research paper on organisational culture

Culture and Organizational Performance

• First Online: 03 January 2022

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• Sonja A. Sackmann   ORCID: orcid.org/0000-0001-7846-652X 2  

Part of the book series: Contributions to Management Science ((MANAGEMENT SC.))

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Chapter 5 explores the relationship between an organization’s culture and organizational performance. First, the chapter addresses the different ways used to measure both culture and performance. This information provides the readers with an understanding of the multifaceted nature of the relationship between culture and performance. The following five sections focus on: (1) research that investigated the direct link between culture and performance indicators; (2) research that investigated cultures perceived to be different and their impact on performance indicators; (3) studies that found an indirect link between culture and performance; (4) research that proposes an interaction effect; (5) non-linear and reciprocal relationships between culture and performance. The chapter closes with a discussion if strong cultures tend to be better for organizational performance, as some authors and practitioners claim.

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Sackmann, S.A. (2021). Culture and Organizational Performance. In: Culture in Organizations. Contributions to Management Science. Springer, Cham. https://doi.org/10.1007/978-3-030-86080-6_5

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Research Article

Dynamics of organizational culture: Individual beliefs vs. social conformity

* E-mail: [email protected]

Current address: Engineering Mathematics, University of Bristol, Bristol, United Kingdom

Affiliations Engineering Mathematics, University of Bristol, Bristol, United Kingdom, Systemic Consult Ltd, Bradford-on-Avon, United Kingdom

Affiliations Systemic Consult Ltd, Bradford-on-Avon, United Kingdom, Systems IDC, University of Bristol, Bristol, United Kingdom

Affiliation Systems IDC, University of Bristol, Bristol, United Kingdom

• Christos Ellinas, 
• Neil Allan, 
• Anders Johansson

• Published: June 30, 2017
• https://doi.org/10.1371/journal.pone.0180193
• Reader Comments

The complex nature of organizational culture challenges our ability to infer its underlying dynamics from observational studies. Recent computational studies have adopted a distinctly different view, where plausible mechanisms are proposed to describe a wide range of social phenomena, including the onset and evolution of organizational culture. In this spirit, this work introduces an empirically-grounded, agent-based model which relaxes a set of assumptions that describes past work–(a) omittance of an individual’s strive for achieving cognitive coherence; (b) limited integration of important contextual factors—by utilizing networks of beliefs and incorporating social rank into the dynamics. As a result, we illustrate that: (i) an organization may appear to be increasingly coherent in terms of its organizational culture, yet be composed of individuals with reduced levels of coherence; (ii) the components of social conformity—peer-pressure and social rank—are influential at different aggregation levels.

Citation: Ellinas C, Allan N, Johansson A (2017) Dynamics of organizational culture: Individual beliefs vs. social conformity. PLoS ONE 12(6): e0180193. https://doi.org/10.1371/journal.pone.0180193

Editor: Renaud Lambiotte, Universite de Namur, BELGIUM

Received: January 13, 2017; Accepted: June 12, 2017; Published: June 30, 2017

Copyright: © 2017 Ellinas et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: CE was partially funded by the Engineering and Physical Sciences Research Council (EPSRC), UK ( https://www.epsrc.ac.uk/ ) under the grant EP/G037353/1, an EPSRC Doctoral Prize fellowship and Systemic Consult Ltd ( http://www.systemicconsult.com/ ). Systemic Consult Ltd provided support in the form of salaries for authors CE and NA, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the 'author contributions' section.

Competing interests: CE and NA are partially and fully employed, respectively, by Systemic Consult Ltd, a commercial consultancy in the risk industry. This does not alter the authors' adherence to PLOS ONE policies on sharing data and materials. The authors have declared that no competing interests exist.

Introduction

On July 13 th 2012, JP Morgan announced a loss of 5.8 billion USD as a result of fraudulent activity taking place, ironically, in a unit aimed in reducing risk [ 1 ]. In the following years this event has come to be known as the “London Whale” incident, with substantial financial consequences—including 459 million USD of losses in net income and over 1 billion USD in penalties imposed by regulators [ 1 , 2 ], with JP Morgan putting aside a further 23 billion to pay for related potential legal bills to come [ 3 ]. One would expect that a single manifestation of such malpractice would have been enough to tarnish the reputation of the entire sector, yet nothing much has changed for the finance sector, with examples of fraudulent activity and misconduct continuing to emerge on a regular basis [ 4 ].

The emergence of these events is partly attributed to the way in which risk management is practiced, which reflects the risk culture of a given organization. In this context, risk culture can be defined as “patterns of behavior, habits of thinking, traditions and rituals, shared values and shared language that shape and direct the management of risk” [ 5 ] within these organizations. In other words, risk culture can be interpreted as the embodiment of various beliefs that affect the way the risk management function is performed within a given organization. More generally, any set of beliefs that affects a given organizational function—such as risk management—reflects organizational culture.

Understanding organizational culture falls in the class of problems traditionally tackled by social scientists. Such problems are notoriously hard to tackle (i.e. non-linear in nature [ 6 ]; multiple levels involved [ 7 ]; temporal character [ 8 ]), with traditional approaches being limited in identifying correlations between certain variables, whilst noting the ability of certain control variables in mitigating this behavior. However, recent arguments have challenged the validity of such regression studies due to the complex nature of the underlying dynamics, emphasizing the fact that little attention has been given in mapping the underlying mechanisms responsible for the emergence of these correlations [ 6 , 9 , 10 ]. A compounding factor to this criticism is the static view imposed on such social phenomena, with the majority of sociological studies being limited in describing snapshots of an organization’s state rather than focusing on the dynamic nature of the problem [ 8 , 11 ] (some notable exceptions can be found in the recent review of [ 12 ]). The issue with such an approach is that volatile micro level dynamics may be missed by looking at a macro level trend, which in turn may lead to a misleading interpretation of the nature of the system being studied. As a result, social scientists have so far been unable to provide a unified theory for explaining the emergence of collective social phenomena that define organizational behavior [ 13 – 15 ].

Recent developments made under the umbrella of complexity science [ 16 – 18 ] have adopted a distinctly different view, where contextual differences of various social phenomena are abstracted away in search for overarching principles [ 19 ]. Such studies typically introduce plausible mechanisms which are subsequently tested in their capacity to replicate widely observed patterns, with homophily and social influence often cited examples of such mechanisms [ 20 , 21 ].

Despite the appeal of such generalization, context dependent aspects are often crucial in the dynamics of collective social phenomena, questioning the extent of abstraction a model should have [ 22 ] (for a network-related discussion, see [ 23 ]). Hence the challenge lies in identifying mechanisms that capture important aspects of a phenomenon while preserving the model’s transparency. In the case of collective social behavior, typical mechanistic models focus on social [ 24 ] or cognitive [ 25 ] aspects, with a choice between the two being enforced in an attempt to keep the proposed model as simple as possible (and consequently preserve generalizability of results). In the context of adopting a new cultural belief, the majority of work focuses either on the process of adopting a new belief due to peer-pressure (the social aspect) or due to increase cognitive coherence (the cognitive aspect). Yet we argue that by decoupling the two aspects, the conflicting reality of certain social phenomena is omitted e.g. an individual may adopt a certain belief due to social conformity even if it contradicts his/her own beliefs. Therefore, this study introduces an integrative framework able to capture both social and cognitive aspects in a simple model. In addition, the model extends the degree of contextual integration by introducing both peer-pressure and social rank (i.e. social conformity) into the core dynamics, thus extending previous studies which focused in examining each aspect in isolation (e.g. [ 26 ] and [ 27 ] respectively). Notable examples which adopt a similar integrative approach by accounting for both social and cognitive aspects include the recent work of Gavetti and Warglien [ 28 ] and Rodriguez et al. [ 29 ].

