Brinda Sampat
email: brinda.sampat@nmims.edu

Ashu Sharma
email: ashu.sharma@sbm.nmims.edu

Bala Prabhakar
email: bala.prabhakar@nmims.edu

Understanding factors influencing the usage intention of mobile pregnancy applications

Keywords: mHealth, mobile pregnancy apps, UTAUT, developing country, perceived risk, personal innovativeness, health informatics, pregnancy

Advancement in digital technology and the need to provide alternate healthcare delivery channels to individuals in developing countries has led to the boom in mobile Health (mHealth). A wide range of mHealth applications (apps) and services are available today to combat the maternal and newborn health disparities in India. Yet, there is scant research in understanding the predictors of pregnant women's adoption towards pregnancy apps in developing countries. The objective of this study is to identify the most significant predictors influencing behavioural intention to use pregnancy apps. To meet this objective, a conceptual model was developed and empirically tested by extending UTAUT with relevant constructs namely personal innovativeness in IT and perceived risk. A conceptual model along with the hypothesized causal paths among the constructs are empirically validated with the help of structural equation modeling using Smart PLS 3.0 with a sample of 220 pregnant women. Results showed that intention to use pregnancy apps by women was predicted by six influencing factors: performance expectancy, effort expectancy, facilitating conditions, social influence, personal innovativeness and attitude. Perceived risk had no significant effect on the behavioural intention to use pregnancy apps.

Introduction

Health 2.0 has enabled to focus more on patient care due to the evolving relations between healthcare providers and patients. Information and Communication Technologies (ICT) usage by hospitals and healthcare professionals has increased tremendously which has enabled new forms of communication between patients and health professionals (Antheunis et al., 2013). There is a shift towards patient empowerment in maternal care, which now accommodates electronic devices into its functioning by incorporating the use of shared maternity care needs. Blogs, wikis, forums, discussion groups, social media, mobile health (mHealth) apps have played an important role in eliciting information and gaining support about early motherhood and parenting matters [START_REF] Doty | Online social support for parents: A critical review[END_REF]. Pregnant women consider information obtained through online platforms to be reliable and of good quality (Szwajcer et al., 2008;Huberty et al., 2013). The information found online plays a significant role in women's decision-making during pregnancy (Lagan et al., 2010).

In an increasing digitized world, the introduction of smart phone has revolutionized the way in which healthcare services are provided. Mobile applications (apps) are end user software that are designed to be used on mobile devices like smartphones and tablets.

During the first quarter of 2020, there were 43,285 mHealth apps available in the Google Play store, according to the study published in May 2020 by Statista (2020a).

According to a report by Statista (2020b), published in May 2020, over 500 million users in India will use mobile phone internet by 2023.

There is a decline in the extended family structure in India. The use of digital media has increased tremendously in supporting the needs of pregnant women. Consultations are limited by time particularly in a busy clinical setting. Women seek information from online media and pregnancy-related apps to search, share personal experiences and information about pregnancy and parenting. The accessibility, pervasiveness and popularity of mobile pregnancy apps make them a cost-effective and easy way for women to monitor their health. However, these apps are fairly new in developing countries, which means that the behavioural intention for women to use them are not completely understood. Thus, the objective of this study is to understand the factors that may directly and indirectly influence user's behavioural intention to use pregnancy apps.

Literature Review

Maternal care now accommodates electronic devices into its functioning by incorporating the use of mHealth apps. According to study by Trip et al., (2014), out of the pregnancy apps available in the app distribution platforms, forty per cent of the apps were informative, thirteen per cent interactive, nineteen per cent had medical tools, eleven per cent were social media apps. This study also points that the reliance on healthcare professionals may be reduced with the proliferation of such interactive and personalized apps.

These findings were confirmed by a study in Dublin where women had used an app during pregnancy. Women drew their attention to digital media to get information related to pregnancy, subscribed to the use of online discussion forums, shared their pregnancy photos and status on social media, used micro-blogs and podcasts. Disadvantaged women in Ireland reported high levels of digital media usage during pregnancy to obtain health information (O'Higgins et al., 2014). Similar findings were observed in a focus group study carried out in Australia by [START_REF] Lupton | It just gives me a bit of peace of mind': Australian women's use of digital media for pregnancy and early mother-hood[END_REF]. This study suggested, social media, mobile apps, content-sharing platforms and online platforms were very important to the participants to connect with family and friends, post images about their pregnancy, track their pregnancy, and child's development. Maternal subjects used social media features provided on smartphone apps as a new form of communication, or socialization whilst staying at home. Women view health apps as a means to monitor and improve their pregnancies, health, and their children's development and health (Johnson, 2014).

A study on pregnancy and parenting apps usage by 410 Australian women revealed that women found apps useful in providing information, documenting changes in their own bodies, self-monitoring foetal development or child development and providing reassurance. However, while using these media the participants in the focus group had not considered the issues of third-party access and use of personal data. Women were aware of the digital ties these apps had however, they were not fully aware of some of the privacy challenges these apps posed (Lupton & Pedersen, 2016).

Convenience, immediacy, ease of access, sharing experiences and easy access to information were the major benefits of pregnancy apps according to the participants in South Korea. Information concerning signs of risk and disease during pregnancy was searched most often followed by physical changes in the body, prenatal education, and breastfeeding (Lee & Moon, 2016). Features in pregnancy apps can help pregnant women make informed choices about nutrition, weight gain and development of the baby. According to a study in Australia, parents used online media such as social media platforms, blogs, websites, online discussion forums, apps during pregnancy as they provided immediate, regular, detailed, entertaining, customized, practical, professional, reassuring and unbiased information [START_REF] Lupton | The use and value of digital media for information about pregnancy and early motherhood: a focus group study[END_REF]. In another study participants described pregnancy apps being as more significant and beneficial than other forms of health communication in receiving information related to pregnancy [START_REF] Rodger | Pregnant women's use of in-formation and communications technologies to access preg-nancy-related health information in South Australia[END_REF]. Participants in a randomized controlled trial demonstrated higher use of mobile applications as against the use of distributed notebooks to record details about their pregnancy (Ledford et al., 2016).

Theoretical Framework

The success of any Information System (IS) depends on the way users accept it (Venkatesh et al., 2003). The most extensively used models to understand technology acceptance are Theory of Reasoned Action (TRA), Theory of Planned Behaviour (TPB), Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT).

UTAUT

UTAUT model focusses on explaining user intentions towards IS usage. The theory has key constructs performance expectancy, effort expectancy, social influence, and facilitating conditions which act as the direct determinants of behaviour intention to use (Venkatesh et. al., 2003). Gender, voluntariness of use, experience and age are posited to moderate the impact of the four main constructs on usage intention and behaviour (Venkatesh et. al., 2003). Critics believe that attitude leading to behaviour is missing in this model and that intention to use may or may not lead to use behaviour. In this study, the UTAUT model provides the theoretical framework underpinning the research which is extended with personal innovativeness and perceived risk to understand and empirically test the factors that influence user's intention to use mobile pregnancy apps in a developing country's context (Refer Fig. 1)

Fig. 1. Proposed Research Model 3.

