An Inclusive Leadership Framework to Foster Employee Creativity in the Healthcare Sector: The Role of Psychological Safety and Polychronicity

Creativity at the level of employees is of utmost importance for every sector of an economy, with no exception to a healthcare system. The reason why employee creativity is important lies in the fact that employees have profound knowledge of their job and thus can serve as a source of meaningful innovation in an organization. Research shows that employee creativity is largely dependent on leadership. Corporate leaders significantly influence subordinates’ behavior. However, with the economic development, globalization, and changing business environment, a traditional authoritative leadership style can no longer be effective in understanding employees’ psychological needs to foster their creative behavior. In this regard, the role of inclusive leadership as an effective organizational management strategy was recently discussed in literature at different levels. It was also stated that an inclusive leader could foster employee creativity. However, such relationships in healthcare systems of developing economies have largely remained under-explored previously. We explored employee creativity in a healthcare context of a developing economy in an inclusive leadership framework to bridge such knowledge gaps. We also investigated the mediating roles of psychological safety and polychronicity in the above-stated relationship. We collected the data from hospital employees through a questionnaire (paper–pencil method). A hypothetical model was developed, which was tested through structural equation modeling in AMOS. Based upon the statistical outcomes, we found that an inclusive leadership style in a hospital can significantly foster employee creativity, whereas psychological safety and polychronicity mediate this relationship. This study offers different theoretical and practical insights, especially to a healthcare system. An important finding was that an inclusive leader can motivate the followers to be more creative. This finding is significant for a hospital because creative employees provide a hospital with a solid competitive base.

Introduction
In today's corporate environment, which is very competitive and challenging, creativity plays an important role in the success of an organization [1]. Creativity in the context of a workplace keeps an organization moving forward successfully. Innovation as an outcome of employee creativity is of high value for an organization as such innovation is meaningful because employees have a better knowledge of their workplace and job of how to perform their work in creative ways. Despite the fact that massive development in the global healthcare system has been evident during recent years, both national and global health systems are identified as "resource deficient" [2]. In this respect, healthcare systems around the globe are continuously trying to optimize their resources through process-driven advancements, standardized procedures, and innovations in order to reduce costs while improving the quality of services. Whether operating in public or private spheres, hospitals prioritize serving their patients in creative ways. To this end, from an efficiency and effectiveness perspective, employees of a healthcare organization can serve as a "valued source of creativity" [3] who can lead their organization towards success.
Similar to most businesses, hospitals also face a dynamic market environment characterized by constant change and erratic challenges [4]. To elucidate further, the healthcare sector faces a constant pressure situation that demands creative service delivery solutions to outperform the rivals on one hand and get an attractive place in the minds of clients (the patients) on the other hand. The above discussion clearly indicates the significance of seeking innovation, incrementally or radically, as an outcome of creativity on the part of employees. A review of related literature uncovers that most of the prior literature investigated the outcomes of employee creativity. For example, it was realized that employee creativity could boost the performance of an organization [5,6] or place an organization in a better competitive position [7]. From an economic perspective, the above studies were important. Nevertheless, we take a different position to advance the debate on employee creativity. That is, to answer what drives employee creativity in an organization? Though the early work identified some factors of employee creativity, for example, job autonomy [8], psychological capital [9], perceived organizational support [10], organizational culture [11], and leadership [12] may influence employee creativity. However, the inconsistent results indicate that a consensus has not yet been reached. Furthermore, employee creativity in healthcare did not receive due attention previously. Hence, there is a dire need to find out the factors that drive employee creativity in the healthcare segment. Therefore, one of the critical aims to carry out this study is to find out the factors that influence employee creativity.
Research shows that different factors influence individual behavior. It was mentioned that various organizational factors affect employee behavior in an organizational context. In this regard, it was realized that an effective leadership style, as an organizational factor, can drive employees' behavior in a workplace [13]. The role of inclusive leadership to foster employee creativity was recently discussed at different levels [14,15]. The greater focus of an inclusive leader on openness is something that places this leadership style at the heart of employee creativity [16]. Although the role of inclusive leadership is well discussed in literature from a perspective of employee creativity, surprisingly, the healthcare sector remained an understudied area. Given that employee creativity is a matter of prime concern to this sector, investigating the role of inclusive leadership to enhance employee creativity is worthwhile. Therefore, this study also aims to investigate the relationship between inclusive leadership and employee creativity in a healthcare context. In this regard, we propose the following hypothesis.

