Introduction

In the present scenario, healthcare experiences a driving and essential change in business, clinical and operating models including healthcare spending, public/private, innovation and demographic changes over the polices and programme to provide efficient service and care (Maruthappu et al. 2015; Sharma et al. 2018). Healthcare requires continuous and systematic improvement to remain cost-effective and to provide better care and high quality of service (Singh et al. 2014). The healthcare organizations need to maintain their standards with factors such as refined funding system, trained and skilled workforce, anticipated information for decisions and policies and a strong mechanism to save excellence in medicine and technologies (Martínez-García and Hernández-Lemus 2013). These aspects make a clearly unbalanced situation, which determines to make increasingly multifaceted and unpredictable with time (Basole and Rouse 2008; Tolf et al. 2015). Healthcare organizations need to find a new approach to improve the quality and respond quickly with lower cost and to increase the service and quality (Aronsson et al. 2011). The approach handles unpredictable changes as flexibility which is denoted as ‘agility’ (Ganguly et al. 2009; Acharya 2019).

Agility is an ability to react to new chances and issues by integrating meaningful new design features in a shorter period of time and in an extra promised manner (Amin and Horowitz 2008). Agility is fundamentally a holistic thought, primarily about adaptability, which can be accomplished during reconfiguration competence (Mishra et al. 2014). It has an essential competitive potential as it modifies a firm to satisfy its customer’s necessities with enormous speed (Sieger et al. 2000; Drupsteen et al. 2016; Voss et al. 2016). It gains importance in service sector to establish coordination and cooperation for determining enhancement in a patient’s experience with high quality of service (Mandal 2018). It can be employed in the healthcare sector to increase organizational abilities by making with strategies such as formal and informal inter-organizational interactions by proper communication, denoted environment scanning, decentralized decision making, self-organizing principles, trust in employees, enhanced employee skill in independence, flexibility and creativity (Tolf et al. 2015; Vaishnavi et al. 2019).

The effective execution of agile system in healthcare is done by understanding the agile healthcare along with the change process and how it deviates from industry and manufacturing agile system (Sindhwani et al. 2019). The change process could be made effective by addressing the issues, leadership commitment, customer’s involvement, team efforts to identify external factors and quick decision by leadership (Fuller et al. 2007). The change process is required to reflect on the agility with clarity, rapidity, high commitment and flexibility along with organizational strategies, organizational capability for learning with the ability to provide resource, funding, facilities, process and human resource (Worley and Lawler 2010). The initiation of change is accomplished effectively by formulating readiness at the beginning of change process (Self and Schraeder 2009). To implement agility successfully, the management needs to identify the organization’s readiness level, agile philosophy and their capability to make the change effectively (Carew and Glynn 2017; Vaishnavi et al. 2019).

Readiness is a set of information about willingness or unwillingness to accept the change by employees in the healthcare organization. It is composed of beliefs, attitudes and purpose of change of target members concerning to the necessities and possibility of executing organizational change (Armenakis and Fredenberger 1997). The readiness is a basic factor, which serves to initiate change in the organization by making support and association from employees (Holt et al. 2007; Stevens 2013; Douglas et al. 2017). The recognition of readiness level of a healthcare organization would support to know the potential of an organization to implement change procedure effectively (Washington et al. 2018). The assessment of readiness would help to recognize the gap between the expectations about the change priority with the employees. If the gap is identified more, it will be necessary to take an action to overcome the resistance among employees, in addition to threats in the change initiatives (Holt et al. 2007).

Assessment of readiness level would help the organization to know the degree of motivation among employees for delivering and implementing change, to measure the ability within organization, to improve the organizational capabilities and to enrich the organization as a whole (Lehman et al. 2002). A few studies have emphasized on the assessment of agility in healthcare (Rust et al. 2013; Teoh and Chen 2013; Suresh and Patri 2017; Goodarzi et al. 2018; Mahmoudi and Abdi Talarposhti 2018; Ornelas-Vences et al. 2019). There is an absence of the assessment of readiness for employing agility in healthcare organization, which is the motivation for the current study. The present study applies fuzzy logic to formulate a conceptual model for assessing the readiness level for the implementation of agility in healthcare organization. Fuzzy logic is said to be a classical logical method, which aims at modeling correct modes of reasoning that play a significant role in the human capacity to make rational decisions in situations of ambiguity and distinctiveness (Zadeh 1988). It can acquire the linguistic data as input, analyse it and then convey the results back in linguistic terms using triangular fuzzy numbers (Vinodh and Vimal 2012; Suresh and Patri 2017). The following are the questions used for the research:

RQ1:

How can the readiness level be measured for the implementation of agility in healthcare organization?

RQ2:

What are the enablers, criteria and attributes that influence the readiness level of agility in healthcare?

RQ3:

How are the weaker attributes addressed to enhance the readiness level of healthcare organization?

The above research questions are taken into account to identify the attributes that improve the readiness level for agile implementation. The current study adds to the existing literature of agility in two ways. First, key factors for agile readiness are defined from the agility literature and grouped into six enablers. Second, the conceptual model is developed to assess the readiness level of the case hospital for agility implementation.