The contribution of this study lies at both a theoretical and practical level. From a theoretical point of view, the development of a formal model of the dynamics of organizational culture can allow for an explicit test of various hypotheses found in the large body of empirical work that has already been developed. In doing so, it has the potential of exposing weaknesses of prevailing wisdom (e.g. individual behavioral traits are independent, as assumed in [ 30 ]; collective behavior can be modelled in a context independent manner i.e. ignoring the influence of social rank, as assumed in the class of threshold models [ 24 ]) and thus, sharpen future research questions. From a practical point of view, the model can be used to assess how various organizational changes, such as the underlying hierarchical structure, can affect the evolution of organizational culture.

Literature review

Left alone, it is reasonable to assume that every individual would possess a unique set of beliefs, negating the very notion of shared beliefs—and to an extent—organizational culture. Yet it is common experience that beliefs are exchanged between individuals through social interaction [ 17 ], with individuals reacting accordingly by adopting, amending and/or discarding various beliefs [ 31 – 33 ]. As a result of these actions, the onset of organizational culture can follow a number of possible trajectories, including complete agreement (i.e. every individual shares the exact same beliefs), complete disagreement (i.e. every individual holds a different belief) and various meta-states (i.e. clusters of agreement of various size). In other words, even though there is an envisioned culture at which an organization abides to, achieving coherence at lower aggregation levels (e.g. individuals) is increasingly challenging due to its emergent nature (e.g. [ 34 ]).

In an attempt to explore the role of social interaction (or peer-pressure ) in collective behavior, Granovetter [ 24 ] highlighted how individuals are willing to switch behavior, if a given percentage of individual surrounding them already shares that behavior [ 35 ]. In other words, the state of an individual is a function of the state of its neighbors—and in general, to the social network—with a threshold value controlling the individual’s tolerance to the induced peer-pressure. This powerful notion gave rise to the major class of threshold models [ 26 , 36 ] which has subsequently been used to study a wide range of collective social behaviors [ 35 , 37 ]. By doing so, the influence of the social network architecture has slowly consolidated within the field [ 38 , 39 ], shifting the focus in uncovering the mechanisms that take place across these networks.

The opinion vector-based model is one such class of models that focuses on these mechanisms [ 17 ]. In general, opinion vector-based models define the state of an individual as a vector of independent behavioral traits which can be modified through social interactions. A prominent example has been developed by Axelrod [ 30 ], which eloquently proposes that: the probability of two individuals interacting is a function of their belief overlap (i.e. homophily ), with interacting individuals becoming increasingly similar through imitation (i.e. social influence). Despite the evident self-reinforcing nature of Axelrod’s model, complete agreement between individual agents is not always attainable, with disparate clusters of distinctly different sub-cultures emerging. The simplicity and non-trivial behavior of this, and similar, models have made it increasingly attractive, with recent work applying it to progressively more realistic contexts, where individuals are embedded in complex network architectures that resemble real-life interaction networks e.g. [ 32 , 40 , 41 ]. This class of models illustrates the non-trivial outcome of even simple, plausible dynamics that may describe aspects of social behavior, reinforcing the proposition of computational models as suitable tools for exploring organizational behavior [ 13 , 42 , 43 ]. More generally, it is an early response to recent calls from organization theorists, proposing a shift of focus from mapping the state of an organizational aspect to understanding the dynamics that fuel it.

In summary, threshold models [ 24 , 26 ] and opinion vector-based models [ 30 , 32 ] are two major classes of models that have been used to explain the emergence of empirically-noted correlations across various collective social behaviors. However, a set of assumptions that underlies these models challenge their validity. In particular, opinion vector-based models assume that behavioral beliefs held by an individual are entirely independent . This is because the process of trait exchange is modelled on each individual trait independently of the state of the remaining traits. Yet, psychological research has consistently shown that individuals strive for cognitive coherence using various cognitive mechanisms [ 25 , 44 ], suggesting that the converse is true i.e. beliefs are interacting. Hence, by assuming that beliefs are independent, the conflicting nature between preserving internal consistency (by rejecting an inconsistent belief) and peer-pressure (by accepting an inconsistent belief) is missed. Similarly, threshold models consider the structure of the social network as the only factor relevant to the dynamics e.g. [ 24 , 35 , 36 ]. Hence, these models are context agnostic i.e. contextual information is assumed to be irrelevant to the dynamics of the social interaction process—a typical feature of studies that draw from the natural sciences [ 22 , 39 ]. Yet recent studies challenge the validity of this assumption in an organization context, where the perception of rank plays a key role in collective functions, including organizational learning [ 27 ], social exchange [ 45 ] and co-operation [ 46 ]. In other words, recent work suggests that the strength of social conformity is a function of both social interaction and social rank, yet the latter is ignored by the class of threshold models.

In response, this work develops an integrative, agent-based model where a network of interactions between agents is constructed, with the cognitive state of each agent being characterized by a set of interconnected beliefs—for a visual overview of the model see Fig 1 . By doing so, this model relaxes the two aforementioned assumptions (i.e. belief independence and being context agnostic) by: (a) accounting for the conflict between external (peer-pressure) and internal (preserving cognitive coherence) pressure in accepting an external belief, and (b) including both peer-pressure and social rank directly into the dynamics of the belief adoption mechanism.

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https://doi.org/10.1371/journal.pone.0180193.g001

Belief network

An empirical dataset is used as the basis to construct the belief network of each agent. Specifically, the dataset is composed of survey results captured during a risk-culture mapping project commissioned by a UK-based insurance organization. Each of the 49 participants was given a total of thirty questions revolving around six central themes (five questions per theme), with each question drawing on a specific belief related to the application of risk management processes within their organization. For details see S1 File ; the entire dataset is available in S1 Dataset . With beliefs beings widely-considered to be a core component of culture [ 12 , 47 ], this dataset can be viewed as a suitable proxy for the risk culture of this organization.

Each question has two components, where each participant is asked to reflect on both current and desirable state of that given belief—see Fig 2a and 2b respectively. By doing so, the study captures whether a given individual prescribes “more of the same” behavior—i.e. future state for a belief scores equally, or higher, that its current counterpart—or a shift in the current behavior, referred to as “less of the same” i.e. future state for a given belief scores lower than the current state.