Performance Expectancy

Users are inclined to use a technology if they find it useful and advantageous in their day-to-day activities (Alalwan, Dwivedi & Williams,2016; Davis 1989). Performance expectancy in the context of pregnancy app usage is the degree to which users believe that using pregnancy apps would improve their ability to manage their health. When users perceive pregnancy apps useful in seeking information and managing their health, the perception generates positive attitude towards using them. Thus, it is reasonable to hypothesize that: H1: Performce expectancy has a positive and significant effect on attitude to use pregnancy apps

Effort Expectancy

Effort expectancy is defined as "the degree of ease associated with the use of the system" (Venkatesh, Morris, Davis & Davis, 2003, p.450). Studies related to mHealth applications (Zhang et al., 2014) have pointed out that effort expectancy is a major attribute leading to intention to use these systems. Thus, the hypothesis, H2: Effort expectancy has a positive and significant effect on performance expectancy to use pregnancy apps When users perceive using pregnancy apps less complex and requiring less effort to use them, they will use the apps. This leads to the following hypothesis, H3: Effort expectancy has a positive and significant effect on attitude to use pregnancy apps

Social Influence

Social influence is defined as "the degree to which an individual perceives how important others believe he or she should use the new system" (Venkatesh et al., 2003). Social influence helps one understand the fact that though an individual may not have a positive attitude to use a particular technology, how under the influence of family, friends one may use it anyway as it may be expected of one to do so (Venkatesh & Morris, 2000). Thus, the following hypothesis is postulated H4: Social Influence has a positive and significant effect on attitude to use pregnancy apps

Facilitating Conditions

Facilitating Conditions (FC) define a users' belief that infrastructure (technical and organizational) are available to support the use of the system (Venkatesh et al., 2003, Dwivedi et al. 2016). In the case of mobile pregnancy apps, not all Indian users have equal access to information, mobile phones, knowledge, internet connectivity and other resources that enable the usage of these apps. Thus, the following hypotheses is proposed, H5: Facilitating Conditions has a positive and significant effect on performance expectancy to use pregnancy apps H6: Facilitating Conditions has a positive and significant effect on intention to use pregnancy apps H7: Personal Innovativeness has a positive and significant effect on intention to use pregnancy apps

Personal Innovativeness

Perceived Risk

Perceived risk refers to the likelihood of certain negative outcomes and consequences from engaging in those behaviours. In the context of pregnancy apps, it is defined as "the degree to which an individual perceives certain negative outcomes and the danger and severity of negative consequences from using pregnancy apps". Performance risk refers to "the possibility of the product malfunctioning and not performing as it was designed and advertised and therefore failing to deliver the desired benefits" (Featherman & Pavlou, 2003). It refers to the losses which a user may incur due to incorrect processing of health information.

H8: Performance Risk is negatively associated with Perceived Risk of using pregnancy apps

In the context of pregnancy apps, some user may be time conscious and guard against the loss of time they may incur in having to learn how to setup, use and maintain the app.

H9: Time Risk is negatively associated with Perceived Risk of using pregnancy apps

Researchers have studied the effects of privacy risks in the context of online services (Cocosila, 2013; Lee, 2009; Featherman & Pavlou, 2003; Shareef, Kumar & Kumar, 2008) which affects their intention to use those services. Pregnancy apps may communicate personal information about users with third parties. This uncertainty of how personal information will be used leads to privacy concerns related to the app usage. This leads to the following hypotheses H10: Privacy Risk is negatively associated with Perceived Risk of using pregnancy apps H11: Perceived Risk is negatively associated with Behavioural Intention of using pregnancy apps

Attitude

Attitude towards using a system is defined as the extent to which a user positively or negatively evaluates that system to carry out the behaviour in question. In

Data Collection and Sampling

The structured questionnaire was emailed to the participants to collect the primary data for the study. An online link was embedded in an email and sent to the participants. Questionnaires were mailed to 750 participants across the country. 243 completed questionnaires were obtained of which 220 were used for further analysis. 23 responses were eliminated in the process as they were incomplete or had the same value marked for all the questions in the survey. The final response rate was 32.4%.

Demographic Profile of the Respondents

The sample consisted of 220 females. Table 1 illustrates the respondents' demographic de-tails. 85 per cent of the respondents were less than the age of 35 indicating that the sample comprised of young or middle-aged respondents. 61 per cent of the population was well educated and 47 per cent of the respondents were working professionals.

Data Analysis

Tests for missing values and multicollinearity were conducted. The sample did not suffer from any multicollinearity issues as the VIF values were less than 7 (Hair et al., 2011). The data analysis process involves two steps proposed by Hair et al. (2006). The first step aims at evaluating the measurement model followed by the structural model.

Reliability and Validity Tests

In order to check the reliability of the constructs used in the instrument, the Cronbach alpha values were calculated. Table 2 represents the factor loadings for all constructs were higher than 0.5 (Byrne, 2001) and Cronbach's α values ranged from 0.749 to 0.922 establishing reliability of the measure. Convergent and discriminant validity in the study were met as mentioned in Table 3. After establishing the reliability and validity of constructs, the hypothesized relations in the model were tested using PLS-SEM. The path coefficients and the corresponding p-values are presented in Table 4.

Results

This research is concerned with understanding the factors that influence the behavioural intention of women to use mobile pregnancy apps. The model fit indices include SRMR as 0.097, and RMS theta as 0.191. This study aimed at understanding and explaining the significance of these constructs. It also aimed at understanding the driver construct and thus PLS-SEM was used in this study. Smart PLS 3.2.8 (Ringle et al. 2017) was used to analyse the data. The sample size in PLS is considered by the often cited ten times rule Barclay et al. (1995). The sample size in this study meets the recommended rule. The results obtained from the study are presented in Table V. H1, H2, H4, H6, H8, H9, H10, H12 are supported whereas H3,H5, H7, H11 are not supported.

Discussion

In the study on intention to use pregnancy apps, the relationship between performance expectancy and attitude was found to be significant. If women perceive pregnancy apps useful, they develop a positive attitude towards them. Effort expectancy was found to be a significant predictor of performance expectancy. Effort expectancy was not found to be a significant predictor of attitude. This result is not in affirmation with studies that have indicated a strong relationship between the effort expectancy and attitude to use online services (Ruane & Wallace, 2013;To et al., 2007;Lee, 2009). A reason to this could be as women were educated, belonged to cities and thus, were familiar with the use of mobile applications. Social influence was significantly related to attitude. Women rely on expertise by friends for making information seeking choices during pregnancy (Tiidenberg, 2017; Lupton,2017; Lupton & Pedersen,2016). Users did not feel that the access to knowledge and resources would help them improve performance. Facilitating conditions did not have an effect on performance expectancy. The relationship between facilitating conditions and behavioural intention was significant. The relationship between personal innovativeness and behavioural intention was significant. This implies women are wanting to learn new techniques to get more information about their foetus and manage their health well. The relation between perceived risk and behavioural intention was insignificant. Women usually will learn new things for the baby. Most women did not share their photos on these apps and thus did not fear loss of information.