The sequential order of the paper is as follows: Sect. 2—literature review on agility. Section 3—methodology and development of the conceptual model using fuzzy logic. Section 4—evaluation of a case hospital, Sect. 5—results and discussion with suggestions for improvements, Sect. 6—managerial implications, and Sect. 7—conclusion with future research.

Literature Review

Agility

Iacocca Institute of Lehigh University (Iacocca Institute Report 1991) first introduced agility as a main component of market competition which permits the organization to activate and reply continuously to the changing business atmosphere (Abdelilah et al. 2018). Hooper et al. (2001) have defined agility as “the ability of an enterprise to develop and exploit its inter- and intra-organizational capabilities”. Sherehiy et al. (2007) have described agility with responsiveness, flexibility, rapidity, cultural change, incorporation, high quality, customized product/service and enhanced core competencies. Agility is a capability, strategy and automation which focuses on team-based exploration for the chances to accomplish consumer requirement for innovation and potential solution with quick decision and investigation unexpected situation (Denning, 2017; Kacani and van Wunnik 2017).

An agile healthcare system is a mixture of flexibility and decision making which includes effective leadership to prove quality of care and delivery process with utilizing effective organizational structure and capability to change quickly to configure with quality (Worley 2012).

In healthcare, Pipe et al. (2012) have applied agility to analyse workplace stress and resilience level of employees, which contributes to form interference in healthcare organization. Guimaraes and de Carvalho (2012) have applied both lean and agility in hospital to understand “leagile” which is a key for current requirement strategy of market to meet the service unpredictability, uncertainty and complexity and hyper active competition to enhance the organizational goals. Burwitz et al. (2012) have utilized agility in the treatment process and adopted it for a pathway model to mix the complete process and enhance quality of care for long run and patient satisfaction. Teoh and Chen (2012) have focused on an agile Hospital Information System (HIS) execution with the strategic utilization of various Information Technology (IT) governance models to encourage the organizational competencies to prepare hospitals for change. Nazari et al. (2013) have identified the obstacles related to the agility as awareness about the system, proper training, cultural resistance, lack of reporting, lack of current market software and infrastructure problems for HIS. Košinár and Štrba (2013) have utilized agility to develop a model with the machine learning for the HIS development with alignment and modification on the software process with the knowledge gathered with the company. Ghodrati and Zargarzadeh (2013) have dealt with the organizational agility assigned to the employee’s mental health of hospital for the strategic readiness for dealing with the crises.

Similarly, Converso et al. (2015) have adopted agility to improve a simulation model for the emergency department for the arrangement of resources, enhance the performance to diminish the time essential to achieve the critical tasks, drop the overcrowding of the process, enhance service efficiency by decreasing the number of stretches and also decreasing the capacity of bed. Shirey (2015) has highlighted strategic agility as the essential leadership ability and proposed a method for the change management strategies for a healthcare system. Teoh and Cai (2015) have combined agility and innovation capability to enhance the quality of healthcare services. The process model is established to innovate strategy and integrate responsiveness and resources for better medical process. Tolf et al. (2015) have recognized five important organizational capabilities that are needed for the hospital to become agile which includes transparent cooperation throughout the organization, understanding the market and customers, leadership support to motivate employees, flexible resource and human capacity for effective delivery of service. Patri and Suresh (2017) have developed the conceptual model to analyse the agile performance of healthcare organization. Glassman and Withall (2018) have utilized learning agility to identify the leadership ability on the capacity for development, performance improvement and forecast success in leadership style for nursing. Chakraborty et al. (2019) have noted as that agility is utilized to adopt the Internet of Things (IoT) for the caring and prominent accomplishment for the improved flexible patient care delivery. Rungsrisawat and Jermsittiparsert (2019) have used agility and health supply chain performance to enhance the human capital of the healthcare sector, which is a significant mediating role. The agility indicates the improved human capital of healthcare with the increased supply chain performance.

Assessment of Agility

An assessment model for agility is developed by the Lin et al. (2006a, b) in manufacturing, and further it is utilized by various researchers in supply chain, tested with different manufacturing sector, twenty, thirty and forty level criteria to enrich the model and benchmarking (Lin et al. 2006a, b; Jain et al. 2008; Vinodh et al. 2010; Dahmardeh and Pourshahabi 2011; Shahrabi, 2011; Tseng and Lin, 2011; Vinodh and Devadasan, 2011; Vinodh and Vimal, 2012; Vinodh et al. 2012; Aravindraj and Vinodh 2014; Lotfi and Houshmand 2014; Vinodh and Aravindraj 2015; Khorasani 2018). The assessment of agility is done in different aspects which include business type process, supply chain, internal operational measure, performance and specific projects (Yauch 2011).

Yauch (2011) has made a quantitative study to develop a metric for agility performance that measures agility as a performance result holding both organizational achievement and environmental turbulence, which is indeed applied to manufacturing organizations of all types. Mishra et al. (2014) have extended an agility model using fuzzy logic to analyse a particular organization’s hierarchy and how it reflects in decision making attitudes as overall performance of agility in an organization. Vinodh and Aravindraj (2015) have benchmarked the assessment approaches of agility in a manufacturing organization using fuzzy logic and multi-grade fuzzy approach. Narayanan et al. (2015) have applied contingency theory and Transaction Cost Economics (TCE) to well understand the linkage between collaboration, trust and agility performance in a buyer-supplier relationship. Azevedo et al. (2016) have developed an assessment model for Lean, Agile, Resilient and Green (LARG) index as a benchmarking instrument to evaluate the leanness, agility, resilience and greenness of the automotive companies representing Supply Chain (SC). Potdar et al. (2017) have utilized fuzzy (DEMATEL) to assess the barriers of agile manufacturing and applied it in Indian automobile manufacturing company.