Hyphothesis 1.
An inclusive leader gives rise to employee creativity in an organization.
Another factor driving employee creativity in an organizational context is psychological safety (P.S) [17,18]. Although the concept of P.S existed in organizational science for a long time, empirical research in this domain has been flourishing recently [19]. Indeed, P.S is an employee's belief that he/she will not be punished or humiliated by others in a workplace for sharing different ideas, questions, or concerns [20]. Extending this discussion, we refer to a two-year report published by Google on discovering the most influential factors for a great team. The findings of this document were interesting as the most significant factor of a great team was not the individuals with the highest IQs or people with vast experience in a field. Rather, it was found that it is employees' perceptions that they work in a psychologically safe organizational environment. Another interesting finding of the survey was the team that made mistakes was more successful. The underlying reason for this result was to create an environment in which employees feel psychologically safe to take risks, which is critical to fostering creativity and innovation. It has been found that the support from a leader in an organizational context can stimulate subordinates' motivation to get engaged to show their creative potential [21]. An effective leader develops a work environment characterized by a high-quality leader-member exchange of relationships [22], empowers employees to think differently and builds trust [23]. This latter point (trust) is well discussed in the literature to shape employees' behavior, especially their creative behavior [24][25][26], highlighting that P.S has a clear role here. As an outcome of leadership support, this perception of working in a safe environment puts employees at ease to engage in creativity without fear [27]. Thus, an effective leadership style promotes P.S in an organization [28]. Though the literature argues about the positive link between P.S and employee creativity, its mediating role in the inclusive leadership framework was not realized, especially in healthcare. Hence, investigating the mediating role of P.S between inclusive leadership and employee creativity is another objective of this study. The above discussion may be summarized by proposing the following hypotheses.  Literature also highlights different personal factors to spur employee creativity in an organizational context [29,30]. In this vein, a personal factor that recently entered into the lexicon of individual creativity is polychronicity, an individual's preference for multi-tasking [31]. However, the mediating role of polychronicity for employees' behavior formation, especially their creative behavior, was not realized. We feel that polychronicity is related to one's preference for multi-tasking, which can further support an employee to be engaged in creative tasks. Literature shows polychronicity is largely influenced by culture [32,33] and the milieu in which employees interact with their workplace environment. It was also mentioned that the polychronicity of employees is influenced by different organizational factors, including leadership [34,35]. In a healthcare context, few studies investigated the direct impact of polychronicity on employee creativity [36]. Nevertheless, its mediating effect on the relationship of inclusive behavior and employee creativity was not discussed. Thus, the last objective of this study is to test the mediating role of polychronicity between inclusive leadership and employee creativity. In this respect, we propose the following hypotheses.  The target segment of this study was the healthcare sector of Pakistan, which is a developing nation. To represent the healthcare sector, we select hospital organizations that operate in the country with a mix of private and public. Considering the competitiveness in this sector [37] and changing preferences of patients, a hospital must differentiate itself meaningfully from. From this perspective, employee creativity could be a way forward for a hospital. Furthermore, every hospital's standard operating procedures are the same and are governed by a regulator [38]. Because these procedures to handle a patient are standardized, it implies that not much room is available for a hospital to differentiate itself from the rest of the crowd. Furthermore, the physical outlay of a hospital and the way services are delivered can also be imitated easily by a rival. Thus, finding a meaningful differentiation in this sector is a matter of concern for a hospital. The above situation clearly highlights the importance of employee creativity in this sector, as innovation as an outcome of employee creativity is hard to imitate because it is idiosyncratic in detail. Thus, creative employees can place a hospital in a better competitive position. To boost employee creativity in this sector, the role of leadership is important to create a workplace environment in which employees are motivated to show their creative potential. However, the role of inclusive leadership with this aspect, as an effective management strategy, was not investigated earlier in this sector.
The theoretical roots of this study are based on social exchange theory (SET) which was developed by Homans [39]. The early work of Choi et al. [40] and Qi et al. [15] also employed this theory to explain how the process of social exchange between an inclusive leader and a follower urges him to engage in different extra-role behaviors, including creative behavior. An inclusive leader not only encourages openness in a workplace, he/she also promotes a workplace culture of fairness, trust, respect, and collaborations with the followers. At the same time, such leaders also help their employees in situations that do not come under a formal contract of employees. When employees receive supporting and caring conduct from their inclusive leader, they are urged to provide extra support to their leader in the process of social exchange. Consequently, they engage in different extra-role behaviors, one of which is their creative potential. The theoretical framework of this study is given in Figure 1.
itself meaningfully from. From this perspective, employee creativity could be a way forward for a hospital. Furthermore, every hospital's standard operating procedures are the same and are governed by a regulator [38]. Because these procedures to handle a patient are standardized, it implies that not much room is available for a hospital to differentiate itself from the rest of the crowd. Furthermore, the physical outlay of a hospital and the way services are delivered can also be imitated easily by a rival. Thus, finding a meaningful differentiation in this sector is a matter of concern for a hospital. The above situation clearly highlights the importance of employee creativity in this sector, as innovation as an outcome of employee creativity is hard to imitate because it is idiosyncratic in detail. Thus, creative employees can place a hospital in a better competitive position. To boost employee creativity in this sector, the role of leadership is important to create a workplace environment in which employees are motivated to show their creative potential. However, the role of inclusive leadership with this aspect, as an effective management strategy, was not investigated earlier in this sector.
The theoretical roots of this study are based on social exchange theory (SET) which was developed by Homans [39]. The early work of Choi et al. [40] and Qi et al. [15] also employed this theory to explain how the process of social exchange between an inclusive leader and a follower urges him to engage in different extra-role behaviors, including creative behavior. An inclusive leader not only encourages openness in a workplace, he/she also promotes a workplace culture of fairness, trust, respect, and collaborations with the followers. At the same time, such leaders also help their employees in situations that do not come under a formal contract of employees. When employees receive supporting and caring conduct from their inclusive leader, they are urged to provide extra support to their leader in the process of social exchange. Consequently, they engage in different extra-role behaviors, one of which is their creative potential. The theoretical framework of this study is given in Figure 1.

Participants and Procedure
The healthcare sector of Pakistan was considered for the hypothetical framework of this study. The country's healthcare system is a mix of different players, including public, private, charity-based contributors, parastatal, etc. There are four major modes of healthcare delivery in Pakistan: preventive, curative, promotive, and rehabilitative healthcare services. Approximately eighty percent of the country's population is attended

Participants and Procedure
The healthcare sector of Pakistan was considered for the hypothetical framework of this study. The country's healthcare system is a mix of different players, including public, private, charity-based contributors, parastatal, etc. There are four major modes of healthcare delivery in Pakistan: preventive, curative, promotive, and rehabilitative healthcare services. Approximately eighty percent of the country's population is attended by the private sector [41]. Currently, the provincial government of each province is constitutionally responsible for regulating the healthcare system and structure in a province except the territory administered by the Federal government. The Ministry of National Regulation and Services is a body that sets the policy guidelines of the healthcare system in the country. Nevertheless, the operationalization of the guidelines provided by the Ministry of National Regulation and Services lies with the provincial governments. Large cities of Pakistan, especially Lahore and Karachi, are identified as the two dominant cities where many hospitals exist (both public and private). Moreover, these two cities comprise a multi-million population whose health delivery is reliant on these hospitals. Furthermore, with the rising competitive norms in the healthcare industry, especially in private hospitals, a hospital needs to base its competitive position on a stable foundation, for which creative employees are critical.
Given that Lahore and Karachi constitute a large umbrella of hospitals, we selected these two cities for the purpose of data collection. In this respect, different hospitals were contacted to facilitate the data collection process in the larger interest of industry and academia. We then approached hospitals with a positive response to start the data collection activity. A total of six hospitals were included in the finalized sample (three from each city).
We, prior to producing the final version of the data collection instrument (a selfadministered questionnaire), requested the field experts to assess the statements of our questionnaire for their suitability and appropriateness. The significance of this step is endorsed by various researchers previously [42,43]. This expert opinion led us to produce the finalized version of our instrument, which was then presented to each informant [44][45][46]. The employees serving in these hospitals were invited to partake in the current survey on a voluntary basis. Indeed, employees from different departments and fields were included in the current survey. To observe the ethical guidelines, we followed the Helsinki Declaration's protocols [47,48]. In this regard, the anonymity of each informant was assured, and each informant was served with informed consent to partake in this survey. Furthermore, the quitting from this survey was also allowed if an informant was uncomfortable disclosing the information at any stage in filling the responses on the questionnaire.