Color coding reflects the theme of the question. Central mark in each box plot, with top and bottom box edges, correspond to the median, the 25 th and the 75 th percentile respectively. Markers outside the box correspond to outliers.

https://doi.org/10.1371/journal.pone.0180193.g002

In order to relax the assumption of belief independence, the construct of Social Knowledge Structure [ 48 ] is used, where associations between beliefs are introduced resulting to a belief network . Association assignments follow the structure of a random network with a modular structure reflecting the nature of the survey (i.e. six modules are enforced, corresponding to the number of themes)–see Table A in S1 File . In doing so, every network is inherently composed of stable triads, suggesting that all agents are initially characterized by perfect cognitive. This initialization stage is done to ensure that all agents enter the simulation at the exact same stage, ensuring consistency across all model realizations. Finally, it should be noted that the enforced structure is clearly an assumption, as we are unable to infer the actual belief network from the survey in an unbiased manner. As such, the influence of the belief network topology itself is important and worthy of further exploration.

The survey results are subsequently introduced into the belief network in the following manner: a belief which corresponds to “more of the same” behavior is characterized by a positive sign—see Fig 3 , belief m ; the same applies for the case where a belief has the same current and future state Conversely, a response of “less of the same” belief, results to belief l being allocated a negative sign. Once each node receives a sign depending on the nature of the belief, every association is signed depending on the nature of the two beliefs that it relates: if the two beliefs have similar signs (i.e. +/+ or -/-), a positive association is obtained ( Fig 3 ; belief k and l ); in the case of dissimilar signs (i.e. +/- or -/+) a negative association is obtained ( Fig 3 ; belief l and m ). This process is repeated for each agent at every realization of the dynamics, and in effect results to each agent having a distinct belief network, with respect to all other agents, and with respect to itself at different realizations.

Boxes corresponds to typical survey scores, with upper box leading to belief m being allocated a “+” (“more of the same”) while belief l is allocated a “-” (“less of the same”). Consequently, an association between similarly signed beliefs (e.g. belief k and l ) is considered as positive (solid line), with opposite signs (e.g. belief l and m ) resulting to a negative associations (dotted line).

https://doi.org/10.1371/journal.pone.0180193.g003

Social network

To account for the role of social conformity in the process of belief exchange, individuals are embedded in a network structure which represents the social interactions between them. Formally, the social network is defined as an undirected graph G SN = {{ V SN }{ E SN }} where each participant i of the survey is abstracted as agent i , ∈ V SN , with every interaction between agent i and j being represented by the undirected link e i , j where e i , j ∈ E SN .

A generative model able to replicate typical characteristics of social networks is used. Specifically, empirical studies have highlighted the importance of clustering in social networks where the effect of collective dynamics is prominent [ 24 , 49 , 50 ]. Seminal work by Watts and Strogatz [ 51 ] has further consolidated this insight by identifying it across a wide range of networks, with further work illustrating the perseverance of such architectures across various relevant domains [ 52 ]. Watts and Strogatz [ 51 ] further introduced a generative model capable of replicating the effect, which is subsequently used to generate the social network used herein. Specifically, the generative process is grounded on two basic steps: (a) construct a lattice—in a ring formation—with a given number of connections, and (b) randomly rewire a given portion of link in order to introduce “shortcuts” between distant nodes.

In order to generate this network, a third parameter is needed, which corresponds to the average degree of each node. This parameter is effectively used to set aspect (a)–in this case it is set to 2 i.e. each individual regularly interacts directly with a further two individual, or roughly 4% of the total organization. With respect to (b), the probability of rewiring a link between two nodes is set to 0.5. Finally, the number of nodes is fixed to reflect the number of survey participants (i.e. 49), with a new social network being generated for every realization of the model, in step with Section 3.2.

The rules dictating the dynamics of the agent-based model are as follows: at each time step t, a random pair of connected agents i and j is chosen, with agent i (source) randomly choosing an association from its internal belief network and sending it agent j (receiver). Assuming the receiver is willing to listen to the source, the receiver will accept the incoming association if it increases the coherence of its belief network. If not, the receiver may still accept the incoming association based on social grounds i.e. the individual foregoes cognitive consistency for social conformity. In the case where the incoming association is accepted, it may have one of the following effect—it serves as a new association between two existing beliefs or it replaces an old one association. The probability for accepting an incoming association is a function of peer-pressure (quantified as the portion of the receiver’s neighbors that agree with that belief) and social rank difference between the receiver and its neighbors. A control parameter γ is introduced to control the influence balance between peer-pressure and social rank, enabling various organizational contexts to be formulated and subsequently tested (see Eq 3 ).

Conflicting dynamics

The model proposed is characterized by conflicting dynamics, where agents strive for cognitive consistency yet may forego it for the sake of social conformity—the latter being a twofold aspect combining elements of peer-pressure and social rank. Additionally, control variable γ effectively dictates the balance between the two ( Eq 3 ). Fig 4 first presents results at the three intermediate states of the model, with 4a, 4b and 4c capturing the network coherence ( C SN ) and average cognitive coherence ( C BN ) at γ = 0, γ = 0.5 and γ = 1.0 respectively. Note that the colored band around each line plot maps the standard error across the independent runs, calculated as the sample standard deviation divided by the square root of the number of runs.

Note that values begin at 1 due to perfect initial coherence across agents. Band around each plot corresponds to the standard error across twelve independent runs.

https://doi.org/10.1371/journal.pone.0180193.g004

With respect to the case where social rank sole determining factor for adopting an incoherent belief, both network coherence and average cognitive coherence exhibit increasingly similar behavior—see Fig 4a . In particular, both measures follow a monotonically decreasing trend, with network coherence decreasing at a faster rate. After roughly 45,000 time steps, both measures stabilize, reaching values of approximately 0.798 and 0.686 for the average cognitive coherence and network coherence respectively. Interestingly, the fact that cognitive coherence of the average is consistently preserved at higher levels compared to network coherence is an example of how social interactions can undermine the state of the overall system. In the context of risk culture, the case of social rank being the sole determining factor results in a situation where fairly coherent individuals (with respect to their belief system) interact to give rise to an increasingly heterogeneous organization—an increasingly undesirable state.

Shifting focus to the case where social rank and peer-pressure play an equal role (i.e. γ = 0.5), both network coherence and average cognitive coherence initially exhibit a monotonically decreasing behavior—see Fig 4b . However, given enough time this convergence breaks downs, with the average cognitive coherence of the agents continues to reduce until it stabilizes around 0.63. At the same time, the trajectory of the network coherence of the agents is reversed, exhibiting a slow but steady increase reaching a maximum value of just below 0.9. By the end of the simulation, the majority of connected agents are, on average, increasingly similar to their neighbors (hence, high network coherence) yet each individual agent is increasingly incoherent in terms of its belief network (hence, low average cognitive coherence). In the context of risk culture, the inclusion of both social rank and peer-pressure, at equal weights, results in a situation where the organization appears to be in an increasingly coherent state, despite its composition of increasingly dissimilar individuals.