Conclusion

From a theoretical perspective, this study adds to the field in two additional ways. First, the study extends UTAUT by adding perceived risk and personal innovativeness in IT to understand user's behavioural intention to use pregnancy apps. Second, the findings extend the existent knowledge on the relationships of UTAUT in the context of mobile pregnancy apps in a developing country.

  Personal Innovativeness in IT describes the tendency of an individual to adopt a product/service relatively before others (Jackson, Mun & Park, 2013; Lassar et al.,2005, Agarwal & Prasad, 1998; Rogers,1983). Agarwal and Prasad (1998) referred to PI as the degree to which a person believes that he/she is positively predisposed towards the use of new technologies. The extent to which a user is open to experimenting, experiencing new technologies indicates their novelty and innovativeness-seeking abilities.

  As the scale items in the study were modified to suit the context of this study, it is essential to test the validity and reliability of the scale. Tests for the construct validity, face validity, content validity, discriminant and convergent validity were performed. Multiple items were used to measure constructs, in order to increase the reliability of the construct. These were measured on a five-point Likert scale with anchors ranging from 1= "Strongly Disagree" to 5= "Strongly Agree". The construct validity is met by examining discriminant and convergent validity (Forner & Larcker, 1981).

	4	Research Methodology
	4.1 Instrument Design
	Instrument developed for this study borrowed scale items from previously established
	studies.
		mHealth, a
	number of studies (Hajiheydari & Ashkani, 2018; Canhoto & Arp, 2017; Du et al.,
	2016; Zhao, Ni & Zhou, 2018; Deng, Zhang & Zhang, 2012) have supported the rela-
	tionship between attitude and behavioural intention. Considering the importance of at-
	titude in IS adoption research, the following hypothesis is proposed:
	H12: Attitude is positively associated with Behavioural Intention to use pregnancy apps

Table 1 .

 1 Demographic details of the respondents

	Measure	Items	Frequency	Sample percentage
	Gender
	Female		220	100
	Age
	25 or Under	8	3.64
	26-35		179	81.36
	36-45		33	15
	Educational background
	HSC		6	2.72
	Graduate	67	30.45
	Post Graduate	135	61.36
	Other		12	5.45
	Occupation
	Student		6	2.72
	Working Professional	102	46.36
	Self-employed	33	15
	Homemaker	75	34.09
	Location

Table 2 -

 2 Reliability and convergent validity of the final measurement model

	Constructs Items	Item Load-ings	Cronbac h's Alpha	rho_A	Compo-site Reli-ability	Average Variance Extracted (AVE)
	Perfor-	PE1	0.775
	mance Ex-pectancy	PE2 PE3	0.84 0.832	0.749	0.755	0.857	0.666
		EE1	0.874
	Effort Ex-pectancy	EE2 EE3	0.897 0.881	0.899	0.9	0.929	0.767
		EE4	0.85
	Social Influence	SI2 SI3	SI1	0.892 0.888 0.731	0.787	0.795	0.877	0.706
	Facilitating Conditions	FC1 FC2		0.948 0.924	0.859	0.879	0.934	0.857
	Personal Innovative-ness	PI1 PI2 PI3 PI4		0.872 0.637 0.768 0.922	0.813	0.833	0.88	0.652

Table 4 : Results

 4

			B	t-statis-	P val-
	Hypothesis	Path	values	tics	ues	Result
		Performance Expectancy -> At-
	H1	titude	0.108 4.698	0.00 Supported
		Effort Expectancy->Perfor-
	H2	mance Expectancy	0.115 5.745	0.00 Supported
						Not
	H3	Effort Expectancy->Attitude	0.127 0.223	0.82	Supported
	H4	Social Influence ->Attitude	0.096 3.603	0.00 Supported
		Facilitating condition-> Perfor-				Not
	H5	mance Expectancy	0.13	0.101	0.92	Supported
		Facilitating condition-> Behav-
	H6	ioural Intention	0.068 2.334	0.02 Supported
		Personal Innovativeness in IT -				Not
	H7	>Behavioural Intention	0.075 0.119	0.91	Supported
		Performance Risk->Perceived
	H8	Risk	0.033 9.492	0.00 Supported
	H9	Time Risk->Perceived Risk	0.019 15.706	0.00 Supported
	H10	Privacy Risk->Perceived Risk	0.028 19.352	0.00 Supported
		Perceived Risk->Behavioural In-				Not
	H11	tention	0.073 1.298	0.20	Supported
	H12	Attitude->Behavioural Intention	0.101 6.482	0.00 Supported

Table 3 :

 3 Discriminant Validity -Fornell Larker criteria

		Attitude	Behavioural intention	Effort expec-tancy	Facilitat-ing condi-tions	Perceived Risk	Perfor-mance Risk	Perfor-mance Expec-tancy	Personal Innova-tiveness	Privacy Risk	Social Influ-ence	Time Risk
	Attitude	0.885*
	Behavioural Intention	0.702	0.93*
	Effort Expectancy	0.392	0.36	0.876*
	Facilitating conditions	0.315	0.346	0.592	0.936*
	Perceived Risk	-0.06	0.017	-0.355	-0.241	0.764*
	Performance Risk	-0.023	0.025	-0.187	-0.19	0.806 0.839*
	Performance Expectancy	0.607	0.463	0.671	0.406	-0.246	-0.146	0.816
	Personal Innovativeness	0.413	0.323	0.481	0.33	-0.09	0.01	0.299	0.807
	Privacy Risk	-0.055	0.016	-0.321	-0.174	0.929	0.607	-0.23	-0.081	0.879
	Social Influence	0.516	0.504	0.236	0.105	0.084	0.133	0.351	0.493	0.05	0.84
	Time Risk	-0.079	0.000	-0.418	-0.298	0.83	0.561	-0.257	-0.167	0.666	0.052	0.961
	Note: * Square root of AVE


Thirunavukkarasu Ramalingam
email: thirunavukkarasu.ramalingam@collins.com

Joel Otto
email: joel.otto@collins.com

Benaroya Christophe
email: c.benaroya@tbs-education.fr

Design Space Exploration for Aerospace IoT Products

Keywords: Internet of Things (IoT), Aerospace Systems, Design Space, IoT Value, Aerospace & Defense, Product Development

de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Digital transformation is the heart of business strategies and it begins with the executive mandate. There is a strong sense of urgency among executives as the threat of digitally enabled competitors and disruptive technologies remain high on the list of concerns [START_REF] Dunbrack | White Paper "IoT and Digital Transformation: A Tale of Four Industries[END_REF]. Internet of Things (IoT) is becoming more and more important in many industry sectors and domains (see Fig. 1) for the global IoT aerospace and defense market. Digital Technology evolution is happening rapidly and within no time will impact every business. However, digital transformation is essentially a commitment by organizations to innovate which would add value to their customers [START_REF] Ramalingam | Assessing the Potential of IoT in Aerospace[END_REF].