Instrument
An adapted questionnaire was considered to collect the data from informants on a seven-point Likert scale. We employed a paper-pencil survey methodology to receive the responses. Generally, the questionnaire included two major sections. The demographic information was collected in the first section, whereas the variable-related information was the subject of the second part. A three-wave data collection procedure was applied in this vein. A time interval of three weeks was maintained for each wave. To elucidate further, in the first wave, the demographic information of the informants was obtained. The information for P.S and employees' perceptions of inclusive leadership (InL) was also obtained in this phase. Employees with managerial ranks or leadership positions were approached in the second wave to share their perception about a subordinate's creative behavior. Lastly, the data for polychronicity were collected in the third wave. The early researchers in the field also found this multi-wave data collection strategy as an effective strategy to deal with informants' fatigue and to avoid the issue of common method variance (CMV), which is a largely reported issue in a survey in which all information was collected from a single source [49].

Measures
To measure the variables of this study, we adapted the already existing scales from different published sources. For example, nine items to measure InL were adapted from Carmeli et al. [50]. A sampled item from this scale was "Our leader/manager is open to discuss the desired goals and new ways to achieve them". A reliability value (α) of 0.922 was obtained for this scale. In the same vein, we adapted five items of P.S from the study of Edmondson [51]. A significant α = 0.855 was observed in this case. A sample item was "No one in this hospital would deliberately act in a way that undermines my efforts". The scale of employee creativity was adapted from the stud of Coelho and Augusto [52], which included five items. One item of this scale was "This person experiments with new approaches in performing his/her job". The overall α = 0.868 showed a significant value.
Lastly, the scale of polychronicity was adapted from Lindquist and Kaufman-Scarborough [53], which included five items with α = 0.869. One particular item was "I prefer to do two or more activities at the same time". For more details on the items of this survey, Appendix A can be seen. Initially, we distributed 600 surveys to the employees of the selected hospitals who responded with 61% (n = 366). For more descriptive detail, Table 1 can be seen. The data were collected between September to November 2021.

Non-Response Bias and Common Latent Factor Test
To assess, if the issue of nonresponse bias exists, we compared the informants who provided full information with the informants who did not provide the full information. It was realized that no significant observable discrepancy has existed, implying that a non-response bias was not a matter of concern. Similarly, though the data were collected from multiple sources in different intervals, we still performed a common latent factor (CLF) test to verify the non-existence of CMV. For this purpose, we drew a measured model in AMOS, which was then compared with another alternate measured model (this model includes a CLF). It was observed that neither a CLF model explained a sheer amount of total variance (more than 50%), nor any significant difference between the standardized factor loadings (>0.2) between the two models existed. These results were enough to confirm that a CMV was not a critical issue in this work which requires any measures to address this issue.

Establishing Validity and Reliability
To establish the validity and reliability of the variables in this work, we first of all checked the standardized factor loadings (λ) of each item (InL = 9, employee creativity = 5, P.S = 5, polychronicity = 5). Usually, a λ-value > 0.5 is considered good; however, values beyond 0.7 are desirable. Table 2 shows the results of factor loadings along with other values. It can be seen that all λ-values were positive and significant. This implies that all the items showed a good λ-value. We then used these λ-values to calculate each variable's average-variance-extracted (A.V.E) value. Generally, an A.V.E value > 0.5 for a variable indicates a good convergent validity. It was realized that the A.V.Es for all variables were positive and beyond the standard value of 0.5 (A.V.E for InL = 0.597, E.C = 0.559, P.S = 0.607, and PoL = 0.592). These results clearly indicate that the convergent validity was established in every case, and all the items of one variable were converging on it. Thus, the case of convergent validity was well supported by the statistical findings of the current dataset. Moving forward in the process of construct evaluation, we also assessed the candidature of each variable to prove its composite reliability (C.R). To this aspect, we again considered λ-values to calculate C.R value of each variable. A C.R value not less than 0.7 is normally considered a significant value. In the current case (Table 2), all C.R values were above 0.7, which implies that these values were significant (C.R for InL = 0.930, E.C = 0.864, P.S = 0.865, and PoL = 0.878).

Correlations and Divergent Validity
The validation of variables through A.V.E and C.R values led us to move forward in the process of data analysis. Therefore, we performed a correlation analysis in order to see the value and direction of correlation between different pairs of variables. Table 3 shows the results of correlations. According to these results, a positive and significant correlation was observed between different pairs. To explain further, it can be seen that the pair of InL and employee creativity

Correlations and Divergent Validity
The validation of variables through A.V.E and C.R values led us to move forward in the process of data analysis. Therefore, we performed a correlation analysis in order to see the value and direction of correlation between different pairs of variables. Table 3 shows the results of correlations. According to these results, a positive and significant correlation was observed between different pairs. To explain further, it can be seen that the pair of InL and employee creativity ꟷE.C showed a positive and significant correlation value (r = 0.489, p < 0.01). This positive association indicates that these variables co-vary in a positive direction with each other. A similar case can be seen in all other pairs (Table 3). All this implies that correlations were all significant in every case. Likewise, we also tested the divergent validity of all of our studied variables. In doing so, we first calculated the square root of A.V.E (sqA.V.E) of each variable which was then compared with the correlational values. A positive case of divergent validity occurs when the sqA.V.E value of a variable is superior to the correlational values in comparison. Put simply, one could see that the E.C showed a positive and significant correlation value (r = 0.489, p < 0.01). This positive association indicates that these variables co-vary in a positive direction with each other. A similar case can be seen in all other pairs (Table 3). All this implies that correlations were all significant in every case. Likewise, we also tested the divergent validity of all of our studied variables. In doing so, we first calculated the square root of A.V.E (sqA.V.E) of each variable which was then compared with the correlational values. A positive case of divergent validity occurs when the sqA.V.E value of a variable is superior to the correlational values in comparison. Put simply, one could see that the sqA.V.E of InL was 0.773 which was superior to the correlational values (InL ⇔ E.C = 0.489; InL ⇔ P.S = 0.416; and InL ⇔ PoL = 0.278). Similarly, a divergent validity was confirmed for all other variables. Lastly, different measurement models were developed in AMOS compared with the hypothesized model (4-factor). It was revealed that the hypothesized model was the most significant compared to the alternate models. These results are presented in Table 4.

