic neighbours[13] (Vajjhala & Strang, 2014, Cross Cultural Management).

Methods used and socio-cultural big data for measuring organizational fit

The Albanian work combines a quantitative survey with exploratory and confirmatory factor analysis (Vajjhala & Strang, 2014, Cross Cultural Management) and a qualitative multisite case study coded in NVivo 9.0 (Vajjhala & Baghurst, 2014, IJKMS). Strang and Vajjhala's later conceptual paper asks whether socio-cultural big data could measure organizational fit for hiring and partnering decisions. It proposes 'viability' as a socio-cultural big data dimension and a two-phase approach: sample big data and reduce it with methods such as factor or cluster analysis, then test specific hypotheses before generalising (Vajjhala & Strang, 2017, NMNC). It draws on arguments that big data has value only when it drives decisions[16], on a Twitter study linking emoticon styles to individualism and collectivism[17], and on work showing how big data could help cultural sociology[18]. The paper cautions that data collected in one location may not represent only its inhabitants (Vajjhala & Strang, 2017, NMNC).

Limitations and practical implications for managers in transition economies

The evidence base is narrow. The culture survey used a nonrandom sample of 73 professionals from Tirana only, below the commonly recommended 100 for factor analysis, and did not analyse the sixth Hofstede dimension, indulgence versus restraint (Vajjhala & Strang, 2014, Cross Cultural Management). The interview study covered five sectors and medium-sized firms only, and its percentages come from 20 managers (Vajjhala & Baghurst, 2014, IJKMS). The organizational-fit and communities-of-practice papers propose frameworks that have not been tested empirically (Vajjhala & Strang, 2017, NMNC) (Vajjhala, 2016, Organizational Knowledge…).

With these caveats, the authors suggest that foreign organisations use Albania's indexes to compare against their own culture profile and adapt their approach where gaps are large (Vajjhala & Strang, 2014, Cross Cultural Management). They also suggest that leaders of Albanian SMEs take national culture and trust into account, secure top management support, introduce incentives and training, and adapt information systems to national and organisational culture rather than adopting generic knowledge management systems (Vajjhala & Baghurst, 2014, IJKMS).

Evidence table

StudySetting / dataMethodKey result
Vajjhala & Strang (2014)
Cross Cultural Management
73 business professionals in Tirana, Albania (mean age 32)Survey; exploratory and confirmatory factor analysis of a Hofstede-based five-factor instrumentPDI = 78.7, ICI = 42.7, MFI = 72.9, UAI = 64.6, LTI = 52.2; CFI = 0.94, RMSEA = 0.05, alpha = 0.76
Vajjhala & Baghurst (2014)
International Journal of Knowledge Management Studies
20 managers from ten medium-sized Albanian enterprises in five sectorsQualitative multisite exploratory case study; 27 open-ended questions; NVivo coding85% national culture influences behaviour; 70% lack of trust a barrier; 90% top management support essential
Vajjhala (2016)
Organizational Knowledge Facilitation through Communities of Practice in Emerging Markets
SMEs in transition economiesConceptual book chapterFramework for communities of practice; success depends partly on social and cultural factors
Vajjhala & Strang (2017)
New Mathematics and Natural Computation
Literature on big data and culture models (Hofstede, GLOBE, Trompenaars and Hampden-Turner, Competing Values Framework)Conceptual literature reviewProposes 'viability' as a socio-cultural big data dimension and a two-phase sampling and hypothesis-testing approach

Questions researchers ask

What are Albania's Hofstede cultural dimension scores?
Vajjhala and Strang estimated PDI = 78.7, ICI = 42.7, MFI = 72.9, UAI = 64.6 and LTI = 52.2 from a survey of 73 business professionals in Tirana. They describe this as the first measurement of Albania's national culture with a Hofstede-based model and note that the small, nonrandom sample limits it (Vajjhala & Strang, 2014, Cross Cultural Management).
What prevents knowledge sharing in SMEs in transition economies such as Albania?
In interviews with 20 managers, lack of trust (70%), hesitation to share knowledge (60%), fear of losing one's position (45%) and age-based clustering (70%) were reported, alongside a legacy of communism (Vajjhala & Baghurst, 2014, IJKMS). Literature cited by the study similarly links post-communist settings to a culture of non-sharing and mistrust[2].
How do Albania's culture scores compare with GLOBE findings?
Vajjhala and Strang report agreement with GLOBE on high uncertainty avoidance, but a higher power distance than GLOBE showed for Albania[10] (Vajjhala & Strang, 2014, Cross Cultural Management). Their high masculinity score also differs from the gender egalitarianism reported for the Eastern European cluster[11].
Can big data be used to measure culture and organizational fit?
Strang and Vajjhala propose that it could, using sampling, data reduction and hypothesis testing, and point to Twitter-based studies of emoticons and individualism-collectivism as examples (Vajjhala & Strang, 2017, NMNC)[17]. The proposal is conceptual, and the authors caution about big data quality and representativeness.