In the two decades since, the concept of IoT-which now encompasses a global ecosystem of sensors, embedded computers and smart devices that communicate with each other and share data-has exploded. Today, approximately 14.2 billion connected devices are currently in use around the world. By 2021, that figure is expected to jump to more than 25 billion due to a combination of cloud computing power, decreasing hardware cost and faster 5G data transfer that will accelerate growth. These devices include smart home appliances and wearable trackers, as well as a large variety of industrial innovations to improve manufacturing and operational efficiency. In the business world, IoT is already having a major impact, from changing the methods of business operations to transforming the way information is collected and exchanged. And every sector is impacted-including aerospace [16].

The Internet of Things is one of the biggest value generation technology in Aerospace and Defense (A&D) industry despite economic downturns. Long term increase in travel demand, development of new technologies and security threat for nations are fueling increase in aircraft production, defense budgets and the need for global supply chain [START_REF] Bénaroya | Aerospace Marketing Management[END_REF]. Aircraft manufacturers and operators are always on the lookout to improve the vehicle performance adopting more connected and smarter systems to achieve the fuel efficiency, zero downtime and route optimization [START_REF] Ramalingam | Assessing the Potential of IoT in Aerospace[END_REF].

As there are multifold opportunities opens up due to IoT product, the exploration of design space increases the engineer's understanding of the design problem [START_REF] Wang | Review of metamodeling techniques in support of engineering design optimization[END_REF]. The exploration must be done carefully due to a large number of design alternatives. A large system may have millions, if not billions of design alternatives, and it may have infinite alternatives for some design problems [START_REF] Kang | An approach for effective design space exploration[END_REF]. In addition, a larger complex system also has a larger number of design constraints that must be satisfied by every valid design alternative or solution. Furthermore, the analysis of these design alternatives includes higher computational costs.

Design space exploration (DSE) refers to the activity of exploration and investigating design alternatives prior to system implementation. DSE is used for rapid prototyping, optimization and system integration [START_REF] Kang | An approach for effective design space exploration[END_REF]. In rapid prototyping, DSE helps to generate several prototypes before the system implementation. By simulating these prototypes, engineers can increase the understanding of the impact of design decisions. In optimization, DSE can be used to eliminate the lower quality designs and selecting a set of design candidates for further analysis. The elimination is done by comparing one design to another using predefined metrics, for instance, design requirements. In system integration, DSE can be used to find legal assemblies and configurations that satisfy all global design constraints for the integration of multiple components into a working whole system [START_REF] Wang | Review of metamodeling techniques in support of engineering design optimization[END_REF]. A SITA air transport study found that 67% of airlines have plans to invest on airline IoT by 2030. One of the drivers is passenger demands for seamless in-flight connectivity. Stimulating this investment is an enabler for IoT, if the passenger demand wasn't there, airlines would not be adding the Internet to the airplane at the rate they are with 50 billion or more IoT devices expected to be in circulation in the world by 2030. In the aviation industry, IoT has the potential for operational improvements in areas including aircraft performance, air traffic control and maintenance schedulesall of which ultimately lead to greater customer satisfaction. Similarly, for supply chain management, IoT has implications for improving product flow and warehouse efficiency, resulting in improved customer experiences and loyalty [START_REF]Setting the Pace of Progress in Aerospace[END_REF].

Objective of the paper

The design of aircraft systems is an extremely complex task involving multidisciplinary studies that address the behavior of the design from mechanical, electrical, aero thermal and producibility aspects. In the early phase of aircraft systems development, engineers rapidly explore the design alternatives or design concepts to achieve design solutions that meet all requirements. This task is called design space exploration. It involves highfidelity physics-based numerical simulations to evaluate each design concept [6] [7]. Due to the time constraints of simulations, it is impossible to explore all these design alternatives. At the same time, exploring very few design concepts result in design issues in later stages. Thus, it is a crucial phase where engineers need support to evaluate design concepts and to prevent product failures.

The concept of IoT bringing new capabilities to interact with products or systems, independent of the core (certified) function of the system. A generator still has to generate, but IoT enables us to do more (predictive maintenance, performance tuning, etc.). Aerospace industries are currently adopting agile way of developing of minimum viable systems/products and are investing heavily to adapt IoT technologies which can yield long term value and benefit to customers. IoT technologies are evolving rapidly, and these have the opportunity to change the product landscape in the aerospace industry as well. However, there is no established approach to identify such systems/products. The objective of this paper to develop methodology that would help in developing minimum viable system/product by A&D industry to focus, while recognizing it must be adapted to fit alongside core certified designs without interference in the core function. There is strong motive in A&D industry to develop IoT products focusing on customer experience and enhanced product capabilities. This exploratory research is targeted towards develop design space exploration methodology for aerospace & defense IoT enabled products.

Methodology

A process that facilitates the creation of new functionalities, products, and services thereby making provision for better revenue streams, transform business models, drive measurably better outcomes for customers and every business sector including A&D [18]. The Methodology (see Fig. 2) i). Customer need identification ii). Product selection iii). Minimum Viable Product is developed around these three key steps. All these steps would be iterative till all the stakeholders are satisfied with identified product and its service to customer. First step Customer need identification is the critical one and majority of the time to be spent on this to understand customer interest. As there is no conclusive research findings available to find the best approach to identify the customer need regarding Aerospace IoT products, a survey has been conducted among aerospace and aviation professional group. Remaining two steps would takes less time if the welldefined problem emerges out of customer need identification.

Fig. 2. Design Space Exploration Methodology for Aerospace IoT

Customer need Identification

Customer Needs Identification is the process of determining what and how a customer wants a product to perform. Customer Needs are non-technical, and they reflect the customers' perception of the product, not the actual design specifications, although frequently they are closely related [13].

There are three ways to identify the customer need Aerospace IoT products (see Fig. 3) Option-1: Customer Feedback, Option-2: Product Improvement opportunities, Option-3: End user level opportunities.

Customer Need Identification

Product Selection

Minimum Viable Product

This document does not contain any export controlled technical data

As there is no conclusive research findings available to find the best approach to identify the customer need, a survey has been conducted among aerospace and aviation professional group. Customer Need Identification Generally item iii was communicated directly by customer, sometimes they aren't complaints, customer gives ideas that would make a good product to better. Company should effectively apply design thinking techniques and empathies customers untold needs to identify them. Survey results shows (see Fig. 5) that customer complaints is the area to look for potential IoT products.

Fig. 5. Survey Results on Customer Feedback Parameters

Product Improvement Opportunities

Products/process could be improved when there is a manual, resource intensive and repetitive tasks. Not all the mentioned opportunities could be automated due to compliance, safety and security requirements in A&D. There should be tradeoff between the product improvements and all the mentioned requirement and customer willingness to pay (Value). Shown below (see Fig. 6) survey results for product improvement opportunities. As per the survey results A&D companies could explore repetitive tasks to develop IoT products.

Fig. 6. Survey Results on Product Improvement Opportunities

End User Level Opportunities

Aerospace products end users are pilots, cabin crews, maintenance teams and passengers. Minimum equipment list tells flight and maintenance crews if they are able to dispatch an aircraft with a failed piece of equipment. IoT products have the potential to make airline employee tasks more effective or efficient, to engage and empower employees, and to allow the airline to optimize their workforce. Finally, these improvements can also make the passenger experience better, both by providing personnel more time to spend engaging customers, or to empower the passenger to self-serve, helping to retaining the customer for future business. Survey results (see Fig. 7) reveals that passenger experience is the area in which companies could explore and develop IoT products.