In healthcare, slowly attention is paid on the implementation and assessment of agility in manufacturing and software development. Teoh and Chen (2013) have adopted agile organization and IT governance to assess the case hospital with the strategic process model empirically. Further, the model suggests that agile healthcare IT is achieved with the IT governance strategies and it approves decision making with resources strategically for dynamic environment. Rust et al. (2013) have adopted agile practices as guidelines in the hospital to enrich the performance of the system to meet the new challenges in supply chain management and to provide knowledge to the healthcare managers and policy makers of the hospital. Suresh and Patri (2017) have attempted to execute the agility of a healthcare organization by assessing the agility of a university dispensary by using fuzzy logic approach. Mahmoudi and Abdi Talarposhti (2018) have assessed the performance of organizational agility in a hospital using a descriptive analytical study. As a result, health policy makers are recommended to plan customer satisfaction, timely utilization of facilities, elimination of weak points, cost reduction, encouragement and punishment system for staff, and staff empowerment. Likewise, Goodarzi et al. (2018) have tried to correlate the organizational agility with the performance of Human Resource (HR) of Tehran emergency centre where HR is the major vital tool in the agility of an organization and is reflected to be the most valuable benefit of any organization. Ornelas-Vences et al. (2019) have utilized agility assessment to develop a model to sense the leg quantification with the case of Parkinson’s disease patients. The model is accomplished to capture all information irrespective of the mission speed and reduce the integral uncertainty of the examiner with at least one expert.

Research Methodology

Fuzzy Logic Approach

Fuzzy logic is a system of interpretation and computation in which the objects of reasoning and computation are classes with unsharp (fuzzy) borders (Zadeh 2015). It specifies an adequate way of handling with problems related to imprecise and unclear phenomena. It would not make any assumption about independence, exhaustiveness, and exclusiveness and can tolerate a blurred boundary in definitions (Lin and Chen 2004). Fuzzy concepts enable evaluators to use linguistic terms to measure indicators in natural language expressions, and each linguistic term can be connected with a membership function.

Fuzzy logic approach in the present study is acquired from Lin et al. (2006a, b); Narayanamurthy et al. (2018); Sreedharan et al. (2019) to evaluate the readiness for the implementation of agility in a hospital. The fuzzy readiness assessment framework for agile implementation in the hospital is given in Fig. 1, and it consists of three phases. The first phase is the identification of enablers, criteria and attributes from literature review. The second phase is the selection of an appropriate hospital for testing agile readiness. The last phase is the continuous assessment of readiness and making the hospital ready to accept the implementation of agility successfully.

Fig. 1
figure 1

Framework for the assessment of readiness for the implementation of agility in a hospital

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For assessing agile readiness, six enablers such as management responsibility, workforce, organizational, strategy, technology and environmental agility are identified. Then 20 criteria along with 56 attributes are identified. These attributes are evaluated and rated by experts like doctors and managers from different hospitals in India. Then, the questionnaire is given and responses are recorded from the six employees of a particular hospital. To calculate the fuzzy logic model, the notations given in Table 1 are utilized.

Table 1 Notations used for fuzzy logic readiness assessment model for the implementation of agility
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Fuzzy logic has been used to improve the performance of manufacturing sector by evaluating the leanness, agility, Lean Six Sigma (LSS), sustainability and leagility (Tseng and Lin 2011; Vinodh 2011; Vinodh and Vimal 2012; Vinodh and Aravindraj 2013; Sreedharan et al. 2019). Likewise, in healthcare, leanness and agility are evaluated to improve the performance, but no study has analysed readiness for the implementation of agility using fuzzy logic (Suresh and Patri, 2017; Narayanamurthy et al. 2018; Narayanamurthy and Gurumurthy,2018). Fuzzy tools deliver a simplified and scientific route for the progress, analysis and testing of models quantitatively compared to other approaches in a relatively lesser duration. As a result, fuzzy tools are simple to work with and to adapt (Sreedharan et al. 2019). The readiness for the implementation of agility in healthcare has been assessed by experts using importance weights of readiness with linguistic variables. Fuzzy logic will consider linguistic data as input, analyse and further express the results back in linguistic terms. Triangular fuzzy number is used in the present study to get approximate linguistic variables (Vinodh and Vimal 2012; Rajak and Vinodh 2015; Suresh and Patri 2017). Using the fuzzy logic approach, two levels of calculations are done and FRAI is calculated. Euclidean distance method has been used; the readiness level of the hospital is obtained by matching the FRAI in the natural expression with linguistic terms. FPII is calculated to identify the weaker attributes, and suggestions are made to improve the readiness level of the hospital for effective implementation of agility.