Hypotheses Validation
In the last phase of the data analysis, we tested the hypothetical relationships by employing the structural equation modeling technique (SEM) for which we used AMOS software. As an advanced level technique to analyze the complex models, the data scientists have largely considered this technique, especially to analyze complex models (a model which involves multiple mediations, moderations or both). As a second-generation data analysis tool, SEM provides data analysis with a flexible environment with several advanced features which were not available in traditional regression analysis. To proceed with SEM, we drew a structural model twice. Firstly, the structural model was drawn to observe the direct effects without any inclusion of mediator(s) in this model. This was carried out to see the results of H1, H2, and H4. Table 5 shows the output of this structural model (direct effect model). These results explicitly state that H1, H2, and H4 were statistically valid. For example, the purpose of H1 was to establish a positive link between inclusive leadership and employee creativity. In this regard, we evaluated the results of Tables 3 and 5. The result of Table 3 showed a positive correlation between an inclusive leader and employee creativity (InL ⇔ E.C = 0.489). Likewise, the regression weight in Table 5 indicates a positive change in employee creativity due to a change in inclusive leadership (beta value

Correlations and Divergent Validity
The validation of variables through A.V.E and C.R values led us to move forward in the process of data analysis. Therefore, we performed a correlation analysis in order to see the value and direction of correlation between different pairs of variables. Table 3 shows the results of correlations. According to these results, a positive and significant correlation was observed between different pairs. To explain further, it can be seen that the pair of InL and employee creativity ꟷE.C showed a positive and significant correlation value (r = 0.489, p < 0.01). This positive association indicates that these variables co-vary in a positive direction with each other. A similar case can be seen in all other pairs (Table 3). All this implies that correlations were all significant in every case. Likewise, we also tested the divergent validity of all of our studied variables. In doing so, we first calculated the square root of A.V.E (sqA.V.E) of each variable which was then compared with the correlational values. A positive case of divergent validity occurs when the sqA.V.E value of a variable is superior to the correlational values in comparison. Put simply, one could see that the sqA.V.E of InL was 0.773 which was superior to the correlational values (InL ⇔ E.C = 0.489; InL ⇔ P.S = 0.416; and InL ⇔ PoL = 0.278). Similarly, a divergent validity was confirmed for all other variables. Lastly, different measurement models were developed in AMOS compared with the hypothesized model (4-factor). It was revealed that the hypothesized model was the most significant compared to the alternate models. These results are β1 = 0.476; CR = 15.305; p < 0.01). These results provided the needed statistical evidence to accept H1 of this work. Thus, it can be stated in the light of the statistical results that in the presence of an inclusive leader, employees are motivated to be engaged in creativity. The same kind of interpretation can be repeated to arrive at the conclusion that H1, H2, and H4 were statistically significant and hence were accepted. Secondly, we re-drew the structural model; nevertheless, this time P.S and PoL were included in the model as mediators. To see the significance of mediation, we enabled the bootstrapping option in AMOS. In this vein, we selected a larger bootstrapping sample (2000) to see the mediation effect on employee creativity. At the same time, a biased corrected 95% confidence interval (CI) was also employed in this process. The results of mediation analysis have been reported in Table 6. It was noted that both psychological safety and polychronicity mediated between inclusive leadership and employee creativity (H3: E.C ← P.S ← InL: β3 = 0.211, Z-value = 10.655, p < 0.01; H5: E.C ← PoL ← InL: β5 = 0.173, Z-value = 07.208, p < 0.01). Hence, as per the statistical findings, we confirm a mediation role of psychological safety and polychronicity between inclusive leadership and employee creativity. Thus, H3 and H5 were also accepted. Furthermore, the mediation effect explained almost 39% of the change in employee creativity.

Discussion and Implications
Our research contributes to existing knowledge by filling the following knowledge gaps. First, this study is among the few which approach creativity from an individual perspective. To this end, most of the early work in innovation and creativity was conducted at an organizational level. This line of reasoning is also endorsed in the work of Slåttenetal. [54]. Considering the seminal role of employees in the success of an organization, it was important to highlight their creative role well to position a hospital in the face of competition. A second implication is the consideration of this study for P.S and polychronicity as mediators in a unified model. Although prior literature has discussed the mediating role of P.S in a leadership framework, nevertheless, as per our knowledge, the mediating effects of P.S and polychronicity were not highlighted. Third, this study intends to enrich the field of leadership and organizational management from a healthcare context of Pakistan, a developing economy. To this end, a large body of previous knowledge focused on developed countries or non-healthcare contexts. Considering the culture and context specificity of leadership, it was important to carry out more work in a developing context, rather than trying to generalize the context or culture of developed countries in developing countries.
Furthermore, this research helps the healthcare sector of Pakistan in different ways. For instance, this research study tends to place a hospital in a solid competitive position by engaging its employees in creativity as an outcome of an inclusive leadership style. This implication has special relevance to a healthcare system of a country. Considering the isomorphism in the physical outlay of hospitals and standard operating procedures, it is very challenging for a hospital to find a solid base of competitive advantage because isomorphism in the above factors leads hospitals toward competitive convergence (a situation where all players have access to the same resources). In this situation, employee creativity could be a way forward for this sector, at least for two specific reasons.
For example, innovations derived from employee creativity are hard to imitate because such innovation is idiosyncratic in detail.
Another important implication of our study to the field is that it highlights the mediation mechanism of P.S and polychronicity between the relationship of inclusive leadership and employee creativity. Given that, for employees to be creative, they need to build a perception of a safe environment to work. This perception to be safe in a workplace is very important from a creativity aspect because when employees see their workplace as safe, they work without the fear of failure. Working without the fear of failure is central to employee creativity. However, to develop this safety perception among employees, the role of leadership is very important. A leader, especially an inclusive leader, plays a seminal role in creating a workplace environment that is perceived as psychologically safe by employees. As stated earlier, an inclusive leader builds trust, collaboration, and fairness with subordinates. All these factors give rise to their perceptions of working in a safe environment, which eventually influences their creative behavior. In the same manner, polychronicity is very important from the perspective of employee creativity. Importantly, in a healthcare context, where the working environment is dynamic, there is a role of employee polychronicity to be creative. An inclusive leader has a clear role in fostering employee polychronicity, which then guides employee creativity. Therefore, to deal with a competitive environment, a hospital needs to understand the seminal role of leadership style for organizational management.