Fig. 7. Survey Results on End user level Opportunities

Product Selection

This step introduces the idea of a portfolio of products from a company and the methodology that helps them prioritize the order of products to invest in and what capabilities to introduce. The steps are iterative in nature (see Fig. 8) which would help to pick the most strategic products and determine the associated IoT maturity, Value offering and minimum viable benefits to the customer.

Step

IoT Value Identification

The value of the Internet of Things increases exponentially on a relatively stable and foreseeable maturity curve. What starts with a "dumb" (non-connected) device should in theory end in a completely cross-ecosystem automated solution [START_REF] Lasse | whitepaper 'IoT Strategy Primer: The new sources of value enabled by the Internet of things. Implications of competitive advantage and tools[END_REF]. Physical products have been isolated for hundreds of years in aerospace (i.e., not connected or digitally enhanced). Starting in the 1980s, many aerospace devices underwent a digitalization trend. Longevity of the product in A&D sector is the key to decide the maturity of the offerings as the decision should be made align with company product strategy and along with industry requirements (airworthiness, flight safety etc.)

Once the customer needs are identified, the firms should start with value maturity curve to select their product initial offerings and end user value identification. An IoT value curve (see Fig. 9) can help firms understand their current product offering to compare with competitors and position themselves with their new offerings. This curve also This document does not contain any export controlled technical data helps to develop product launch strategy while developing a roadmap for long run product success.

Fig. 9. Generic Internet of Things Value maturity curve [8]

Value potential of aerospace IoT varies; not every product will generate benefits by going all the way up the curve. As part of the iterative process, you have to be able to generate incremental value (now or future) as you move up each level of the curve.

Value curve analysis

Generic value curve analysis (see Fig. 10) establishes the relative value of offerings between IoT and Non-IoT products. From customer stand point the benefits that can be experienced along the value/maturity curve of IoT are numerous and they vary significantly by industry and application [START_REF] Lasse | whitepaper 'IoT Strategy Primer: The new sources of value enabled by the Internet of things. Implications of competitive advantage and tools[END_REF]. Once the product launch strategy is firmed up the next step is to characterize the customer value Identification for the specific aerospace applications between the IoT and Non-IoT products.

There are generic list of customer values in the Internet of Things given in appendix-1 which could be good starting point to analyze potential value for the customer. Plot the chart between the relative value offerings between the IoT & Non-IoT product (see Fig. 10) by selecting appropriate parameters (see Appendix-1) in the IoT product.

As an example for the benefit type process (see Appendix-1), category daily operations there are 6 different customer value parameters. These value parameter are plotted as relative value offerings between IoT and Non-IoT products.

Benefit analysis

In this step tradeoff study to be conducted to select the best possible cost effective approach to develop the IoT product. Along with minimum viable benefit to be selected which could help in developing the minimum viable product in the next step. There are multiple tools available to conduct trade-off study, subject matter experts input is the key to consider all the possible design options while evaluating the cost effective product development. Cost Benefit Analysis (CBA) is a process businesses use to analyze decisions. The business sums the benefits of a situation or action and then subtracts the costs associated with taking that action. A cost-benefit analysis will also factor the opportunity cost into the decision-making process. Opportunity costs are alternative benefits that could have been realized when choosing one alternative over another [START_REF]Cost Benefit Analysis[END_REF].

Development of Minimum Viable Product

The Minimum Viable Product (MVP) is a product with just enough features to satisfy early customers that provides feedback for future development [9]. Some experts suggest that in B2B, a MVP also means saleable. "It's not an MVP until you sell it. Viable means you can sell it". Gathering insights from an MVP is often less expensive than developing a product with more features, which increase costs and risk if the product happens to fail due to incorrect assumptions. An MVP can be part of a strategy and process directed toward making and selling a product to customers [START_REF] Thirunavukkarasu | How to Find Minimum Viable Product (MVP) in IoTA[END_REF]. In aerospace, this may require us to look for ways to trial a product prior to locking the design and completing the certification/qualification of the product. Once this process is complete, changes become much more costly. This is one of the key differences between aerospace and non-aerospace product development. Things to be considered to develop minimum viable product (see Fig. 11). Identifying the IoT elements and its constraint is the first step to start the product development effort. Then develop MV product with minimum feature identified in step 2.2.3.

This document does not contain any export controlled technical data Once the product is developed to satisfy the mentioned conditions it is time to test the product in the field. Customer feedback is an essential asset while maturing an MVP, as their inputs will help in improving the product in the market. Feedback could come in two areas. The first one is on the design of the product and user interface, which could improve the product usability. The second one is about the value realization in terms of its tangible and intangible benefits and cost reduction. Flow chart (see Fig. 12) shows the step by step approach to develop MVP. Logical conclusion for the product is when customer interest turned out to be 'NO'. When we reach this state it is suggested to go back to customer need identification and restart the process of design space exploration methodology.

Challenges in Aerospace IoT design exploration

The IoT is, in a sense, a hyper-scale System of Systems problem with multiple environments, requirements, governance structures and millions of stakeholders. Metcalfe's Law reminds us that three interconnected components result in a maximum of three interconnections, but with four the maximum number of interconnections rises to six, and with ten components, designers must deal with 45! This law illustrates how so any connected devices will challenge the system design [START_REF] Thirunavukkarasu | Assessing the Potential of IoT in Systems Engineering Discipline[END_REF]. Design space exploration methodology for aerospace products is having its own challenges some of them listed but not limited to such as criticality of its intended function and IoT capability cannot interfere with that function. Design Constraints such as size and weight of the product which could change the overall system design. Cyber security in the aerospace IoT becomes a critical element when developing connected products for aerospace. Data Ownership is another complication in IoT products as it generates terabytes of data with no defined guidelines on who has rights to use the data. Data access is important aspects in the IoT device if the product controls the function of the system if there is no deterministic/real-time data access.

Conclusion

Aerospace and Defense industries are increasing investments in developing IoT products to capture the increased value their product portfolio can deliver to customers and end users. IoT product development should pivot around the customer value as a center point. Systematic methodology and qualitative decision making while identifying the IoT product selection should drive customer adoption while minimizing the iteration in the product development cycle.

The exploratory research developed a methodology based on the both theoretical and industry expertise of authors. This paper proposed step by step approach starting from problem definition based on customer need identification narrowed down using detailed survey conducted among A&D and IoT experienced audience. Then the product selection three step approach IoT value Identification, Value curve analysis and Benefit analysis.

This document does not contain any export controlled technical data Finally developing the minimum viable product with bare minimum feature could reduce the product launch time and obtain the customer feedback to improvise the product with increased value and benefit the customer with bottom line growth. Survey results indicates that focusing on both end user level opportunities and delivering operational efficiencies are the key areas to consider for the next potential IoT aerospace product.