Finally, in the case where peer-pressure is the sole determining factor for adopting an incoherent belief (i.e. γ = 1), the disparity between average cognitive coherence and network coherence increases, with the overall behavior becoming non-monotonic—see Fig 4c . In particular, both average cognitive coherence and network coherence initially exhibit a monotonic decrease, albeit at a faster rate compared to the case of γ = 0.5. Cognitive coherence continues to decrease until reaching a minimum value of roughly 0.6 –at this point the behavior reverses reaching a final value of 0.62. Similarly, the network coherence reverses its decreasing trajectory early on, yet this increase manifest at an increasingly smaller rate until it plateaus at roughly the same time when cognitive coherence reaches a value of 0.6. Beyond this point, network coherence starts to slowly decline, reaching a value of roughly 0.93. By the end of the simulation, the situation is fairly similar to the one obtained in the case of γ = 0.5, where the network appears to be increasingly coherent, despite the overall reduction in cognitive coherence of the individual agents. Importantly, this case varies from the case of γ = 0.5 in the way both measures evolve across time, where Fig 4c suggests a mismatch between the evolution time of cognitive coherence and that of network coherence, evident by the difference in the rate at which the trend of each measure changes. For the sake of completeness, Figure B in S1 File contains additional results for the entire range of γ.

Organizational vs. individual level

The distinct focus of the two measures introduced herein—average cognitive coherence and network coherence—allows for a systematic examination of the influence of γ across the organizational and individual level. Specifically, Fig 5a illustrates the influence of the belief exchange process at the organizational level by considering network coherence. On the other hand, Fig 5b focuses on the individual level by considering the average cognitive coherence. As such, it is clear that varying γ has a distinctly different effect in terms of aggregation levels, where an increase in γ has a positive effect at the organizational level (denoted by an increase in network coherence) while γ has a negative effect at the individual level (denoted by an increase in average cognitive coherence).

Similar to Fig 4 , values begin at 1 due to perfect initial coherence across agents.

https://doi.org/10.1371/journal.pone.0180193.g005

Delving further into each aggregation level, the influence of γ impacts both the overall trend and the resulting value. Focusing on the network level ( Fig 5a ), the monotonic pattern noted in the case of γ = 0 quickly changes to a non-monotonic trend, where the rate in which network coherence increases depends on γ. With respect to the individual level ( Fig 5b ), a similar change is noted albeit at higher γ values where the overall behavior switches from monotonic to non-monotonic. Considering the distinct effect that γ has with respect to the affected scales (positive effect on the overall network; negative effect on the average individual), the magnitude of difference across its extreme values is also examined. In order to capture this effect, the absolute difference between the two extreme values (i.e. γ = 0 and γ = 1) for each measure is plotted—see Fig 6 . Overall, the impact of γ in terms of absolute size is initially greater at the network level (i.e. red line overcomes the blue line). After roughly 20,000 time steps, this behavior changes as the impact of γ at the individual level increases (i.e. blue line overcomes the red line).

https://doi.org/10.1371/journal.pone.0180193.g006

Fig 7 plots E for the entire range of γ, where solid and dotted weights represent positive and negative E values respectively. With respect to the one extreme (γ = 0), E increases slowly as the simulation progresses, which translates to social average cognitive coherence being larger than the network coherence measure. This difference stabilizes at the final stages of the simulation, reaching a value of approximately 0.07. At this point, the state of an average individual can be inferred by observing the state of the entire organization, as the two measures (average individual coherence and network coherence) are fairly close to each other. However, this situation changes rapidly with increasing γ. In the case of the other extreme end (γ = 1) the value of E dives into the negative regime reaching a maximum value of approximately -0.22, effectively translating to the converse effect i.e. network coherence is higher than average cognitive coherence. At this point, an organization may appear to be increasingly coherent (i.e. high average network coherence)–a rather deceiving deduction since it is composed by individuals with reduced levels of cognitive coherence. More generally, given enough time for the evolution process to set in, it is increasingly challenging to infer the coherence of individuals by mapping the overall organization (and vice-versa). Increasing the role of peer-pressure (i.e. γ increases) amplifies this effect as it results to an increase rate of change for E.

https://doi.org/10.1371/journal.pone.0180193.g007

Organizational culture emerges via the aggregation of beliefs of each agent that the organization is composed of. Driven by social interactions and cognitive biases, agents exchange, amend and/or discard their beliefs and as a result, organizational culture remains in a state of continuous flux.

From a methodological standpoint, the proposed model relaxes a number of assumptions that underlie a significant portion of past related work, including belief independence (as assumed in opinion, vector-based models e.g. [ 30 , 32 ]) and being context agnostic (as assumed in threshold models e.g. [ 24 , 26 ]). In particular, the proposed model leverages the conflicting dynamics of individual consistency and social conformity to map the onset of organizational culture. In doing so, it integrates both social and cognitive aspects of the belief adoption process—aspects which are traditionally examined in isolation. With a focus on social conformity, the compounding effect of its components (peer-pressure and social rank) is isolated and further explored. Results indicate that peer-pressure plays a significant role in shaping the ability of individuals to reduce cognitive dissonance that describes their beliefs. At the same time, social rank significantly affects the homogeneity of an organization, in terms of overlapping beliefs—such insight is in step with the increased recognition of rank as a key determinant to organizational behavior [ 27 ]. As such, the proposed model contextualizes the influence of social influence by introducing social rank, and in doing so highlights the disparity in their influence across different organizational levels.

Theoretical implications

Disparity between organizational levels..

At the original model formulation ( Eq 3 , γ = 0.5), peer-pressure and social rank influence an agent from adopting a new association, even if it contradicts its own belief network. As a result, two important features are uncovered: (a) at the initial stages of the simulation, the average individual and network coherence decrease monotonically at a similar rate ( Fig 4b ; ~1,000 time-steps) and (b) given enough time, the two measures diverge, highlighting a chasm between the state of the organization at the individual and network level ( Fig 4b ; >1,000 time-steps).

With respect to (a), the heightened influence of social conformity in the process of association exchange is linked with a deteriorating coherence in terms of cultural overlap, both at the individual and organizational level. Such insight is consistent with empirical work highlighting the negative relationship between increased exposure to social conformity (e.g. the effect of open-plan offices) and trust (a component of organizational culture)–see [ 61 ]. Whilst one should be cautious when drawing such broad inference, the dynamics proposed herein provide a simple, and plausible, mechanism for such phenomena. Importantly, the state of an average individual can be inferred by observing the state of the entire organization, as the two measures (average individual coherence and network coherence) deteriorate at the same rate.

However, such inference is not always possible. Specifically point (b) highlights that an organization may appear to be increasingly coherent (i.e. high network coherence) yet be composed by individuals with increased cognitive dissonance (i.e. low average cognitive coherence). Such individuals may eventually undertake harmful actions, surprising outside observers who were deceived by the evident coherence of the organization. More generally, given enough time for the evolution process to set in, it will be increasingly challenging to infer the coherence of individuals by mapping the overall organization. As a result, damaging events undertaken by individuals with distinctly different culture (e.g. conduct risk [ 62 ]) is inherently hard to predict given that the identification of such individuals must take place at an individual basis—a resource intensive and challenging task. These finding highlight some of the limitations of observational studies for theory building purposes, as (a) a trend appears to emerge and then disappear without any external intervention (where observational studies are limited in mapping the state of an organization at a given point in time—see Roe [ 8 ] and Holme and Liljeros [ 9 ]) and (b) different organizational levels exhibit distinct behaviors despite the fact that the exact same mechanism is in place (where observational studies do not explicitly distinguish between multiple levels of analysis, as noted by Kozlowski et al. [ 7 ]).