Way Forward for Future Studies
This study faces some limitations; however, we feel that these limitations also serve as the base for future studies. First, this study was carried out in two large Pakistan cities, which makes this work's generalizability a bit weaker. In this respect, it is suggested to consider more cities from other regions of Pakistan. Second, due to different policy reasons, hospitals did not share any list of employees with us. Therefore, it was hard to apply a probability sampling technique that is considered superior to a non-probability sampling technique (a case with this study). Therefore, in future studies, it is suggested to adopt a probability sampling technique, if possible. Third, the nature of the data was crosssectional, which limits the causality of relationships. Although the proposed relationships were significant, we still suggest that future research studies consider a longitudinal data design.

Conclusions
The significance of employee creativity has become an important business imperative for all sectors in the current era. Employees capable of developing new ideas are the demand of every contemporary organization, with no exception of a healthcare system. To deal with a changing business environment in the face of competition, promoting employee creativity at all levels in an organization is important. Especially in a healthcare context, which is already identified as a sector with insufficient resources, it is important for a hospital to base its competitiveness on employee creativity, which is meaningful and effective. In this vein, hospital management can benefit from the potential role of an inclusive leadership strategy. An inclusive leader, on the one hand, manages organizational resources effectively, and on the other hand, he/she also promotes employee creativity. Not only is the role of an inclusive leader important for employee creativity, but his/her role is also important to foster P.S and polychronicity in a workplace. To sum up, it is suggested in the light of the above scholarly debate that for effective organization management and to promote creativity at the level of employees, hospital management needs to pay a special focus on inclusive leadership style. To achieve this, we suggest that hospitals develop different training programs on a managerial level with a central focus on inclusiveness. Informed Consent Statement: Informed consent was obtained from each respondent.
Data Availability Statement: Data may be provided on a reasonable request by contacting the corresponding authors.

Conflicts of Interest:
The authors declare no conflict of interest.

Inclusive Leadership
Our manager is open to hearing new ideas. Our manager is attentive to new opportunities to improve work processes.
Our manager is open to discuss the desired goals and new ways to achieve them. Our manager is available for consultation on problems. Our manager is an ongoing 'presence' in this team-someone who is readily available. Our manager is available for professional questions I would like to consult with him/her. Our manager is ready to listen to my requests.
Creativity Employees here try to be as creative as they can. Employees experiment with new approaches in performing their job. When new trends develop, employees are usually the first to get on board. Employees think creatively in performing their job. Employees are inventive in overcoming barriers.
Polychronicity I prefer to do two or more activities at the same time. I typically do two or more activities at the same time. Doing two or more activities at the same time is the most efficient way to use my time. I am comfortable doing more than one activity at the same time. I like to juggle two or more activities at the same time.
Psychological Safety I am able to bring up problems and tough issues. It is safe to take a risk in this hosptial.


Simulation and identification of foodborne outbreaks in a large supermarket consumer purchase dataset

Foodborne outbreaks represent a significant public health burden. Outbreak investigations are often challenging and time-consuming, and most outbreak vehicles remain unidentified. The development of alternative investigative strategies is therefore needed. Automated analysis of Consumer Purchase Data (CPD) gathered by retailers represents one such alternative strategy. CPD-aided investigations do not require trawling questionnaires to create a hypothesis and can provide analytical measures of association by direct data analysis. Here, we used anonymized CPD from 920,384 customers enrolled in Norway’s largest supermarket loyalty program to simulate foodborne outbreaks across a range of different parameters and scenarios. We then applied a logistic regression model to calculate an odds ratio for each of the different possible food vehicles. By this method, we were able to identify outbreak vehicles with a 90% accuracy within a median of 6 recorded case-patients. The outbreak vehicle identification rate declined significantly when using data from only one of two retailers involved in a simulated outbreak. Performance was also reduced in simulations that restricted analysis from product ID to the product group levels accessible by trawling questionnaires. Our results show that—assuming agreements are in place with major retailers—CPD collection and analysis can solve foodborne outbreaks originating from supermarkets both more rapidly and accurately than than questionnaire-based methods and might provide a significant enhancement to current outbreak investigation methods.

www.nature.com/scientificreports/ peer-reviewed papers describing CPD-assisted foodborne outbreak investigations between 2003 and October 2017 3 . In the reviewed investigations, CPD was generally used after interviews and questionnaires failed to yield sufficient hypotheses, with varying degrees of success. A search of articles published between October 2017-October 2021 using the same keywords as in 3 yielded two additional articles detailing the use of CPD for outbreak investigation. In one, loyalty card information of a single listeriosis patient in Finland was used along with sampling of the patient's freezer to identify the source of contaminated meatballs 5 . In the second article, CPD was used in Ireland to identify the outbreak vehicle in an outbreak of shigellosis originating from a product sold in a restaurant chain by calculating relative attack rates 6 . Of the combined 22 articles reviewed, three used CPD to generate candidate outbreak vehicles with an analytical measure of association [6][7][8] . Recently, we also summarized a common framework for using and reporting CPD in outbreak investigations 9 , which may aid investigators in future outbreaks. CPD provides several potential advantages for automating outbreak investigations. If agreements are in place with database providers, CPD can in theory quickly be accessed and used to generate outbreak vehicle hypotheses. Secondly, they may be used to estimate the strength of the association between a potential vehicle and outbreak cases. However, not all personal consumption will be recorded in CPD; a sandwich bought at a food stand, dinner at a friend's house, or purchases made by a spouse will not be recorded. Additionally, there may be barriers, legal or otherwise, that might complicate CPD usage 3 .
The work presented here was conducted to assess the feasibility of using CPD from supermarket chains to identify the vehicles of outbreaks. Here, we simulate outbreaks in a large, real-world consumer purchase dataset, and develop a model for identifying outbreak vehicles using an automated logistic regression procedure.