The limitations of the study is customer need identification as there is lack of robust methodology to identify customer need regarding Aerospace IoT products. Though authors used survey techniques to narrow down the pain points however there are limitation to use the survey results. Another limitations is due to value curve analysis as the graph is plotted using relative value offering which is heavily depend on the Aerospace and Defense expertise and their knowledge in IoT which would play major role completing the value curve analysis. Future scope of research would be to develop a robust framework for customer need identification and extend the analysis to more international markets.

Fig. 1 .

 1 Fig. 1. Global IoT Aerospace & Defense Market [5]

Fig. 3 .

 3 Fig. 3. Customer Need Identification methodology for Aerospace IoT

Fig. 4 .

 4 Fig. 4. Survey Participant details2.1.2 Customer feedbackCustomer feedback could be obtained in multiple ways, mentioned below three best possible ways.i). Field failure report ii). Corrective action board iii). Customer complaintsAll the mentioned items shows the unhappy customers and their unsatisfied needs. Items i & ii are generally received as a document from company nodal point like MRO, Business Development team etc. While analyzing these documents one should pay attention through written language and think like customer to identify their pain areas.

Fig. 8 .

 8 Fig. 8. Proposal of product selection methodology for Aerospace IoT

Fig. 10 .

 10 Fig. 10. Generic Value curve analysis for IoT & Non-IoT product

Fig. 11 .Fig. 12 .

 1112 Fig. 11. Methodology for Development of Minimum Viable Product

This document does not contain any export controlled technical data

Appendix


Balakrishnan Unny
email: balakrishnan@nirmauni.ac.in

Bhajan Lal

Blockchain in Supply Chain Management: A Review of the Capability Maturity Model

Keywords: Blockchain, Supply Chain Management, Capability Maturity Model, Information System Implementation

With the growing popularity of cryptocurrencies, Blockchain systems have found a new audience within the business environment. The decentralized ledger technology that allows the creation of verifiable transactions has extraordinary applications within business. In this paper the authors have attempted to identify the core benefits of using Blockchain technology to improve Supply Chain Management (SCM). With industry 4.0 transformation in its preliminary stages, a discussion on how Blockchain systems can benefit SCM is important. Like any other information system, Blockchain implementation is not just limited to infrastructure changes but also requires organisational wide changes to reap the benefits of the new system. The inherent technological issues associated with Blockchain are preventing many organizations from adopting Blockchain systems at a large scale. The authors have presented a model to guide the implementation of the Blockchain based systems using the industry accepted Capability Maturity Model (CMM) as the reference.

Introduction

The Global Supply Chain Forum (GSCF) defines supply chain management as "the integration of key business processes from end user through original suppliers that provides products, services, and information that add value for customers and other stakeholders" [START_REF] Lambert | Issues in Supply Chain Management[END_REF]. Chief among the issues faced in supply chain management is the issue of business process links as they have a significant impact on the performance of the supply chain [START_REF] Lambert | Issues in Supply Chain Management[END_REF]. To achieve a truly integrated supply chain, commitment from all the members is essential.

To create an integrated supply chain, the buying organization has to invite its business partners into their organization. This goes beyond just sharing information about the products and requirements thereby including business partners into buying organization's operational activities. This integration does come with a challenge since there is limited reciprocity from the business partners. The business partners are provided extensive information about the inner workings of the buying organization while they can choose to remain firewalled. This is a unique problem since the objectives of both parties may not be always in line.

Additionally the creation of such an integrated supply chain often puts the buying organization at the risk of being held since there is an overwhelming reliance on the partners [START_REF] Keah Choon | Supply chain management: Practices, concerns, and performance issues[END_REF]. The sheer complexity of managing business partners through the entire supply chain is compounded by the limited willingness to create an integrated supply chain process [START_REF] Lambert | Issues in Supply Chain Management: Progress and potential[END_REF].

The contemporary literature identifies various methods to implement and sustain an integrated supply chain. There is consensus that supply chain integration requires both managerial and technical solutions [START_REF] Turkulainen | Managing Internal Supply Chain Integration: Integration Mechanisms and Requirements[END_REF][START_REF] Gunasekaran | Improving supply chain performance through management capabilities[END_REF][START_REF] Lambert | Supply Chain Management: Implementation Issues and Research Opportunities[END_REF]. To improve the supply chain performance, contemporary organizations have been looking at technology driven solutions and they found varying amount of success.

Introduction of cyber-physical system in production and operations environment with the aim of interconnecting human beings and machines in a globally accessible network is referred to as "industry 4.0". The technologies involved in industry 4.0 include "Advanced Robotics, Artificial Intelligence, Hi-Tech Sensors, Cloud Computing, Internet of Things (IoT), Data Capture and Analytics, Digital Fabrication, Mobile Devices, Software As A Service and Blockchain" [START_REF] Tjahjono | What does Industry 4.0 mean to Supply Chain?[END_REF]. Supply chain 4.0 is the application of the above mentioned technologies in SCM to improve performance and customer satisfaction [START_REF] Alicke | Supply Chain 4.0the nextgeneration digital supply chain[END_REF].

Supply chain 4.0 creates a disruption in the traditional supply chain environment which requires organizations to radically rethink and redesign their business functions That leads to leads to potential cost reduction in various domains of supply chain [START_REF] Tjahjono | What does Industry 4.0 mean to Supply Chain?[END_REF][START_REF] Alicke | Supply Chain 4.0the nextgeneration digital supply chain[END_REF]. The supply chain 4.0 benefits have been achieved thus far on the traditional business intelligence infrastructure. The tradition infrastructure relies on centralized data repositories which connects all the partners of the supply chain. However centralized system could be easily manipulated by anyone rendering the data untrustworthy. With the use of Blockchain in an organization can create a decentralized platform for recording transactions that cannot be manipulated.

The supply chain 4.0 benefits have been achieved thus far on the traditional business intelligence infrastructure. The tradition infrastructure relies on centralized data repositories which connects all the partners of the supply chain. However, centralized system could be easily manipulated by anyone rendering the data untrustworthy. With the use of Blockchain in an organization can create a decentralized platform for recording transactions that cannot be manipulated.

There is also an issue of transparency which needs to be addressed. The question of "how much can I actually trust the information coming from my supply chain partner?" is a difficult question for any organization to answer. One of the solutions to such a conundrum is the use of modern technology such as Blockchain. The transparency that the Blockchain systems provide will enable organizations to improve the supply chain. Many organizations are now looking at Blockchain to ensure the validity of the information being shared across organizations. Recently Walmart had implemented Blockchain based systems in collaboration with IBM to track the movement of produce from Chinese farms to their shelves [START_REF] Jing | Here's a peek at Walmart's game-changing plan to trace food from China's farms to store shelves[END_REF].

Blockchain Technology and Supply Chain Management

The last two decades of technological innovations in the area of peer-to-peer (P2P) networks and cryptography has led to the development of the Blockchain. The fundamental technological principle of Blockchain is the distributed, encrypted database, which cannot be reversed and is incorruptible. The following three technological concepts form the underpinnings of Blockchain technology; Decentralized Databases, Blockchain, Proof of Work/Stake [START_REF] Morabito | Business Innovation Through Blockchain[END_REF]. Bitcoin was the first system to utilize the Blockchain data structure and has become the blueprint for future Blockchain implementations.