Sampling Design and Data Collection

The present study utilizes scheduled interviews with questionnaire to collect data for importance weightage and performance rating for the assessment analysis. Initially, an interview is conducted with various experts from hospitals after the review of literature to formulate the conceptual model with enablers, criteria and attributes. Then the importance weightage is collected from different hospital experts who are well knowledgeable and decision makers of their premises and they have successfully running a well-established hospital for the last 10 years. Totally, five experts include doctors, general managers and nurse supervisor from different hospitals from India. Those five experts provide for the importance weightage for enablers, criteria and attributes.

After collecting importance weightage, the final survey is conducted with six caregivers to collect the performance rating from a single-case hospital. Among the caregivers 2 are doctors, 1 is a nurse supervisor, 1 is an admin manager, 1 is a senior laboratory technician, and 1 is a head of pharmacy at the case hospital. The caregivers are well experienced and good decision makers in the administration of the hospital for the past 10 years. First, the readiness for agility implementation is discussed with the experts and they are requested to provide final survey on the performance rating for the attributes.

Conceptual Model

A conceptual model for the evaluation of readiness for the implementation of agility in the hospital has been developed from the evaluation of different models including leanness assessment, agility assessment, lean readiness and LSS in healthcare and manufacturing (Suresh and Patri 2017; Narayanamurthy et al. 2018; Narayanamurthy and Gurumurthy 2018; Sreedharan et al. 2019). It is developed to evaluate the readiness level of a hospital. It consists of three levels. The first level consists of six enablers, second level consists of twenty criteria and third level consists of fifty six attributes.

Table 2 depicts the detailed description of the enablers, criteria and their attributes (Zain et al. 2005; Lin et al. (2006a, b); van Oosterhout et al. 2006; Misra et al. 2009; Vinodh et al. 2012;Ghodrati and Zargarzadeh 2013; Aravindraj and Vinodh 2014; Avazpour et al. 2014; Dubey et al. 2015; Vinodh and Aravindraj, 2015; Dubey and Gunasekaran, 2016; Appelbaum et al. 2017;Suresh and Patri,2017) identified in the healthcare context of readiness for the implementation of agility in the hospital. Table 12 depicts the detailed explanation of each attribute for the implementation of agility in the hospital.

Table 2 Readiness for the implementation of agility index for the evaluation of the hospital
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A stepwise description of fuzzy logic is given as follows:

Step 1:

Selection of enablers, criteria and attributes for assessing the readiness level for implementation of agility

The appropriate attributes related to each criterion are identified from the literature review and expert opinions from five different hospitals in India. The identified criteria related to assessing readiness level would classify the enablers as management responsibility, organizational, environmental, technical, workforce and strategy perspective. Agility can be applied in healthcare sector to enhance the organizational capabilities by enabling with strategies such as formal and informal organizational relationships (Tolf et al. 2015). An agile organization integrates organizational processes and individuals using advanced technologies in order to attain high-quality products and services, thereby accomplishing customer needs (Shahrabi 2012). To implement agility successfully in healthcare organization, it is necessary to know the readiness level of agile implementation before starting the change process. So, the readiness assessment model for the implementation of agility is developed by considering employees, organization, external environment, strategy, technology and management perspective as a whole.

Step 2:

Determination of the linguistic scale

Linguistic terms are used to access performance rating and importance weights to evaluate readiness for the implementation of agility based on attributes, criteria and enablers. They are selected from natural language expressions to provide more information than numerical grades for many situations (Lin and Chen 2004; Sreedharan et al. 2019). The linguistic scale used by Lin et al. (2006a, b), Rajak and Vinodh (2015) has been used in the current research. To assess the performance rating of readiness for the implementation of agility, the linguistic variables used are excellent (E), very good (VG), good (G), fair (F), poor (P), very poor (VP) and worst (W). Likewise, to assess the weights of importance for readiness for the implementation of the agility, the linguistic variables used are: very high (VH), high (H), fairly high (FH), medium (M), fairly low (FL), low (L) and very Low (VL). These linguistic ratings are expressed by triangular fuzzy numbers. Triangular fuzzy numbers are used in assessment due to the abstraction and impreciseness associated with predictable assessment of societal performance. They are utilized widely in performance assessment studies (Lin et al. 2006a, b; Vinodh and Devadasan 2011).

Step 3:

Measurement of performance ratings and importance weighting from experts

The next step is the collection of importance weights and performance rating of enablers, criteria and attributes for evaluating readiness for the implementation of agility. A questionnaire is distributed to five experts including doctors, decision makers and managers of a hospital who develop the new strategy and policy for the betterment of service. The performance rating is collected only for attributes because the analysis uses aggregated performance rating to criteria and criteria rating to enabler rating (Suresh and Patri 2017). For performance rating, the point scale is (0–10) of the linguistic variables and for importance weights, the point scale is (0–1) of the linguistic variables (Sreedharan et al. 2019). The experts' responses include rating and weights obtained for evaluating readiness for the implementation of agility. The following equation is used to calculate average operation method by using the responses from the experts (Wang et al. 2012; Suresh and Patri, 2017; Narayanamurthy and Gurumurthy 2018).