Methods
Dataset. Our CPD was obtained from Norgesgruppen, Norway's largest grocery wholesaler with a 43.7% market share in 2019 10 . Norgesgruppen also runs Trumf, the largest customer loyalty program in Norway with almost 2.5 million registered customers 11 , recording all purchases made by Trumf-registered customers. We were generously granted access to an anonymized excerpt of CPD's containing records of purchases of consumable items shops in 6 different municipalities in 2019. For our analyses, we partially collapsed the dataset to include unique item purchases per week per customer only, resulting in a dataset with more than 222 million recorded purchases of 30.929 items by 920.384 registered customers. The dataset does not contain batch numbers or expiry dates, as these are not included in Norgesgruppen's records.
We were also given access to an item database with information on all listed items. Each product was categorized at two hierarchical levels of detail: primary (408 categories), and secondary (1043 categories), in addition to the actual product ID (tertiary). For example, a particular brand of blueberry would reside in the primary group "berries" and the secondary group "blueberries". Implementation. Our model was implemented in the R software programming language 12 , and the statistical model built using the package glmnet 13 . All graphs were produced using package ggplot2 14 .
Outbreak simulation. Because data were anonymized, it was not possible to perform a controlled statistical study on notified outbreaks in Norway. Instead, we simulated realistic outbreaks from the consumer purchase dataset. We first considered outbreak vehicles sold in a single supermarket chain and then expanded the model to cover two-supermarket chain-outbreaks.
To simulate outbreaks, we specified outbreak vehicles in addition to the following parameters: pathogen incubation time, delay parameter (defining the time between purchase and consumption), attack rate, background case proportion (proportion of total cases without a CPD-registered purchase of the outbreak vehicle), window size (how many days of CPD history are used for analysis dating back from case registration), outbreak start date, and outbreak duration.
Outbreak vehicles were randomly selected from the list of items which accumulated ≥ 500 registered purchases in 2019. This condition was applied since very rarely purchased items are highly unlikely to generate outbreaks with ≥ 5 cases. This left a pool of 12,191 possible outbreak vehicles. Flowcharts representing the outbreak simulation algorithm are shown in Fig. 1.
Two types of simulated outbreak cases were generated; cases based on purchase history, and cases based on incomplete CPD coverage (explained below). These cases generated from purchase history were termed purchase cases. Shown in green in Fig. 1, purchase cases were randomly drawn with probability given by the attack rate from the CPD population who purchased the outbreak vehicle within the defined outbreak timeframe. A detection date was also generated for each case, defined as the date they were registered as falling ill from the outbreak. Detection dates were generated as purchasedate + delay , where the delay was drawn from a beta distribution Beta(α = 2, β = 10) and multiplied by a delay parameter d representing the incubation time and outbreak vehicle shelf-life. The mean value of this beta distribution is 0.167 and has a long right-hand tail, modelling that while most consumers quickly consume their purchased items, some items might be stored for a longer time. Finally, complete CPD records for each case were retrieved, dating back the number of days given by the window size parameter. Outbreak simulation is described in greater detail in the Supplementary Methods.
Simulating incomplete CPD coverage. In any realistic outbreak scenario, some outbreak cases will not have any registered purchases of the outbreak vehicle. For this reason, a second type of cases were also randomly drawn during outbreak simulation: background cases. These background cases represented people who had no registered purchases of the outbreak vehicle but still fell ill from consuming it (such as by consuming the vehicle away from home). www.nature.com/scientificreports/ Background cases were generated differently in two scenarios. In the first scenario, we imagine the outbreak vehicle having been sold in a single supermarket chain, while in the second scenario it would be sold in two different supermarket chains, each with their own separate CPD. In the single-chain outbreak scenario, the number of background cases was generated as a proportion of the total number of cases; if the background case proportion parameter was 0.2, then 80% of cases would be purchase cases and 20% will be background cases. Customers with fewer registered purchases were also assumed to have more unrecorded consumption, and thus an increased chance of being a background case. Hence, the chance of being randomly selected as a background case was inversely proportional to the number of weeks with registered purchases for each customer (Fig. 1a, yellow boxes).
For the two-chain outbreak scenario, we made two alterations (highlighted in red in Fig. 1b). First, we modeled customers with purchases from both supermarket chains by combining CPD profiles pairwise, where one of the sub-profiles represented CPD from supermarket chain A, and the other sub-profile represented CPD from chain B.
For this, 25% of profiles registered in the dataset were randomly selected. Next, each of these profiles were matched with a different profile in the remaining dataset with the constraint that their combined profile had registered purchases in 80-120% of weeks (weeks with purchases from both chains allow for > 100%). This ensured that profiles contain a reasonable purchase record. For example, if a customer shops 60% of weeks at the primary supermarket chain, it seems likely they might shop 20-60% of weeks at a different supermarket chain. The unmatched CPD profiles were discarded in this simulation.
After profile merging, outbreak simulation was performed as described above, generating purchase cases and background cases. However, instead of returning the complete purchase records of all case profiles, one of the subprofiles was randomly selected and returned from each merged profile. This modeled a situation where we only have access to CPD from one of the two grocery chains. For comparison we ran a parallel simulation where the entire merged profile's purchase records were returned for analysis, simulating access to CPD from both chains.

Outbreak analysis.
After simulating outbreaks, we used logistic regression to generate odds ratios (OR) for each purchased item. The algorithm iterated chronologically through the outbreak period, analyzing newly arisen cases in order of detection date. This approach is shown in Fig. 2.
Once the number of cases exceeded five, logistic regression was initiated using item purchases as covariates. We did not attempt to solve outbreaks with less than five cases. As controls, we used the purchase histories of four randomly selected customers for each registered case. Items purchased by the recorded cases were used as regression covariates and analyzed as binomial data (1 if the covariate item was purchased by the customer, and 0 if not). Each customer's purchases in the logistic regression were weighted by their percentage of weeks with  www.nature.com/scientificreports/ registered purchases; again, loyal customers would be less likely to have consumed the outbreak vehicle outside of their CPD records. This process continued either until an item reached a pre-determined OR value, the algorithm iterated through the entire outbreak period, or more than 200 cases are reached (the latter condition set due to computational limitations and only rarely encountered). In the former situation, the significantly identified item was returned as the presumed outbreak vehicle, and in the latter two situations, the algorithm returned the message that the analysis was inconclusive.
Outbreak simulation and analysis was performed across a range of important outbreak parameters; background proportion, attack rate, delay parameter (expiry date), and item purchase frequency. Each parameter was subjected to a sensitivity analysis while holding other parameters constant at default values. For each parameter value tested, 300 outbreaks were simulated using 300 randomly chosen items, and performance was quantified according to the mean outbreak vehicle ID rate and the median number of cases required to solve outbreaks. Analysis sensitivity to item detail level. In this section, we sought to identify the food category of outbreak vehicles based on item categories instead of item ID. One problem encountered by questionnaire-based identification methods is that they can only ask a limited number of questions, and hence operate on item categories instead of the actual consumed items. For example, the CDC national hypothesis generating questionnaire for foodborne outbreaks queries patients on their consumption of 166 different item categories such as "peperoni" and "cabbage" 15 . In contrast, our CPD-based approach has access to the specific brand of peperoni a patient purchased.
To compare our model to such a questionnaire-based approach, we used the hierarchical primary and secondary categories in addition to the actual item IDs (tertiary category). We first generated outbreaks using the single-chain outbreak simulation as before, and then ran three separate regression analyses: one with all item IDs replaced with their primary level classification (408 categories), another with secondary level classifications (1043 categories), and one with purchases classified at the tertiary level (30,929 product IDs). For the primary and secondary level classification analyses, the outbreak vehicle identification regression method returned the respective item classification level instead of product IDs.