The role of peer-pressure and social rank.

Results highlight that the influence of peer-pressure and social rank are segregated across aggregation levels, where peer-pressure has a greater influence on the overall network while social rank has a greater influence on the state of the individual.

The theoretical argument emerging from this finding is two-fold. Firstly, the influence of social conformity is not isolated on a single organizational level, highlighting the non-trivial nature of its effect. As such, future studies around social collective behavior in general (and organizational culture in particular), should account for distinct levels of analysis—an argument echoed by Kozlowski et al. [ 7 ] and evident by the results of this work. Secondly, studies focusing solely on the influence of the network structure[ 26 ] or on the influence of social rank[ 27 ] should not be taken in isolation as their influence is exercised at distinct levels.

This point is increasingly important as organizational studies increasingly embrace the complex nature of organizational and are consequently tempted to decouple the two aspects. Results herein further reinforce this argument by providing a comparable richer picture. Specifically, in the case of isolating the effect of peer-pressure and social rank, strictly monotonic behavior typically describes results at both individual and network level. Yet when their effect is integrated, non-trivial behavior is observed i.e. through non-monotonic trend (e.g. Figs 4 and 6 ). Evidence of this sort emphasize the non-trivial effect of the evolution of organizational culture even under the relatively simple premise of the proposed model.

Limitations

This work has some limitations that provide opportunities for further work. One limitation is that it considers static network topologies. In the case of the social network, this is a simplification as individuals come and go in an organization (corresponding to a change in the number of nodes in the social network), along with their interactions dynamically evolving (e.g. [ 63 ]); a similar argument applies for the belief network. This assumption does not diminish the value of this work as the emphasis here is on integrating the dynamical processes that drive the evolution of the organizational culture—yet providing an increasingly realistic picture of the network that these dynamics unravel upon adds a desirable layer of realism. Adaptive networks may serve as possible route for relaxing this restriction, where dynamics are coupled with the network topology, resulting in an adaptive topology which evolves over time—the work of [ 63 , 64 ] serve as notable examples of their application.

Another limitation stems from the integrative nature of the model, as it provides for a wider range of parameters that can potentially affect the outcome of the model. Specifically, consider the assumed structure of the two network (i.e. social and belief network)–even though they form reasonable approximations [ 52 ], the sensitivity of the dynamics in varying the initial parameters that dictate network characteristics remains unexplored. Future work could explore this aspect by considering various topologies (e.g. random, scale-free, core-periphery etc.) using a range of parameters to explore whether any significant differences emerge.

Managerial recommendations

Varying the hierarchical structure of an organization forms a reasonably form of intervention in an attempt to promote (or hinder) a given evolutionary trajectory of an organization’s risk culture. The influence of such intervention can be explored by varying the social rank distribution across the organization. As an example, consider the case where the distribution of social rank resembles a Normal distribution—in effect it implies that the majority of individuals have the same rank, with few deviating on higher and lower levels of hierarchy. In other words, it resembles a relatively ‘flat’ organization. In contrast, consider the case where social rank is distributed based on a Log-Normal distribution—in effect it suggests that the majority of individuals are found on the lower levels of the organization with a few being on much higher levels. In other words, it resembles an increasingly ‘vertical’ organization.

Generally, all three cases of hierarchical structure illustrate qualitatively similar behavior albeit being quantitatively different. In terms of network coherence, all three cases follow a similar, non-monotonic trend. Overall, the case of Log-Normal hierarchy results in reduced performance under both measures, with the Empirical and the Normal case being increasingly close in terms of both measures. In particular, both Normal and Empirical result in similar network coherence values, with this convergence breaking down at the latter stages of the simulation. At this point, the Normal case reaches the highest network coherence value, followed by the Empirical and the Log-Normal case—see Fig 8a . In terms of the average cognitive coherence, the Log-Normal case results to distinctively lower levels of average cognitive coherence, with both Empirical and Normal cases achieving increasingly similar levels ( Fig 8b ). As such, an increasingly ‘flat’ hierarchy promotes heightened levels of homogeneity and cognitive coherence in terms of belief exchange, while an increasingly ‘vertical’ organization hampers both cognitive coherence and network coherence, inevitably affecting the dissemination of beliefs.

The empirically-obtained hierarchical structure is included for reference (blue line). Results corresponds to the case of γ = 0.5.

https://doi.org/10.1371/journal.pone.0180193.g008

It is worth noting that the relevance of these results extends beyond the dissemination of risk-related cultural beliefs to the general dissemination of various quantities across an organization. For example, increased levels of both cognitive and network coherence are bound to increase the rate of information exchange and hence accelerate collective functions such as organizational learning. In such context, an increasingly ‘vertical’ organization hinders both individual coherence and network coherence, consequently hampering collective functions. Such evidence is consistent with recent empirical studies that highlight a negative relationship between increased organizational structure (in the form of hierarchical levels) and internal team learning [ 65 , 66 ], whilst providing a plausible mechanism that may be responsible for noting such effects.

In this paper, we have proposed an empirically-grounded, integrative model that was used to tackle the following previous assumptions; (a) belief independence; (b) increasingly context agnostic; by utilizing networks of beliefs and incorporating social rank (which is an important aspect in the context of organizations).

Thereby, results indicate that increased social conformity can be increasingly damaging to the evolution of organizational culture—a view consistent with past empirical work [ 61 ]. In the context of organizational hierarchy, a ‘flat’ organization outperforms a ‘vertical’ organizational structure in terms of culture coherence, benefiting related processes such as organizational learning. Such insight is consistent with recent empirical work [ 65 , 66 ] reinforcing the plausibility of the proposed model. By isolating the influence of peer-pressure and social rank, a disparity of scales, in terms of their influence, emerges, with peer-pressure having a greater impact on the macro scale (i.e. organization) while social rank has a stronger influence at the micro level (i.e. individual). As a result, future attempts focusing on the influence of social conformity to organizational behavior should follow similarly integrative approaches otherwise they risk missing the interplay of influence between the macro and micro organizational levels.

Supporting information

S1 file. additional supporting information..

Document contains additional clarifications and results. Table A. Central themes of survey . Spectrum of the six central themes on which the survey builds on. Figure A. Power rank histogram of individuals . Histogram of individuals’ power rank, which corresponds to their role and experience within the organization. Figure B. Additional results for entre range of γ . Additional results, with respect with respect to average cognitive coherence and network coherence, for the entire range of γ , γ ∈ [0,1].

https://doi.org/10.1371/journal.pone.0180193.s001

S1 Dataset. Survey responses.