The starting point of the Blockchain is the concept of Distributed Ledger Technology in which each participant of the P2P network has access to a shared ledger. This provides a secure environment to perform transactions without relying on a third-party service provider [START_REF] Ølnes | Blockchain in government: Benefits and implications of distributed ledger technology for information sharing[END_REF]. Every transaction performed in the network uses public key encryption and digital signatures to validate the transaction.

The process of validation of the transaction is based on decentralized consensus. Instead of the traditional centralized consensus which was built based on database rules; authority and trust of ensuring validity of a transaction is moved to the decentralized network. Each node in the network is enabled to record transactions continuously and sequentially in a public "block" creating a unique "chain". Each successive block contains the unique fingerprint of the previous block in an encrypted format (hash). This combination of cryptography and Blockchain system removes the need for a centralized intermediary for authentication of transactions [START_REF] Mougayar | The Business Blockchain: Promise, Practice, and Application of the Next Internet Technology[END_REF].

In order to ensure the validity of information being created by the nodes, Blockchain can use the network security protocol called "proof of work". This protocol essentially prevents attackers from creating transactions which are not authenticated. This protocol uses computational power from all the nodes to solve a complex mathematical function which serves as the prerequisite for the Proof of Work scheme. Proof of Stake scheme serves as an alternative to Proof of Work scheme which revolves around the entities that holds stake within the network. In certain conditions a hybrid of both the schemes can also be used to authenticate the transactions [START_REF] Morabito | Business Innovation Through Blockchain[END_REF].

Contemporary literature is littered with examples of how Blockchain systems can be used to create a digital platform where authenticated transactions and records could be created [START_REF] Lemieux | Trusting records: is Blockchain technology the answer?[END_REF]. We can classify the areas in which Blockchain has been implement into 10 overall themes namely "Finance, Citizenship Services, Integrity Verification, E-Governance, IoT, Healthcare, Privacy & Security, Business, Education and Data management" [START_REF] Casino | A systematic literature review of blockchain-based applications: Current status, classification and open issues[END_REF]. The Estonian government has created a Blockchain based e-residency program [START_REF] Sullivan | E-residency and blockchain[END_REF]. In the Indian context, the Government of Andhra Pradesh has launched an ambitious scheme to use Blockchain technology to manage land records and streamlining vehicle registrations [START_REF] Haridas | This Indian City Is Embracing BlockChain Technology --Here's Why[END_REF]. The impact of Blockchain technology on financial services industry beyond cryptocurrencies [START_REF] Guo | Blockchain application and outlook in the banking industry[END_REF][START_REF] Petersen | Blockchain Technology in Supply Chain Traceability Systems: Developing a Framework for Evaluating the Applicability[END_REF]. Blockchain could modernize the utilities grid to create a new era of decentralized power [START_REF] Basden | How utilities are using blockchain to modernize the grid[END_REF]. The Blockchain based sharing services can benefit smart cities as well [START_REF] Sun | Blockchain-based sharing services: What blockchain technology can contribute to smart cities[END_REF]. Blockchain can also be considered as a general-purpose technology i.e. it can be used to create a platform without a powerful intermediary wherein the privacy risk and censorship risk is drastically reduced thereby reducing the cost of networking [START_REF] Catalini | Some Simple Economics of the Blockchain[END_REF][START_REF] Chen | Blockchain tokens and the potential democratization of entrepreneurship and innovation[END_REF]. In all the above examples, the traditional centralized intermediary based authentication is replaced with Blockchain. In all cases the authors have identified that the ability to create transaction which are authenticated increases the transparency of the transaction and the underlying service as well. In creating the transparency in transactional systems, the Blockchain has business value in record keeping of static information and maintaining a registry of tradeable information [START_REF] Carson | Blockchain beyond the hype: What is the strategic business value? Digital McKinsey[END_REF].

Fig. 1. Blockchain Process

The Blockchain based systems are able to ease paperwork processing, identify counterfeit products, facilitate origin tracking thereby transforming enterprise supply chains [START_REF] Kersten | Blockchain in Logistics and Supply Chain: Trick or Treat? Digitalization in Supply Chain Management and Logistics[END_REF][START_REF] Lee | How the Blockchain Revolution Will Reshape the Consumer Electronics Industry [Future Directions[END_REF]. The container logistics company A.P. Moller -Maersk is using a Blockchain based system to reduce the paperwork and associated cost for container shipment [START_REF] Moller -Maersk | TradeLens blockchain-enabled digital shipping platform continues expansion with addition of major ocean carriers Hapag-Lloyd and Ocean Network Express[END_REF]. The Blockchain enabled supply chain have the potential to improve the strategic performance dimensions including Risk reduction, Sustainability and Flexibility [START_REF] Madhwal | Blockchain and supply chain management: Aircrafts' parts' business case[END_REF][START_REF] Kshetri | 1 Blockchain's roles in meeting key supply chain management objectives[END_REF].

The following studies have shown the potential benefits of using Blockchain to improve supply chain management. The first study, explains the use of Blockchain system in Pharma supply chain [START_REF] Bocek | Blockchains everywhere -A use-case of blockchains in the pharma supply-chain[END_REF]. In their work, the authors were able to present the case of a start-up that uses IoT sensor devices to leverage Blockchain technology to bring about the benefits of fraud detection and identity management. The second study, detailed the utilization and development of RFID based Blockchain system to build agri-food supply chain traceability [START_REF] Tian | An Agri-food Supply Chain Traceability System for China Based on RFID & Blockchain Technology[END_REF]. A similar conceptual study was conducted that detailed how Blockchain system could be used to modernize the halal supply chain [START_REF] Tieman | Leveraging Blockchain Technology for Halal Supply Chains[END_REF]. All three studies look into the different parts of the supply chain's transparency issue. The benefits of Blockchain technology are not limited to transparency alone but also include durability, immutability and process integrity [START_REF] Abeyratne | Blockchain ready manufacturing supply chain using distributed ledger[END_REF]. For example, use of Blockchain makes it much more difficult for illicit or counterfeit products to enter an otherwise legitimate supply chain [START_REF] Apte | Will blockchain technology revolutionize excipient supply chain management?[END_REF][START_REF] Lu | Adaptable Blockchain-Based Systems: A Case Study for Product Traceability[END_REF]. Having a transparent supply chain enables an organization to plan better. In a typical supply chain, multiple parties are involved and this generates extensive amounts of documentation. Small error in the paper documents may result in delays or hold up of payments. Blockchain eliminates this chance of an error thereby reducing the extra price of verification which is required for paper-based systems. Blockchain based systems can also reduce the procure-to-pay gaps with the use of smart contracts. This validated data storage provides organizations ability to create better data analysis platforms which can reduce the inventory cost by better forecasting.