Results
Description of data. A summary of the consumer purchase database used for this study is given in Table 1.
The CPD was unevenly distributed both with respect to customer purchase frequency and item purchase frequency. The median item was purchased 176 times in 2019, and half of all customers made purchases in fewer than 10 separate weeks of 2019. Cumulative distribution plots of customers by number of weeks with registered purchases, and of items by number of purchases are provided in Supplementary Fig. S1.  Fig. 1a). As an example, one such outbreak where the vehicle was frozen chicken is illustrated in Fig. 3. We then attempted to identify the outbreak vehicle using the logistic regression algorithm of Fig. 2. Outbreak vehicle identification for the chicken example is illustrated in Fig. 3b and shows the epidemic curve of the simulated outbreak along with the top 5 item hits generated.
As Fig. 3 shows, frozen chicken was successfully identified as the outbreak vehicle after 12 cases were registered, exceeding the OR threshold of 50.
Default parameter values. Outbreaks were simulated across a range of parameters by conducting sensitivity analyses while holding other parameters constant at default values. These default parameter values are shown in Table 2 along with the ranges tested during sensitivity analysis.
In addition to the outbreak simulation parameters, two outbreak analysis parameters were found to impact model performance: the OR threshold and the window size. These parameters were tested across a range of values to optimize performance. The effect of window size is shown in Supplementary Fig. S2 while that of the OR threshold is shown in Supplementary Fig. S3. A window size of 40 days was found to optimize the ID rate for default outbreak parameters. Although this parameter will be somewhat dependent upon the outbreak item (fresh food outbreaks may benefit from a shorter window size), this parameter seemed to be only of minor importance. Determining an optimal OR threshold is less straight-forward, as this parameter influences both the balance between correct/incorrect/no identification outcomes as well as the number of cases required for identification. From the simulations Supplementary Fig. S3, an OR threshold = 50 was chosen as an appropriate value for all further simulations.     Figure 4 shows the effect of this parameter upon outbreak vehicle identification (ID) rate, the median number of cases analyzed before the model returns a result (correct, incorrect, or inconclusive), and the relative proportions of each outcome. Figure 4a) also shows the top-3 and top-10 rates, representing the proportion of outbreaks where the true outbreak vehicle is ranked in the top 3 and top 10 list of the highest ranked outbreak vehicles respectively. For the default outbreak parameters, Fig. 4 shows that the CPD analysis correctly identified outbreak vehicles for 90% of outbreaks after analyzing a median of 6 cases. Beyond this, we can see that model performance is highly dependent upon the background case proportion. For low background proportions, Fig. 4a) shows the ID rate approaches 100%, while in Fig. 4c) the median number of cases for algorithm termination lies close to the minimum value of five cases. However, as the background proportion increases, the ID rate decreases roughly linearly, while the median number of cases increases nonlinearly. Notably, the top-3 and top-10 rates are also significantly higher than the ID rate, highlighting the potential for generating candidate outbreak vehicles for closer investigation.
From Fig. 4b), the background proportion is also shown to impact the proportional composition of the three possible outcomes, with higher background proportions increasing both the incorrect ID rate and the inconclusive investigation rate. Interestingly, there are also considerable differences in the median number of cases analyzed for the different possible outcomes. Outbreak analyses that resulted in incorrect ID generally terminated after analyzing a lower number of cases than for analyses that resulted in correct identification. This indicates that some analyses would have been successful if the OR threshold had been higher, thus forcing the model to analyze more cases before returning an outcome.
We also ran outbreak vehicle identification on outbreaks generated by the two-chain outbreak simulation algorithm of Fig. 1b) to simulate a situation where the outbreak vehicle is sold in two different grocery chains. Both chains are assumed to have an equal market share. Two scenarios were simulated; in the first, we only have access to CPD from one of the grocery chains, while in the second we have access to CPD from both chains. Using default parameters, we found that the ID rate is severely decreased when operating on CPD from a single grocery chain vs when operating on CPD from both chains (67% vs. 92%). This result illustrates the importance of collecting CPD from all major grocery chains when the outbreak vehicle is sold in several chains.
Outbreak product group identification. Outbreak vehicle identification was attempted using the product group categories previously described. This was done to imitate the item categories used by outbreak investigations which use case interviews and questionnaires. When using the primary and secondary product groups, the outbreak vehicle identification algorithm attempts to return the relevant product group category. The results of these simulations are shown in Fig. 5. Figure 5 shows that using primary (34% ID rate) and secondary (52% ID rate) product groups resulted in drastically lower performance relative to using tertiary (90% ID rate) product ID numbers, analogous to questionnaire-assisted investigations.
Sensitivity analyses of attach rate, delay, and item purchase frequency. Next, the impact of attack rate and the delay parameter were investigated with respect to ID rate. Interestingly, these simulations showed that ID rate is only slightly impacted by either of these parameters (less than ± 10% ID rate within the ranges tested). This data is included in Supplementary Fig. S4. Figure 5. Average ID rate against background proportion for analysis using primary, secondary, and tertiary (product ID number) product groups with standard errors. 300 outbreaks for each data point are simulated as before, but instead of only using the product ID number as covariates, simulations were also run using the primary and secondary product groups for each registered purchase. The identification algorithm then attempted to return the relevant product group. www.nature.com/scientificreports/ Finally, the purchase frequency of outbreak vehicles was considered. After ranking all items by the number of times they were purchased, outbreak vehicles were drawn from three different sets of items: low (0-25th percentile), mid (25th-75th percentile), and very high frequency (97th-100th percentile) items. Using the default outbreak parameters, three sets of simulations were run. Here, a more significant difference was found between groups: the ID rates for the low, medium, and very high frequency groups was 87%, 93%, and 79%, respectively. This data is also shown in Supplementary Fig. S5.