Dataset containing survey answers used to initialize the model.

https://doi.org/10.1371/journal.pone.0180193.s002

Author Contributions

• Conceptualization: CE.
• Data curation: CE.
• Formal analysis: CE.
• Funding acquisition: CE NA AJ.
• Investigation: NA.
• Methodology: CE.
• Project administration: CE AJ NA.
• Resources: CE.
• Software: CE.
• Supervision: AJ NA.
• Visualization: CE.
• Writing – original draft: CE.
• Writing – review & editing: CE AJ NA.
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Organizational culture Organizational culture is embedded in the everyday working lives of all cultural members. Manifestations of cultures in organizations include formal practices (such as pay levels, structure of the HIERARCHY,JOB DESCRIPTIONS, and other written policies); informal practices (such as behavioral norms); the organizational stories employees tell to explain “how things are done around here;” RITUALS (such as Christmas parties and retirement dinners); humor (jokes about work and fellow employees); jargon (the special language of organizational initiates); and physical arrangements (including interior decor, dress norms, and architecture). Cultural manifestations also include values, sometimes referred to more abstractly as content themes. It is essential to distinguish values/content themes that are espoused by employees from values/content themes that are seen to be enacted in behavior. All of these cultural manifestations are interpreted, evaluated, and enacted in varying ways because cultural members have differing interests, experiences, responsibilities and values.

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Organizational culture, innovation and performance: a study from a non-western context

Journal of Management Development

ISSN : 0262-1711

Article publication date: 13 January 2020

Issue publication date: 13 October 2020

The purpose of this paper is to examine the links between organizational culture, innovation and banks’ performance in Palestine.

Design/methodology/approach

Data were gathered from 186 employees working in the Palestinian banking sector. The data gathered were analyzed using the PLS-SEM approach.

The findings of the study show that organizational culture and marketing innovation have a positive impact on banks’ performance. Moreover, it was found that marketing performance partially mediates the relationship between organizational culture and banks’ performance.

Practical implications

The paper may be of use for banks managers to create an organizational culture, which fosters both innovation and performance.

Originality/value

The paper is unique as it examines organizational culture, innovation and performance links in a non-western context.

• Performance
• Technological innovation

Organizational culture

• Marketing innovation

Aboramadan, M. , Albashiti, B. , Alharazin, H. and Zaidoune, S. (2020), "Organizational culture, innovation and performance: a study from a non-western context", Journal of Management Development , Vol. 39 No. 4, pp. 437-451. https://doi.org/10.1108/JMD-06-2019-0253

Emerald Publishing Limited

Copyright © 2020, Mohammed Aboramadan, Belal Albashiti, Hatem Alharazin and Souhaila Zaidoune

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

Introduction

Nowadays, organizations need to operate in business environments, which are characterized by fast technological changes, intensive international competition and continuous changing client’s preferences ( Droge et al. , 2008 ). Given these complexities, innovation is seen as one of the critical factors for achieving organizational success and sustaining competitive advantage ( Damanpour and Gopalakrishnan, 2001 ). It is well documented in the literature that innovative organizations have more flexibility and can respond quickly to changes, in order to take advantage of business opportunities ( Drucker, 1985 ). Innovation is considered as a competitive mechanism for organizations’ performance and success, and is regarded as an important instrument to adapt to a continuously changing business environment ( Blackwell, 2006 ). Furthermore, previous studies provide evidence that innovation can positively affect performance (e.g. Baker and Sinkula, 2002 ; Damanpour and Gopalakrishnan, 2001 ; Luk et al. , 2008 ; Naranjo-Valencia et al. , 2016 ; Uzkurt et al. , 2013 ).

Because of the critical role innovation plays in different organizational settings, several scholars have attempted to determine the factors that are associated with influencing innovation ( Koc and Ceylan, 2007 ). One of the factors that seems to have an impact on innovation is the organizational culture ( Büschgens et al. , 2013 ; Lin et al. , 2013 ; Martins and Terblanche, 2003 ; Tushman and O’Reilly, 1997 ).

On the other hand, organizational culture has been studied in terms of definitions, theoretical scopes, conceptualizations, characteristics and types (e.g. Lavine, 2014 ; Schein, 1996 ). Although organizational culture was argued to contribute to achieving common values promotion ( Naranjo-Valencia et al. , 2016 ), competitive advantage ( Calciolari et al. , 2018 ) desirables employees’ behaviors ( Nazarian et al. , 2017 ; Zhang and Li, 2016 ) and innovation ( Lin et al. , 2013 ), empirical support is still limited ( Hartnell et al. , 2011 ; Kim and Chang, 2019 ).

Regardless of the important role organizational culture plays in promoting innovation, most of the studies were carried out in western contexts. Moreover, a very limited number of studies examined the association between organizational culture and performance through the intervening mechanisms such as innovation (e.g. Martins and Terblanche, 2003 ; Naranjo-Valencia et al. , 2016 ; Uzkurt et al. , 2013 ).

Our study contributes to the literature in several ways. First, we attempt to investigate the “black box” of the organizational culture-performance relationship through the mediating effects of marketing and technology innovation. Based on a critical review of previous empirical studies, very limited research (e.g. Naranjo-Valencia et al. , 2016 ; Tseng et al. , 2008 ; Uzkurt et al. , 2013 ) examined the role of innovation as a mediator between organizational culture and performance. Second, our study responds to the different scholarly calls to advance empirical research on innovation and organizational culture ( McLaughlin et al. , 2008 ; Nakata and DiBenedetto, 2012 ; Tellis et al. , 2009 ). Finally, most of the studies examining organizational culture and performance were carried out in western setting. For instance, Budhwar et al. (2019) suggested that there is a need to enrich the literature of HRM and organizational behavior research in the Middle Eastern region. Among the suggestions made by Budhwar et al. (2019) was to investigate the mechanisms which govern the relationship between OB, HR factors and organizational performance. Given this discussion and to respond the scholarly calls to advance the organizational behavior and HR research in the Middle East, our study aims at investigating the relationship between organizational culture and banks performance via the mediating role of innovation. Moreover, we argue that more studies are needed in diverse non-western settings, in order to better understand the relationship between organizational culture and performance.

Theory and hypotheses

Organizational culture, definitions and models.

Chang and Lin (2007) consider culture as one of the vital factors for organizations and their activities. In literature, many definitions were given to organizational culture, each from a different perspective. Overall, organizational culture commonly represents the routine activities taking place in an organization ( Lundy and Cowling, 1996 ). More specifically, it refers to the shared set of values and behaviors inside an organization ( Deshpande and Webster, 1989 ). It is also used to describe the set of assumptions and behaviors employees within an organization have adopted ( Martins and Terblanche, 2003 ). Many researchers were interested in the field of organizational culture assuming it is a driving factor to the organization’s innovation, productivity and financial performance ( Blackwell, 2006 ).

Many studies were conducted to determine the different categories of organizational culture ( Blackwell, 2006 ; Martins and Terblanche, 2003 ). Some of them have considered that organizational culture can be divided into four categories, namely, clan, hierarchy, adhocracy and market ( Cameron and Freeman, 1991 ; Deshpande et al. , 1993 ; Quinn, 1988 ). Quinn and Spreitzer (1991) have suggested that organizational culture is composed of four different cultures: development culture, group culture, rational culture and hierarchal culture. Similarly, Chang and Lin (2007) believe that organizational culture follows the four concepts of: innovativeness, cooperativeness, effectiveness and consistency. In addition, Wallach (1983) suggested a simpler classification of the organizational culture following its functions: bureaucratic, innovative and supportive perspectives. A further classification for the culture was presented in the organizational culture profile suggesting that it is related to seven main values: innovation, aggressiveness, result orientation, stability, people orientation, team oriented and a detail focus culture. The organization’s culture can be also classified according to being a: service culture organization that focuses on providing the highest value to its customers, or a safety culture that focuses on having strong work-place standards, or both ( O’Reilly III et al. , 1991 ). Moreover, according to Robbins (2001) , characteristics like leadership, risk aversion, amount of detail, result focus, people focus, team focus, hostility and stability are the main characteristics of organizational culture.