$$\begin{aligned} {\text{Formula}}\,{\text{of}}\,{\text{average}}\,{\text{operational}}\,{\text{method }} & = \, \left( {a_{1} b_{1} c_{1} } \right) \, + , \ldots , \, + \, \left( {a_{n} b_{n} c_{n} } \right) \\ & = \, \left[ {\left( {a_{1} + , \ldots , \, + a_{n} } \right)/n, \, \left( {b_{1} + , \ldots , + b_{n} } \right)/n, \, \left( {c_{1} + , \ldots , + c_{n} } \right)/n} \right] \\ \end{aligned}$$
Step 4:

Conversion of linguistic terms into appropriate fuzzy numbers

Triangular numbers are widely applied owing to their simplified methodology in attaining results (Lin et al. 2006a, b). In addition, subtraction and multiplication operations are easy to perform on fuzzy numbers. Chen and Hwang (1992) and Lin et al. (2006a, b) have described triangular fuzzy numbers as a special case of fuzzy numbers and defined a fuzzy number (a,b,c) whose membership functions (fA(X)) are as shown in the following equation:

$$f_{A} (x) = \left\{ {\begin{array}{*{20}l} {(x - a)/(b - a),} \hfill & {a \le x \le b,} \hfill \\ {(x - c)/(c - b),} \hfill & {b \le x \le c,} \hfill \\ {0,} \hfill & {{\text{otherwise}} .} \hfill \\ \end{array} } \right.$$

If a = b = c, then the triangular fuzzy number is reduced to a real number. Lin et al. (2006a, b), Vinodh and Vimal (2012), Vinodh and Aravindraj, (2013) and Sreedharan et al. (2019) have developed a fuzzy set number corresponding to each linguistic term for the evaluation of leanness, agility, leagility and Lean Six Sigma (LSS), and these are used to evaluate readiness for the implementation of agility as shown in Table 3.

Table 3 Linguistic terms and appropriate fuzzy numbers for rating and weights for readiness for the implementation of agility in the hospital
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Step 5:

Aggregating fuzzy rating with fuzzy weights

The aggregate performance rating of the attributes is customized into criteria rating, and the criteria rating is customized into enabler rating and shown in Eqs. (1) and (2), respectively (Lin et al. 2006a, b; Suresh and Patri 2017).

$$Q_{ij} = \frac{{\sum\nolimits_{K = 1}^{K} {(P_{ijk} \otimes Q_{ijk} )} }}{{\sum\nolimits_{k = 1}^{k} {P_{ijk} } }}$$
(1)
$$Q_{i} = \frac{{\sum\nolimits_{j = 1}^{j} {(P_{ij} \otimes Q_{ij} )} }}{{\sum\nolimits_{j = 1}^{j} {P_{ij} } }}$$
(2)

Once the criteria rating is obtained, the next step is to compute the FRAI by Eq. (3).

$${\text{FRAI}} = \frac{{\sum\nolimits_{i = 1}^{i} {(P_{i} \otimes Q_{i} )} }}{{\sum\nolimits_{i = 1}^{i} {P_{i} } }}$$
(3)
Step 6:

Match the FRAI with an appropriate level

The FRAI calculated is compared with the general linguistic term using Euclidean distance method. Euclidean distance method is conceived of as the most spontaneous method for humans to calculate perceived closeness (Lin et al. 2006a, b; Vinodh and Vimal 2012; Vinodh and Aravindraj 2013). In this method, five linguistic terms known as natural language expressions are adopted from Narayanamurthy et al. (2018) which include not ready (NR), low ready (LR), average ready (AR), close to ready (CR) and ready (R). Table 4 represents readiness for the implementation of agility and its corresponding fuzzy interval. The Euclidean distance is calculated by using Eq. (4).

$$D({\text{FRAI}},{\text{RAL}}_{i} ) = \sqrt {\sum {(f{\text{FRAI}}(x) - f{\text{RAL}}_{i} (x))^{2} } }$$
(4)
Table 4 Readiness factor for the implementation of agility and fuzzy intervals
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Step 7:

To identify weaker attributes, FPII is calculated

To identify weaker attributes of readiness for implementing agility by using FPII, the performance rating and importance weights of each attribute are combined. After computation, FPII helps the manager to focus on the attributes that have low value and they address those attributes to improve the readiness level for the implementation of agility in the hospital. The computation of FPII consists of two steps: first is the calculation of as given in Eq. (5).

$${\text{FPII}}_{ijk} = U_{ijk} \otimes Q_{ijk}$$
(5)
$${\text{Where}}\;U_{ijk} = (1,1,1) - P_{ijk}$$

Next step is the prediction of ranking score for each attribute by employing centroid method where a, b and c are the lower, middle and upper numbers of the triangular fuzzy number, respectively, as shown in Eq. (6) (Vinodh and Vimal 2012; Sreedharan et al. 2019).

$${\text{Rank}}\;{\text{score }} = \frac{a + 4b + c}{6}$$
(6)

After obtaining ranks for all attributes, the management should take corrective actions to overcome the issues and to make the organization ready for the implementation of agility.

Case Study

The assessment of readiness model for the implementation of agility is done in a hospital which is 44 years old, located in India. The selected hospital is with a capacity of 41 beds, and it is now functioning with a team of well-qualified and professionally skilled medical staff with long years of experience in their respective fields. The quality of nursing care is maintained by adequate trained nurses and Para medical staff. The hospital works on round the clock and offers services including casualty, Intensive Care Unit (ICU), ambulance service, operation theatre, diagnostic centre, health checkup schemes, skin treatment, gastroenterology, neurological conditions, diabetes management, general paediatrics and gynaecology. A team of doctors on board, including specialists, are equipped with the knowledge and experience of handling various types of medial issues.

