Journal article · 2017
Measuring Organizational-Fit Through Socio-Cultural Big Data
New Mathematics and Natural Computation, 13(2), pp. 145–158 · Published
Summary
Vajjhala and Strang (2017) reviewed the literature on big data and national and organizational culture models in a conceptual paper on how socio-cultural big data could be collected and analyzed to measure organizational-fit factors for human resourcing, partnering and other organizational decisions. They propose “viability” as a socio-cultural dimension of big data, a lens for whether big data interpretations will generalize meaningfully to the intended population. They also propose a two-phase approach: sampling and data reduction, followed by formulating and testing hypotheses to make inferential generalizations for decision-making.
Practical implication. Researchers should collect big data only when its socio-cultural meaning is relevant and generalizable to the population of interest, since collecting and analyzing it requires large economic and time investments.
- The Problem
- Organizations make hiring and partnering decisions on cultural fit, but no published studies had used big data to measure the relationship between culture and organizational fit.
- The Methodology
- Conceptual paper based on a literature review of big data and national and organisational culture models, proposing a socio-cultural viability dimension and a two-phase sampling, data-reduction and hypothesis-testing approach.
- The Core Finding
- The paper proposes “viability” as a socio-cultural sixth V of big data and a two-phase method — sampling and data reduction, followed by hypothesis testing — for turning socio-cultural big data into generalizable organizational-fit evidence.
- The Citation
- Vajjhala, N. R., & Strang, K. D. (2017). Measuring Organizational-Fit Through Socio-Cultural Big Data. New Mathematics and Natural Computation, 13(2), 145–158. https://doi.org/10.1142/S179300571740004X
The summary, key findings, methods, implications and relevance notes on this page are this website’s own description of the paper, written from the published abstract and text. The official abstract and citation details are given further down.
Key findings
- Finding 1. The authors' literature review found no published studies examining the relationship of global culture behavior with organizational fit using big data sources.
- Finding 2. The paper proposes 'viability' as a new big data 'V' representing a socio-cultural lens, alongside volume, velocity, variety, veracity and value, to judge whether big data interpretations generalize meaningfully to the intended population.
- Finding 3. The study proposes a two-phase approach to socio-cultural big data: phase one samples big data to reduce volume and velocity and applies data reduction such as factor or cluster analysis; phase two formulates and tests hypotheses to make inferential generalizations.
- Finding 4. For big data samples, the paper recommends normality tests such as Kolmogorov–Smirnov, Anderson–Darling, Ryan–Joiner or Shapiro–Wilk to decide whether parametric statistical tests may be used, and p-values or triangulation to judge veracity.
- Finding 5. The paper suggests organizational culture fit could be measured with global culture models such as Gudykunst's cultural variability in communication framework, emoticon-based measures of individualism versus collectivism, or Vajjhala and Strang's socio-cultural collaboration strategies.
- Finding 6. The authors recommend that researchers use publicly available Twitter and Facebook datasets to study cultural dimensions such as uncertainty avoidance and individualism–collectivism for organizational-fit decisions.
- Finding 7. The paper cautions that big data collected from a country or geographical area may not represent the inhabitants of that location alone, so researchers must be careful when using and analyzing big data.
Source: Vajjhala & Strang (2017), New Mathematics and Natural Computation, 13(2), pp. 145–158. DOI: 10.1142/S179300571740004X
The study in detail
What question does this paper answer?
How could socio-cultural big data be collected and analyzed to measure organizational-fit factors relevant for human resourcing, partnering and other organizational decisions?
What did the study find, in detail?
From a literature review the authors propose redefining a big data 'V' – viability – as a socio-cultural dimension, a lens for whether big data interpretations will generalize meaningfully to the intended population. They propose a two-phase approach: first sample big data to reduce volume and velocity challenges and apply data reduction (e.g. factor and cluster analysis); then formulate and test specific hypotheses with parametric or nonparametric statistics to make inferential generalizations for decision-making.
Why does it matter?
Collecting and analyzing big data requires large economic and time investments, so the authors argue it makes no sense to do so if the results will not be socio-culturally meaningful for the population of interest. Publicly available social networking data could let researchers test cultural dimensions such as uncertainty avoidance and individualism–collectivism, informing decisions on hiring, partnering and technology acceptance, though researchers must be cautious about big data quality.
This study is part of the evidence overview How does national culture shape knowledge sharing and collaboration in transition economies? Evidence from Albania
Methods and data
| Research question | How can socio-cultural big data be used to measure organizational fit for decision-making? |
|---|---|
| Design | Conceptual paper based on a literature review |
| Data | Published literature on big data, organizational culture and national culture models (Hofstede, Trompenaars and Hampden-Turner, GLOBE, Competing Values Framework) |
| Methods | Development of a conceptual model and a two-phase research approach using sampling, data reduction and hypothesis testing |
| Main result | A new socio-cultural 'viability' dimension for big data and a two-phase approach to study socio-cultural big data |
| Implication | Big data should be collected only when its socio-cultural meaning is relevant and generalizable to the population of interest |
| Citation | Vajjhala & Strang (2017) · DOI 10.1142/S179300571740004X |
Definitions and key terms
- Big data
- Data too big for conventional systems such as relational database management systems, characterized by volume, velocity, variety, veracity and value. — as used in Vajjhala & Strang (2017)
- Organizational fit
- The degree to which an individual or partner matches an organization, which the authors propose could be measured through online personality and socio-cultural behavior. — as used in Vajjhala & Strang (2017)
- Viability (socio-cultural dimension)
- The authors' proposed big data dimension referring to the socio-cultural relevance of big data, based on where it was collected, and whether its meaning will generalize to the intended population. — as used in Vajjhala & Strang (2017)
Abstract
We propose that businesses, government, and not-for-profit entities could benefit from a better understanding of organizational behavior through the lens of a contemporary global culture model. Human resourcing and partnering decisions could be improved by using global culture to ensure a better organizational-fit as well as to reduce the risk of destructive relationship dependencies. For an extreme-limits example, a company could inadvertently hire a terrorist or a social loafer seeking to steal competitive intelligence. A big data approach supported by a socio-cultural framework could help in hypothesis testing which is essential for advancing the body of knowledge in organizational behavior. This paper will make a scholarly contribution by identifying literature relevant to collecting and analyzing organizational big data that could explain beneficial socio-cultural behavior. This paper will explore how sources of qualitative big data could be collected and then analyzed to measure organizational-fit factors relevant for decision-making.