Organizational culture and performance

Organizational culture has a significant impact on banks’ performance.

Innovation, on the other hand, is used to refer to new products, services, processes or technologies that require acceptance and eventually adoption and implementation ( Damanpour, 1991 ; Thompson, 1965 ; Zaltman et al. , 1973 ). Innovation is the factor that enables the innovative processes to produce new products and services, new technologies and new concepts ( Sutanto, 2017 ).

According to Padilla-Meléndez and Garrido-Moreno (2012) , knowledge of innovation needs more communication, and interaction between not only researchers, but also stakeholders affected by this, as well as, leaders. This way new ideas, processes and interactions can have an economic and commercial benefit. Hence, leaders, managers and researchers in organizations and universities should be aware of the different ways of innovation.

Innovation, in the literature, can be divided into different types. The most popular typology of innovation divides it into three types: “administrative vs technical,” “product vs process” and “radical vs incremental” ( Gopalakrishnan and Damanpour, 1997 ). Another classification of the typologies of innovation was developed by Jensen et al. (2007) . According to this classification, innovation can be classified as: “Science, Technology and Innovation” (STI) that is based on analytical knowledge and “Doing, Using, and Interaction” that is subject to knowledge retrieved from the engineering field ( Coenen and Asheim, 2006 ; Lorenz and Lundvall, 2006 ). Innovation can be divided into three groups: product-related, technology-related and behavior-related perspectives. The technology-related innovation is related to the readiness to adopt current technologies and processes and the tendency of the organization to adopt new technologies and processes internally ( Kitchell, 1995 ). Behavior-related innovation relates to the speed, at which the organizational system is ready to adopt new ideas relative to competitors ( Rogers, 1995 ). Lastly, product-related innovation is about the ability of an organization to generate new ideas, products, services and processes, or to buy them ( Stalk et al. , 1992 ). Moreover, as innovation is responsible for implementing totally new or ameliorated versions of products, services or processes within the organization, or in the external relations ( OECD and EUROSTAT, 2005 ), innovation can be classified into four categories. First, product innovation, which refers to the radical changes or ameliorations done to products and services. Second, process innovation, which refers to the major changes done to the production system or to the delivery mode. Third, organizational innovation, which refers to the adoption of new business processes that affect the business process within the organization and or on external relations. And fourth, marketing innovation, which refers to any change made to one of the four marketing Ps (product, price, placement and position) ( OECD and EUROSTAT, 2005 ).

Organizational culture and innovation

As innovation plays a significant role in determining an organization’s success, several studies attempted to examine its antecedences ( Crossan and Apaydin, 2010 ). Different studies found that organizational culture and organizational design are the most influential determinants ( Mumford, 2000 ).

Organizational culture can affect the innovative attitude in two ways. The socialization process teaches individuals how to behave and act toward one another. Moreover, the organization’s structure, policy system, procedure and management orientation can be affected by the basic “values, beliefs and assumptions” ( Martins and Terblanche, 2003 ). Hence, culture can encourage innovation among employees, because it drives them toward accepting innovation as a philosophy of the organization ( Hartmann, 2006 ). Different values of culture were regarded as means to foster innovation. Examples of these cultural values were creativity and initiative ( Jamrog et al. , 2006 ), entrepreneurial mindset ( McLean, 2005 ), freedom and autonomy ( Ahmed, 1998 ), risk taking ( Wallach, 1983 ), teamwork ( Arad et al. , 1997 ), marketing orientation and flexibility ( Martins and Terblanche, 2003 ).

Organizational culture has a significant impact on marketing innovation.

Organizational culture has a significant impact on technology innovation.

Innovation and performance

Research has found that innovation plays a significant role in organization performance ( Higgins, 1995 ; Hult et al. , 2004 ). Organizations able to innovate are more capable to deliver new products and services, improve processes in a faster way to fit the market’s needs and capitalize on opportunities better than non-innovative organizations ( Jiménez-Jiménez et al. , 2008 ). Moreover, innovation has been associated with higher levels of growth and profitability ( Li and Atuahene-Gima, 2001 ).

Marketing innovation has a significant impact on banks performance.

Technology innovation has a significant impact on banks performance.

The present study is a quantitative study applied to the Palestinian banking sector with the purpose of examining the hypothesized positive relationships between organizational culture, marketing innovation, technological innovation and banks’ performance. Data were gathered using a self-administered questionnaire distributed to the employees of banking sector located in Gaza strip. The distribution and collection method were the drop-off and pick up approach. A total of 320 employees were invited to fill the questionnaire. A total of 186 filled and usable questionnaires were gathered and valid for statistical analysis. The response rate in our study is 58 percent.

Respondents’ profile

Most of the respondents were male (70 percent). In total, 25.8 percent of the respondents were aged higher than 44 years, 25.8 percent were aged less than 30 years, 38.7 percent were aged from 30 to 38 years and 9.7 percent were aged from 38 to 44 years. Regarding experience, 32.3 percent had 5–10 years of experience, 16.1 percent had 10–15 years of experience, 22.6 percent had an experience of more than 15 years and 29 percent had less than 5 years of experience. Concerning education, most of the respondents had a bachelor’s degree (87.1 percent).

This scale is measured using 22 items adopted from previous studies, such as Claver et al. (1998) , Denison and Mishra (1995) , Jamrog et al. (2006) , McLean (2005) and Wallach (1983) . These items were “teamwork, communication, openness, work autonomy, commitment, employee’s involvement, flexibility, creativity, responsibility, objective orientation, customer focus, continuous learning, risk taking, adaptability, entrepreneurial mindset, performance incentives, excitement, work engagement, decision making, marketing orientation, and high standards and values.” The internal consistency was 0.956. A five-point Likert scale was used to assess the items of this construct.

Marketing and technology innovation

Marketing innovation and technological innovation were measured by a three-item scale for each. Both scales were adopted from Hogan et al. (2011) . A sample item for marketing innovation is “Our bank develops, revolutionary for the industry, marketing programs for our services/products” and a sample item for technology innovation is “Our bank adopts the latest technology in the industry.” The values of international consistency for marketing and technological innovation were 0.848 and 0.765, respectively. A five-point Likert scale was used to assess the items of these two constructs.

Banks’ performance

Respondents assessed this measure using a seven-item scale adopted from Agbényiga (2011) . Examples of this self-reported assessment were “effective services, customer satisfaction, organizational reputation, quality of the service.” The internal consistency value was 0.921. A five-point Likert scale was used to assess the items of this construct.

Initial analysis

Table I shows correlations and descriptive statistics of the research variables. The means and SDs for the examined variables were (Mean: 4.15, SD: 0.55) for organizational culture, (Mean: 4.44, SD: 0.48) for marketing innovation, (Mean: 4.56, SD: 0.45) for technology innovation, and (Mean: 4.30, SD: 0.60) for banks’ performance. According to the results, correlations were significant between marketing innovation, organizational culture and performance.