The hospital faces an issue of competitive advantage due to the increase in competition, changes in the requirements of customers, continual change process, uncertainty and increase in the standard of quality. All the above issues are handled by applying agility in the hospital. The enablers of agile organization would imply refined environmental scanning, decentralized decision making, informal and formal interrelationship by networking and trust among employees’ skill to take decisions and innovation (Tolf et al. 2015). To implement agility in hospital, management needs to formulate before initiating the change process, for which the current study would assist the management of the hospital to know the readiness level on agility. Data have been collected from six experts including 2 doctors, 1 head of nursing, 1 senior laboratory technician, 1 head of pharmacy and 1 admin manager. The experts provide data for performance rating of attributes. The survey is conducted with five experts from different hospitals to get the importance weights for enablers, criteria and attributes.

The responses of importance weights for enabler and criteria are shown in Tables 5 and 6, respectively. For assessing the readiness level, the response for performance rating and importance weights of attributes is given in Table 7.

Table 5 Importance weights of readiness for the implementation of agility of enabler
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Table 6 Importance weights of readiness for the implementation of agility of criteria
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Table 7 Importance weights and performance rating of readiness for the implementation of agility of attributes
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Primary Computation of FRAI

The fuzzy interval values are assigned by linguistic values using Table 3 to importance weights and performance rating. The aggregated importance weights and performance rating are calculated based on the formula of average operational method.

$$\begin{aligned} {\text{Average}}\,{\text{fuzzy}}\,{\text{weight }} & = \, \left[ {VH + H + H + H + H} \right]/5 \\ = \, [\left( {0.85,0.95,1.0} \right) + \left( {0.7,0.8,0.9} \right) + \left( {0.7,0.8,0.9} \right) + \left( {0.7,0.8,0.9} \right) + \left( {0.7,0.8,0.9} \right)]/5 \\ = \, \left( {0.73, \, 0.83, \, 0.92} \right) \\ \end{aligned}$$
$$\begin{aligned} {\text{Average}}\,{\text{fuzzy}}\,{\text{rating }} & = \, \left[ {P + VP + F + P + P + F} \right]/6 \\ & = \, \left[ {\left( {2,3.5,5} \right) \, + \, \left( {1,2,3} \right) \, + \, \left( {3,5,7} \right) + \, \left( {2,3.5,5} \right) \, + \left( {2,3.5,5} \right) \, + \left( {3,5,7} \right)} \right]/6 \\ & = \, \left( {2.17,3.75,5.33} \right) \\ \end{aligned}$$

The next step is the calculation of fuzzy index rating for criteria (Qij) by using Eq. (1). For example, the criteria rating of multiple task and decision making Q11 (RA11) are computed as given below.

$$Q_{ 1 1} \, \left( {{\text{RA}}_{ 1 1} } \right){ = }\frac{{ \, \left[ \begin{aligned} ((2.17,3.75,5.33) \otimes (0.73,0.83,0.92)) \oplus \hfill \\ ((2.50,4.25,6.00) \otimes (0.54,0.68,0.82)) \hfill \\ \end{aligned} \right]}}{(0.73,0.83,0.92) \oplus (0.54,0.68,0.82)}$$

Q11 (RA11) = (2.31, 3.98, 5.65).

The same equation is applied to calculate the performance rating of criteria and shown in Table 8. The fuzzy index of the enabler RAi is calculated by utilizing Eq. (2). For example, the management responsibility agility enabler RA1 is calculated as

$$Q_{1} (RA_{1} ) = \frac{{\left[ \begin{aligned} ((2.31,3.98,5.65) \otimes (0.58,0.71,0.84)) \oplus \hfill \\ ((2.56,4.34,6.11) \otimes (0.61,0.74,0.86)) \hfill \\ \end{aligned} \right]}}{(0.58,0.71,0.84) \oplus (0.61,0.74,0.86)}$$

Q1 (RA1) = (2.44, 4.16, 5.88)

Table 8 Fuzzy index of readiness for the implementation of agility of criteria rating
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The same equation is applied to calculate the performance rating of enabler and shown in Table 9. The FRAI is calculated using Eq. (3) as

$${\text{FRAI}} = \frac{{\left[ \begin{aligned} ((2.44,4.16,5.88) \otimes (0.79,0.89,0.96)) \oplus ((3.11,4.91,6.72) \otimes (0.69,0.8,0.9)) \oplus \hfill \\ ((3.11,4.93,6.76) \otimes (0.66,0.77,0.88)) \oplus ((3.11,4.88,6.66) \otimes (0.76,0.86,0.94)) \oplus \hfill \\ ((3.01,4.79,6.57) \otimes (0.66,0.77,0.88)) \oplus ((3.31,5.06,6.82) \otimes (0.62,0.74,0.86)) \hfill \\ \end{aligned} \right]}}{{\left[ \begin{aligned} (0.79,0.89,0.96)) \oplus (0.69,0.8,0.9) \oplus (0.66,0.77,0.88) \oplus \hfill \\ (0.76,0.86,0.94) \oplus (0.66,0.77,0.88) \oplus (0.62,0.74,0.86) \hfill \\ \end{aligned} \right]}}$$
$${\text{FRAI }} = \, \left( {3.00, \, 4.78, \, 6.56} \right)$$
Table 9 Fuzzy index of readiness for the implementation of agility of enabler rating and FRAI
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Euclidean Distance Method