Abstract as published in New Mathematics and Natural Computation.
Keywords: Big data; qualitative; organizational behavior; culture; organizational-fit; decision-making
Open questions
- Can publicly available social media data measure cultural dimensions such as uncertainty avoidance and individualism-collectivism validly enough to inform organizational-fit decisions?
Open questions from the evidence overview Culture, knowledge management and transition economies.
Practical implications
- Researchers planning a big data study should check that the results will be socio-culturally meaningful for the population of interest, because the authors argue that the large economic and time investments otherwise make no sense.
- Businesses, government and not-for-profit entities could, the authors propose, improve human resourcing and partnering decisions by using a global culture model to ensure a better organizational fit and to reduce the risk of destructive relationship dependencies.
- Researchers can use publicly available Twitter and Facebook datasets to study cultural dimensions such as uncertainty avoidance and individualism–collectivism for organizational-fit decisions, as the authors recommend.
- Researchers analyzing big data collected from a country or geographical area must be careful, because such data may not represent the inhabitants of that location alone.
Drawn from Vajjhala & Strang (2017), New Mathematics and Natural Computation, 13(2), pp. 145–158.
When this research may be relevant
This article may be relevant to researchers theorising the socio-cultural dimensions of big data, organisational fit and person–organisation matching, applying Hofstede, GLOBE or Competing Values Framework constructs to social media or big data, or designing two-phase big data research strategies for human resourcing and partnering decisions.
Research topics addressed: big data; organizational fit; organizational culture; national culture; Hofstede cultural dimensions; GLOBE model; Competing Values Framework; social media data; cultural sociology; big data V model; viability dimension; hypothesis testing; decision-making; organizational behavior
How to cite
- APA 7
- Vajjhala, N. R., & Strang, K. D. (2017). Measuring Organizational-Fit Through Socio-Cultural Big Data. New Mathematics and Natural Computation, 13(2), 145–158. https://doi.org/10.1142/S179300571740004X
- MLA 9
- Vajjhala, Narasimha Rao, and Kenneth David Strang. “Measuring Organizational-Fit Through Socio-Cultural Big Data.” New Mathematics and Natural Computation, vol. 13, no. 2, 2017, pp. 145–58. https://doi.org/10.1142/S179300571740004X.
- IEEE
- N. R. Vajjhala and K. D. Strang, “Measuring Organizational-Fit Through Socio-Cultural Big Data,” New Mathematics and Natural Computation, vol. 13, no. 2, pp. 145–158, Jul. 2017, doi: 10.1142/S179300571740004X.
BibTeX
@article{vajjhala2017organizational,
title = {Measuring Organizational-Fit Through Socio-Cultural Big Data},
author = {Vajjhala, Narasimha Rao and Strang, Kenneth David},
journal = {New Mathematics and Natural Computation},
volume = {13},
number = {2},
pages = {145--158},
year = {2017},
publisher = {World Scientific Publishing Company},
doi = {10.1142/S179300571740004X},
url = {https://doi.org/10.1142/S179300571740004X}
}RIS
TY - JOUR TI - Measuring Organizational-Fit Through Socio-Cultural Big Data AU - Vajjhala, Narasimha Rao AU - Strang, Kenneth David JO - New Mathematics and Natural Computation PY - 2017 DA - 2017/07/03 VL - 13 IS - 2 SP - 145 EP - 158 PB - World Scientific Publishing Company SN - 1793-0057 DO - 10.1142/S179300571740004X UR - https://www.narasimharao.net/research/organizational-fit-socio-cultural-big-data/ LA - en ER -
Links and files
- DOI: https://doi.org/10.1142/S179300571740004X
- This page as text: https://www.narasimharao.net/research/organizational-fit-socio-cultural-big-data/paper.md
- Permanent address of this page: https://www.narasimharao.net/research/organizational-fit-socio-cultural-big-data/
Related work
Related work: the literature this study cites
The paper draws on arguments that big data has value only when it drives decisions[1], on a Twitter study linking emoticon styles to individualism and collectivism[2], and on work showing how big data could help cultural sociology[3].
- [1] A. Gandomi and M. Haider, Beyond the hype: Big data concepts, methods, and analytics, International Journal of Information Management 35(2) (2015) 137–144.
- [2] 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.
- [3] C. A. Bail, The cultural environment: Measuring culture with big data, Theory and Society 43(3–4) (2014) 465–482.
From the evidence overview How does national culture shape knowledge sharing and collaboration in transition economies? Evidence from Albania, which cites only works that appear in the studies’ own reference lists. This is a selection, not the full reference list of Vajjhala & Strang (2017).
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