Open questions

  • Would the Albanian culture indexes hold with a larger, random national sample drawn from cities beyond Tirana?
  • Do the cultural barriers to knowledge sharing found in Albanian medium-sized firms also appear in large firms and in other Central or Eastern European transition economies?
  • How did communism shape inter-cultural communication and knowledge sharing, and does its influence weaken as younger employees enter the workforce?
  • Are gaps in individualism and masculinity harder to bridge in cross-border collaboration than gaps in power distance, uncertainty avoidance and long-term orientation, as Vajjhala and Strang posit?
  • Can publicly available social media data measure cultural dimensions such as uncertainty avoidance and individualism-collectivism validly enough to inform organizational-fit decisions?

References

Studies summarised

Other literature cited

  1. [1] Michailova, S. and Sidorova, E. (2010) ‘Knowledge management in transition economies: selected key issues and possible research avenues’, Organizations and Markets in Emerging Economies, Vol. 1, No. 1, pp.68–81.
  2. [2] Burke, M.E. (2011) ‘Knowledge sharing in emerging economies’, Library Review, Vol. 60, No. 1, pp.5–14.
  3. [3] Haas, M.R. and Hansen, M.T. (2007) ‘Different knowledge, different benefits: toward a productivity perspective on knowledge sharing in organizations’, Strategic Management Journal, Vol. 28, No. 11, pp.1133–1153.
  4. [4] Michailova, S. and Hutchings, K. (2006) ‘National cultural influences on knowledge sharing: a comparison of China and Russia’, Journal of Management Studies, Vol. 43, No. 3, pp.383–405.
  5. [5] Chen, J., Sun, P.Y.T. and McQueen, R.J. (2010) ‘The impact of national cultures on structured knowledge transfer’, Journal of Knowledge Management, Vol. 14, No. 2, pp.228–242.
  6. [6] Alavi, M., Kayworth, T.R. and Leidner, D.E. (2005) ‘An empirical examination of the influence of organizational culture on knowledge management practices’, Journal of Management Information Systems, Vol. 22, No. 3, pp.191–224.
  7. [7] Jing, L. (2010) ‘Culture and knowledge transfer: theoretical considerations’, Journal of Service Science and Management, Vol. 3, No. 1, pp.159–169.
  8. [8] Herremans, I. and Isaac, R. (2007) ‘Relationships among intellectual capital, uncertain knowledge, and culture’, Global Journal of Business Research, Vol. 1, No. 1, pp.24–35.
  9. [9] Lester, D.L. and Tran, T.T. (2008) ‘Information technology capabilities: suggestions for SME growth’, Journal of Behavioral and Applied Management, Vol. 10, No. 1, pp.72–88.
  10. [10] House, R.J., Hanges, P.J., Javidan, M., Dorfman, P.W. and Gupta, V. (2004), Culture, Leadership, and Organizations: The GLOBE Study of 62 Societies, Sage, Thousand Oaks, CA.
  11. [11] Bakacsi, G., Sandor, T., Karacsonyi, A. and Viktor, I. (2002), “Eastern European cluster: tradition and transition”, Journal of World Business, Vol. 37 No. 1, pp. 69-80.
  12. [12] Borić, M. and Wetwood, J.M. (2003), “Do theories developed in one cultural environment apply abroad? Countries in transition”, Ekonomski fakultet Sveucilista u Splitu, Vol. 1 No. 3, pp. 967-983.
  13. [13] Goic, S. and Bilic, I. (2008), “Business culture in Croatia and some countries in transition”, Management, Vol. 13 No. 2, pp. 41-63.
  14. [14] Shenkar, O. and Luo, Y. (2004), International Business, Wiley, New York, NY.
  15. [15] Fischer, R., Vauclair, C.-M., Fontaine, J.R.J. and Schwartz, S.H. (2010), “Are individual-level and country-level value structures different? Testing Hofstede’s legacy with the Schwartz value survey”, Journal of Cross-Cultural Psychology, Vol. 41 No. 2, pp. 135-151.
  16. [16] A. Gandomi and M. Haider, Beyond the hype: Big data concepts, methods, and analytics, International Journal of Information Management 35(2) (2015) 137–144.
  17. [17] J. Park, Y. M. Baek and M. Cha, Cross-cultural comparison of nonverbal cues in emoticons on twitter: Evidence from big data analysis, Journal of Communication 64(2) (2014) 333–354.
  18. [18] C. A. Bail, The cultural environment: Measuring culture with big data, Theory and Society 43(3–4) (2014) 465–482.