After obtaining, FRAI is converted back into linguistic term. In this method, the five linguistic terms used are known as readiness label which is given in Table 4 and for each readiness label, Euclidean distance (D) is calculated by using Eq. (4) as shown below.

$$\begin{array}{*{20}l} {D \, \left( {{\text{FRAI}}, \, R} \right) \, = \, \left[ {\left( {3.00 - 7} \right)^{2} + \, \left( {4.78 - 8.5} \right)^{2} + \, \left( {6.56 - 10} \right)^{2} } \right]^{1/2} = \, 6.46} \hfill \\ {D \, \left( {{\text{FRAI}},{\text{ CR}}} \right) \, = \, \left[ {\left( {3.00 - 5.5} \right)^{2} + \, \left( {4.78 - 7} \right)^{2} + \, \left( {6.56 - 8.5} \right)^{2} } \right]^{1/2} = \, 3.87} \hfill \\ {D \, \left( {{\text{FRAI}},{\text{ AR}}} \right) \, = \, \left[ {\left( {3.00 - 3.5} \right)^{2} + \, \left( {4.78 - 5} \right)^{2} + \, \left( {6.56 - 6.5} \right)^{2} } \right]^{1/2} = \, 0.55} \hfill \\ {D \, \left( {{\text{FRAI}},{\text{ LR}}} \right) \, = \, \left[ {\left( {3.00 - 1.5} \right)^{2} + \, \left( {4.78 - 3} \right)^{2} + \, \left( {6.56 - 4.5} \right)^{2} } \right]^{1/2} = \, 3.10} \hfill \\ {D \, \left( {{\text{FRAI}},{\text{ NR}}} \right) \, = \, \left[ {\left( {3.00 - 0} \right)^{2} + \, \left( {4.78 - 1.5} \right)^{2} + \, \left( {6.56 - 3} \right)^{2} } \right]^{1/2} = \, 5.69} \hfill \\ \end{array}$$

Thus, the linguistic label is matched with minimum D value and the readiness for the implementation of agility index of the hospital is known as “average ready”. The pictorial representation of readiness label for the implementation of agility is given in Fig. 2.

Fig. 2
figure 2

Linguistic levels to match fuzzy readiness for the implementation of Agility index

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Fuzzy Performance Importance Index (FPII)

Weaker attributes are identified by computing FPII which consists of two steps. The first step is the calculation of FPII done by using Eq. (5), and the second step is the development of ranking score for each attribute by using Eq. (6). The sample calculation of “Multiple tasking (RA111)” is shown below.

$$W_{111} = \, \left( {1, \, 1, \, 1} \right) \, {-} \, \left( {0.73, \, 0.83, \, 0.92} \right) \, = \, \left( {0.27, \, 0.17, \, 0.08} \right)$$
$${\text{FPII}}_{111} = ( 2. 1 7 , { 3} . 7 5 , { 5} . 3 3 ) { } \times ( 0. 2 7 , 0. 1 7 , 0. 0 8 ) { } = { (0} . 5 9 , 0. 6 4 , 0. 4 3 )$$
$${\text{Ranking score of }}(RA_{111} ) = \frac{(0.59 + 4(0.64) + 0.43)}{6} = 0.59$$

Further, the ranking score has been obtained for all the attributes and given in Table 10. The management threshold is fixed with the Pareto principle (20%) to acquire weaker attributes (Narayanamurthy et al. 2018). The management of the case hospital is consulted to fix the threshold level for the improvement of readiness level of the hospital. In the present study, the management fixes the threshold as 1 and identifies weaker attributes less than 1 with the Pareto principle. There are 15 attributes which have lower performance that come below 1 (26%) out of 56 attributes.

Table 10 FPII of readiness for the implementation of Agility
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Results and Discussion

Agility in hospital has the competence to survive and expand its competitive environment continuously and to respond to the unpredictable situation of market (Teoh and Cai 2015). It handles the uncertainty in healthcare which includes changes in demographic, public expectation, general technical innovation, socioeconomic status, work-related, supplier, municipality, social care and clients (Tolf et al. 2015). Agile implementation in a hospital is complex due to continual change, for which readiness level would help the manager for efficient implementation of agility. The analysis of readiness level is incorporated with the framework developed of conceptual model which is given in Table 2. The assessment of readiness level for the implementation of agile in the hospital is done, which divides into two elementary analyses. First, the current study computes FRAI from the selected hospital is “average ready”. Second, FPII is calculated to identify fifteen weaker attributes and the management needs to pay attention to make the hospital ready for accepting agile in its system. The weaker attributes have low performance and also less than the value of FPII value based on Pareto principle (20%) which is discussed with management.