Discussion
With this study, we sought to use real-life CPD collected by supermarket chains to identify outbreak vehicles in simulated foodborne outbreaks. Using a wide range of outbreak parameters and vehicles, we found that the most important parameter for the ability to detect outbreaks using CPD is the completeness of CPD coverage, quantified by the background case proportion. For simulated outbreaks with a background case proportion of 20%, our model was able to correctly identify outbreak vehicles in a median of 6 cases with a 90% correct ID rate. This shows that most outbreaks are solved almost immediately upon initializing analysis at five cases. The model also identified shortlists of the top 3 and top 10 most likely outbreak vehicles with an even higher rate of accuracy. These shortlists could then be subjected to more intensive investigation in a real outbreak scenario. Generating such shortlists may be particularly of interest if investigating outbreaks with fewer than the minimum five cases considered here, where a definitive outbreak vehicle is unlikely to be found.
To compare this CPD-based approach to more traditional questionnaire-based investigations, we analyzed outbreaks using the product group categories obtainable by questionnaires instead of exact item IDs. The number of categories in the primary (N = 408) and secondary (N = 1043) level product groups was quite generous; with traditional outbreak investigation, it is simply not realistic for people to remember and report their consumption of this many categories. For comparison, the CDC national hypothesis generating questionnaire queries people on their consumption of 166 item groups, although in contrast to the item groups specified in our dataset, the CDC's item groups have been chosen to optimize outbreak investigations with as few questions as possible.
Even with a relatively high number of product categories, this analysis resulted in a huge drop in model performance (34% and 52% ID rate for primary and secondary categories respectively). Interestingly, these ID rates are also comparable to real-world results; according to the Center for Science in the public interest, 39% of foodborne outbreaks reported to the CDC in 2013 were solved 16 . Although our simulation certainly does not capture all aspects of a questionnaire-based approach, it does demonstrate that identification at product group levels is very inefficient compared to at the item ID level, and that CPD-based investigations have the potential to greatly improve upon existing foodborne investigative techniques.
To better test simulation parameters in our analysis model, we conducted several sensitivity analyses. The most impactful outbreak parameter was clearly the background proportion, with both the incorrect ID rate and the inconclusive rate greatly increased at higher background proportions. The incorrect ID rate is particularly important to bear in mind, given the economic cost of product recall. This is not a risk unique to CPD-analyses, as epidemiological studies can only ever give statistical association. Just as with standard questionnaires, further investigation such as microbiological product testing would often be necessary for conclusive proof of outbreak vehicle.
With increasing background proportion, the number of cases required for the outbreak analysis algorithm to terminate also increases. Interestingly, the median number of cases for analyses that resulted in false ID was always lower than for analyses that resulted in correct ID, suggesting that some unsuccessful analyses would have been successful if more cases had been analysed. However, sensitivity analysis shows that increasing the OR threshold also increases the number of cases required for identification. In an outbreak scenario, it may be useful to generate shortlists of likely outbreak vehicles early in an outbreak for further investigation, while keeping in mind that more data may change the analysis results.
Diving deeper into the differences between outbreak vehicles revealed that very high frequency items (97th purchase frequency percentile) generated outbreaks significantly more difficult to solve than mid frequency items (25th-75th percentile). Since certain staple goods are so highly purchased, they are likely to be in the purchase history of control cases by chance without causing the control cases to fall ill. This can be very impactful; the most purchased item in the CPD (a brand of banana) constitutes 0.8% of all purchases.
Interestingly, the attack rate and delay parameter were not found to greatly impact model performance. Although initially surprising, the insignificance of the attack rate does make sense. Even if 99.9% of customers who purchased the outbreak vehicle do not fall ill, 80% of cases (at the default background proportion) will still have registered purchases of the outbreak vehicle. Although very low attack rates generate smaller outbreaks which may be more difficult to solve, this was not a significant effect in our model. Similarly, long delay parameters will only reduce model performance if the number of days between a customer's outbreak vehicle purchase and falling ill exceeds that of the analysis window size (40 days). Since most outbreaks are solved quickly, this rarely came into play.
In addition to high background proportions and high purchase frequencies, our model also struggled in scenarios where the outbreak vehicle is sold in two grocery chains, but we only have access to CPD from one of the chains (67% ID rate vs 92% with access to CPD from both). The reduced performance is not especially surprising; this situation is comparable to the single-chain outbreak simulation with a high background case proportion. Clearly, a CPD-based outbreak investigation benefits from a pre-existing relationship with major grocers. With the permission of the patients, their various CPD-records could be merged to a single profile containing all their recorded purchases.
More generally, poor coverage is likely to be a limitation of CPD-based investigations. For this reason, CPD approaches are particularly well-suited in countries with high usage of electronic payments, an oligopolistic www.nature.com/scientificreports/ grocery market, and a low frequency of eating out. These conditions are certainly met in Norway; less than 4% of transactions in Norway are by cash 17 , Norgesgruppen has a 44% market share 10 , and most Norwegians report eating out less than once a month 18 . Despite this, in our CPD the median proportion of weeks in which customers' recorded purchases was 17%, implying coverage of total consumption was quite low. One possible explanation might be households with multiple consumers registered to different loyalty accounts. If a two-person household alternates shopping evenly so that each person only shops every other week, both will have "low" coverage even if they only eat food from Norgesgruppen. These profiles should also be merged in a real outbreak scenario, using their shared address to form a single household profile. Although our CPD-based approach performed very well on the simulated outbreaks, it is not easy to ascertain how well these simulations model real-world outbreaks. There are relatively few 3,6 published studies demonstrating the usage of CPD for foodborne outbreaks and it is therefore important to test the model on real-world outbreak data. Recently, we initiated a pilot study for the collection of CPD from volunteers, aiming to eventually investigate real-world outbreaks with a CPD-based approach 19 . With this, we imagine a future with private-public partnerships allowing streamlined data collection for outbreak investigations with consumer consent. This study also shows that the concerns raised regarding legal barriers to CPD-collection 3 are surmountable.
In conclusion, our simulations showed that access to CPD from a large supermarket chain provides the potential to solve outbreaks quickly and accurately. By combining hypothesis generation and analytical study into a single step to generate quantitative measures of association, CPD analysis represents a major simplification of the traditional investigative process. However, limitations of this approach include low market coverage and highly purchased outbreak vehicles. Due to this, CPD investigations would seem particularly well-suited for countries with a high saturation of electronic payments and an oligopolistic grocery market. In scenarios where such conditions are not completely met, CPD may still provide an important supplement to outbreak investigations conducted using traditional questionnaires and case interviews. If implemented, CPD would offer foodborne outbreak investigators a very powerful tool which might help reduce the public health burden of foodborne illnesses.

Data availability
The data that support the findings of this study are available from Norgesgruppen but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Norgesgruppen.