Assessing the measurement model

For the purpose of checking the internal consistency of the items, factor loading was calculated for each variable. Three items of organizational culture were removed from the model due to their low loading. All other items loadings were retained as their factor loading was higher than 0.5 as presented in Figure 1 . Furthermore, we have checked for the variables’ reliability by calculating the average variance extracted and composite reliability ( Hulland, 1999 ). As presented in Table II , AVE values for all variables were higher than 0.5 and CR values were higher than 0.7 ( Hulland, 1999 ). Hence, all variables in the model can be regarded as internally reliable and consistent.

For the purpose of examining discriminant validity, two approaches were utilized. First, the heterotrait–monotrait (HTMT) method was used, in which the results ( Table III ) show that HTMT values are lower than the value of 0.90, as suggested by Henseler et al. (2015) . The second method was the Fornell and Larcker (1981) technique by estimating the square root of the AVE and comparing it with the correlations between latent variables. The results in Table IV show that all square roots of the AVE are higher than the correlations between the examined variables. Hence, the discriminant validity condition was met.

Assessing the structural model

Table V shows that the R 2 values for banks’ performance and marketing innovation exceed the acceptable moderate ratio as suggested by Chin (1998) . Banks performance has an R 2 value of 0.561, marketing innovation an R 2 value of 0.112. Technological innovation had a week value of R 2 of 0.055. On the other hand, the effect size f ² for the research variables was also calculated. Results of f ² values presented in Table VI showed medium effects for the following relationships: organizational culture on performance, organizational culture on marketing innovation and marketing innovation on performance. On the contrary, the effect was week for the technological innovation and performance link.

Testing the hypotheses: direct and mediating effects

For the purpose of testing the research hypotheses H1 – H5 , we have calculated the direct effects. Table VII shows all the hypotheses were supported expect for H5 . Organizational culture is positively related to banks’ performance ( β =0.596, p =0.000). Organizational culture is positively related to both marketing innovation ( β =0.334, p =0.000) and technology innovation and ( β =0.234, p =0.000). Marketing innovation was found to exert a positive effect on performance ( β =0.297, p =0.000). The relationship between technology innovation and performance was not significant ( β =−0.001, p =0.982).

For the purpose of testing the mediating effects of both marketing and technology innovation, we have calculated the indirect effects. The results show that marketing innovation mediates the relationship between organizational culture and banks performance ( P =0.007, t =2.698***). Technology innovation did not exert a significant mediating effect between organizational culture and performance.

Discussion and implications

The purpose of our study was to examine the links between organizational culture, innovation and banks’ performance in a non-western context (Palestinian context). The findings of our study provide evidence for the relationship between organizational culture and banks performance, supporting H1 . The results of our study are in line with previous studies demonstrating a positive relationship between organizational culture and performance (e.g. Daft, 2007 ; Fey and Denison, 2003 ; Kim and Chang, 2019 ; Kraśnicka et al. , 2018 ; Ngo and Loi, 2008 ; Salimi and Aveh, 2016 ). The results imply that the values and philosophy adopted within Palestinian banks contribute positively to the banks performance.

Concerning the relationship between organizational culture and innovation, our results show that organizational culture is a significant predictor of both marketing and technology innovation at Palestinian banks, lending a support for H2 and H3 . The results are consistent with previous studies, which investigate organizational culture-innovation links ( Büschgens et al. , 2013 ; Chang and Lee, 2007 ; Lau and Ngo, 2004 ; Lin et al. , 2013 ; Miron et al. , 2004 ; Naranjo-Valencia et al. , 2016 ; Rezaei et al. , 2018 ; Tseng et al. , 2008 ; Uzkurt et al. , 2013 ). The results imply that organizational culture fosters both marketing and technology innovation.

Although our results provide empirical evidence on the links between marketing innovation and banks’ performance ( H4 ) and are in line with previous empirical support ( Afcha, 2011 ; Artz et al. , 2010 ; Baker and Sinkula, 2002 ; Damanpour, 1991 ; Farley et al. , 2008 ; Luk et al. , 2008 ; Tseng et al. , 2008 ), technology innovation did not exert any significant effect on banks performance, lending no support for H5 . These results can be justified by the fact that in a developing country like Palestine, technology-related innovation might not attract customers, due to the lack of culture and trust in using different technologies (ATM machines, online banking, etc.). This means that innovating at the technological level does not necessarily contribute to higher performance in the Palestinian banking sector.

Finally, our results show that marketing innovation plays an intervening role in the relationship between organizational culture and banks performance. Marketing innovation partially mediates this relationship, suggesting that organizational culture affects marketing innovation and marketing innovation, in turn, generates higher performance.

Implications

Our results contribute both to the theory and practice. Theoretically, the study is one of the very few studies conducted in a non-western context in the banking sector. In Middle Eastern region and specifically in Palestine, there is a lack of research on the culture-innovation-performance relationships.

Practically, our results provide useful recommendations to banks’ senior management on the significance of organizational culture and innovation and their contribution to performance. Our findings provide fertile grounds for the banking sector in Palestine on the importance of organizational culture as a tool for encouraging innovation and banks performance. The presence of a strong culture that is characterized by teamwork, communication, openness, work autonomy, commitment, employee’s involvement, flexibility, creativity, responsibility, etc., will positively contribute to innovation and firm performance alike. The existence of a climate that is characterized by objective orientation, customer focus, continuous learning, risk taking, adaptability, entrepreneurial mindset, performance incentives, excitement, work engagement, decision making, marketing orientation, and high standards and values, is of extreme importance to the firm success at different levels. Moreover, the results provide insights to the banking sector which is striving to be responsive to challenging environments through successfully adopting innovation.

The Palestinian banking sector encountered several environmental complexities in the last years, hence, innovation can be very useful in order to sustain competitive advantage. Managers in Palestinian banks should encourage their staff members to create innovative ideas and provide them the right reward to establish an innovative culture in the organization. Furthermore, communication between banks’ employees at the horizontal and vertical level can be very beneficial to find the best ways to implement innovation at different levels.

Limitations and future research

Like any other study, our study has some limitations. First, marketing innovation, technology innovation and banks’ performance were assessed by subjective measures. Future research might consider using more objective measures of innovation. Second, data were collected only from the Palestinian banking sector and this might restrict the generalizability of the results to other sectors. Hence, future research might replicate and extend this study to other sectors in Palestine and similar national contexts in the region such as Jordan and Lebanon. Future research using larger data and across different sectors will give more insights on the association between organizational culture and performance through innovation. Third, our research design does not allow the researchers to establish cause and effect links between the examined variables, hence, longitudinal research is recommended for future devours. In general, organizational culture research conducted using only quantities techniques provide restricted understanding. Hence, future studies might consider using qualitative methods to provide better explanation of the organizational culture, innovation and performance associations. Finally, our research analyzed only the role of marketing and technology innovation in the banking sector. Future studies might consider examining the role of other forms of innovation. Finally, it would be also interesting for future studies to investigate the different types of organizational culture and their impact on innovation and performance in the Middle Eastern region.

PLS measurement model analysis

Means, standard deviation and correlation matrix