The weaker attributes are multiple tasking, good relationship with caregivers, management involvement, delegation of authority, positivity towards change, financial resources, customers’ opinions, technical expert suggestions, innovation in service design, creativity, new idea to service, customer expectation, change in taste and preference, customer relationship management and service variety. General strategies to overcome the resistance for change include the continual management support for caregivers, proper training on the updated technology, continuous improvement process to increase quality, informal and formal relationship with caregivers, sharing of knowledge by caregivers with other employees and encouraging caregivers on taking responsibility.

The management also needs to develop and encourage a good relationship with its stakeholders including caregivers, customers, suppliers and other allied partnership from external environment. The good inter-relationship can be created by a proper communication with all the employees and capability to show them to be transparent with all caregivers. The customer requirement should be treated as central for the entire change process and should build trust with customers for long-term which would help to keep the hospital competitive. The management needs to develop and encourage nourishing the skill of employees for changing environment. The caregivers are the backbone, and importance is given to their values and responsibility. New teams are identified, and the responsibility with flexibility is shared to take decisions to increase the trust among employees to accept change. Further, the utilization of the available resource is an essential element of achieving agility in hospital. The readiness level analysis is done to know the required resource to accomplish agility in hospital and carefully distribute resources to all the departments. The readiness can be created in the hospital by reaching the caregivers about the current requirements of hospital from customers, external environment and availability of resources. The suggestions for the enhancement of readiness level are gained from the experts and discussed with the management of the case hospital for further clarity. The detailed and multiple descriptions of suggestions for weaker attributes are given in Table 11.

Table 11 Suggestions for improvement for weaker attributes
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Practical Implications

The preset study develops a readiness assessment model with the enablers, criteria and attributes. The model is communicated with the managers, and the approval is obtained to assess the hospital on readiness for agility implementation. Then the questionnaire is circulated among the managers to collect the ratings which are converted into linguistic terms. Then the linguistic variables use fuzzy set number to calculate FRAI and FPII to know the current readiness level of hospital for agility implementation. The weaker attributes are identified, and the management needs to take necessary actions to improve the readiness level of the hospital. The new strategies are framed and executed to improve readiness in the hospital. Further, the model is tested once again to know the hospital’s readiness level for agility implementation. The continuous improvements in performance, quick decisions, developing a new service design, regular monitoring of external environment, encouraging employees to learn new skills and enhancing the customer satisfaction would make a hospital agile.

Managerial Implications

The readiness assessment model developed would be simple, comfortable and easily assessable by the management of the hospital. The adoption of the model can bring insights for the management into the obstacles to overcome the resistance. Fuzzy logic that uses the triangular can provide the truth level of the hospital whether it is ready or not to accept the agility in their premises. The results of the assessment model are strong enough for the management to understand issues and implement the strategy to overcome the resistance. The model represents the overall readiness level and helps to identify weaker areas that require special attention with detailed explanation for improvement. The model identifies the both external and internal issues separately to improve the readiness level. The external factors regulations, competitors advantage and customer needs are monitored regularly for the betterment of the hospital. In organization level size, location, employee requirement and workforce enrichment to improve readiness level. A special focus is required on both the internal and the external factors for updating and making the organization ready to accept the changes. Further, model is continuously utilized by the practitioners and the experts, and it will help the hospital for the betterment for making the organization ready.

Conclusion

Agility is a quick attractive key and a crucial factor to all the organizations to survive in the uncertain and unstable market. The organization should continuously adopt the change according to the changing environment and need to respond quickly, flexibly and within the ability to meet customer demands (Ganguly et al. 2009). The implementation of agility in an organization requires a concern time, and it depends on various factors. The management and employees are required to be enthusiastic, accept the transformation of organization and develop a strategy to adopt agility (Devadasan et al. 2005). Agility is implemented with the attention of management by measuring the readiness level of a hospital. The current study helps to identify the readiness level of the hospital using fuzzy logic. Fuzzy logic approach is utilized mainly to overcome the difficulties like vagueness, uncertainty, and ambiguity (Vinodh and Vimal 2012).

The current study addresses the all three research questions. First, the framework for the assessment of readiness level is developed with the literature and the experts. Second, the model developed is tested with the case hospital and FRAI is computed and measured by Euclidean distance method and the hospital is found to be “average ready” for implementing agility. Third, FPII is computed to identify 15 attributes that were weaker from 56 attributes. The agile leader needs to discuss the strength and weakness, effective utilization of the potential of human resource, to set the goal to accomplish task and to develop a strategy to make hospital readiness. The organizational culture would encourage collaboration and cooperation among employees, innovation, creativity and transparency for making the organization agile (Sanatigar et al. 2017). The readiness level is increased in the hospital by integrating, implementing and practicing strategic workforce on planning workloads, healthcare size, location and process. The analysis of readiness level would help to increase flexibility and acceptance of agility among the employees of the hospital.

The current study addresses the issues related to resistance on the assessment model for readiness, but it has its own limitations. The generalizability of the study can be done by testing the model in different location and medium-sized hospitals in different regions because each hospital is unique in its nature and all the affecting factors are captured to be included in the model. The study can be extended in future by applying longitudinal study with a single hospital with different levels of healthcare organizations like dispensary, multi-specialty hospital, public and private medical institutes. Comparison and contrast would give further insights into the readiness level on a single hospital with long time and different hospital with a single time. Further, it can use correlation to rank the agility readiness attributes.