One of the critical challenges involved in the implementation of Blockchain systems is the cost. As discussed before Blockchain systems require complete overhaul ng of the IT infrastructure which is expensive [START_REF] Alicke | Blockchain technology for supply chains -A must or a maybe? McKinsey & Company Operations Extranet[END_REF]. The second challenge is with the technology itself; the network-based validation reduces the throughput of Blockchain transactions as well as latency. With the current setup of Blockchain achieving the performance parameters of centrally controlled systems is extremely difficult [START_REF] Mendling | Blockchains for business process management -Challenges and opportunities[END_REF]. The regulatory uncertainty is definitely a challenge within the Indian context as the India Government has recently decided not to recognize cryptocurrencies which is based on Blockchain. The fact that Blockchain implementation involves multiple business partners is a barrier to the implementation of the project. Combined with the lack of acceptance within the industry driven primarily by the uncertainty of benefits makes the implementation project extremely challenging [START_REF] Kersten | Blockchain in Logistics and Supply Chain: Trick or Treat? Digitalization in Supply Chain Management and Logistics[END_REF]. Even with these challenges the benefit of creating a transparent supply chain is pushing major players in the market to adopt Blockchain as a viable alternative to traditional information systems.

The Capability Maturity Model for implementing Blockchain

The Capability Maturity Model for Blockchain implementation has four categories and their corresponding maturity levels [START_REF] Wang | A maturity model for blockchain adoption[END_REF] and the authors have added a fifth category namely "Skills".to the existing model.

Fig. 2. Capability Maturity Model for Blockchain implementation

First of the categories is network; with the Blockchain, organizations will have to transform their existing centralized networks to enable peer to peer communication. Additionally, along ensuring the reliability of the network and load management it is important to ensure that proper hybrid network is designed. It is impractical to replace the entire client-server architecture with the P2P architecture. So, while designing the network there should be synergy with the operations of the organization [START_REF] Loo | The case for a hybrid P2P search infrastructure[END_REF][START_REF] Hoogeweegen | Modular Network Design: Using Information and Communication Technology to Allocate Production Tasks in a Virtual Organization[END_REF][START_REF] Diabat | A perishable product supply chain network design problem with reliability and disruption considerations[END_REF].

Second category is the information systems wherein both technical and managerial considerations are required. The chief among the technical considerations is the requirement of upgrading the infrastructure which includes the computer systems, interfaces and storage. There is also requirement that Blockchain system should not be standalone which requires extensive integration across the legacy systems. While considering adoption of Blockchain based information systems it's important for an organization to understand the business requirement. Therefore, the information technology strategy and organizational strategy should have a convergence as well [START_REF] Tan | E-governance: towards a strategic convergence of stakeholder interests[END_REF][START_REF] Drnevich | Information Technology and Business-Level Strategy: Toward an Integrated Theoretical Perspective[END_REF][START_REF] Clohessy | De)Mystifying the Information and Communication Technology Business Model Concept[END_REF][START_REF] Clohessy | Contemporary Digital Business Model Decision Making: A Cloud Computing Supply-Side Perspective[END_REF].

Third category deals with the computing methodologies. With Blockchain, generating standards that are acceptable across companies is a challenge [START_REF] Alicke | Blockchain technology for supply chains -A must or a maybe? McKinsey & Company Operations Extranet[END_REF]. The computational complexity involved in managing a complex Blockchain is not just limited to the volume and the speed it extends to ensuring data accuracy as well.

Fourth category is the security and privacy. Using a public Blockchain would render confidential information at risk of exposure. The complexity of securing all the nodes in the network is also extensive [START_REF] Lin | A survey of blockchain security issues and challenges[END_REF][START_REF] Joshi | A survey on security and privacy issues of blockchain technology[END_REF][START_REF] Li | A survey on the security of blockchain systems[END_REF]. Like any information system the security of the network is dependent on the security of each device [START_REF] Sharma | Study and Implementation of Face Detection Algorithm Using Matlab[END_REF]. At the same time a technological solutions alone are insufficient in creating a resilient environment therefore it requires an enterprise level integration of active, passive and recovery mechanisms [START_REF] Unny | Strategies for improving cyber resilience of data intensive business information systems[END_REF].

Fifth category in the proposed model is Skills. With the complete redesign of the supply chain environment the employees within all the participating organizations have to develop new skills to leverage the benefits of Blockchain. Improved cross functional communication and leadership skills would strengthen the collaboration between the relevant parties and prepare the organization for the digital transformation which is required. CMM for People (P-CMM) created by the Software Engineering Institute of United States Carnegie Mellon university is designed to judge the maturity of enterprise human resource processes [START_REF] Zhang | Design of human capability maturity analysis system online P-CMM model[END_REF]. Using the P-CMM as a reference we have identified the maturity levels for the development of the skills required in implementing and maintaining Blockchain based systems. The starting stage is having the requisite training for employees and there after identifying the competencies required and thereby ensuring that competencies are mapped and continues improved [START_REF] Curtis | People capability maturity model (P-CMM)[END_REF]. The updated CMM for Blockchain with additional parameters is represented in Figure 2. For a successful implementation of Blockchain systems, organizations have to display maturity in all categories described above along with a clear business rationale for the implementation.

4

Discussion and future work

Digital transformation of the entire supply chain involves both high complexity as well as high novelty whereas single use systems require far less effort from the organizations [START_REF] Iansiti | The truth about blockchain[END_REF]. Blockchain Technology has value in business functions that have been struggling to create a system of recording and validating transactions without an intermediary. Blockchain based systems require an enterprise level transformation for a successful implementation. However, comparison between the approaches used in implementing Blockchain based systems and traditional approaches is challenging [START_REF] Wang | Influence of personality and risk propensity on risk perception of Chinese construction project managers[END_REF].

The number of organizations that have implemented Blockchain based systems is limited. Hence an empirical validation of the model will be challenging. However, a case study-based validation of the model may be considered. Also, the CMM proposed in the present study needs to be expanded to include the other management categories as well.


Vranda Jain

Nidhi Singh

Sajeet Pradhan
email: prashant@iimtrichy.ac.in

Prashant Gupta

FACTORS INFLUENCING AI IMPLEMENTATION DECISION IN INDIAN HEALTHCARE INDUSTRY: A QUALITATIVE INQUIRY

Keywords: Artificial Intelligence, Healthcare, Disruptive Technology

Recently, Artificial Intelligence has started showing up in the realm of health care innovations with researchers exploring its potential for healthcare organisations. Since healthcare possess industry specific features, the context and challenges of exploring AI adoption in healthcare is different than other industries. This study intends to conduct grounded theory to review the strategic, cultural, environmental and operational factors towards adoption of AI technology in Indian hospitals. The study uses purposive sampling to conduct semi-structured in-depth interviews of the decision makers of various healthcare organizations across the country. The present study would contribute to the existing literature on the impact of disruptive technology on healthcare as it would be a comprehensive study assessing the determinants of adoption in hospitals.

Introduction

Artificial intelligence (AI), the defining technology of 21 st century, with its learning, reasoning and adaptation capabilities, has transformed industries worldwide [START_REF] Kakkad | Biometric authentication and image encryption for image security in cloud framework[END_REF]. Academic literature in AI domain has explored technology adoption across sectors like agriculture [START_REF] Talaviya | Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides[END_REF], architecture, engineering and construction [3], energy [4], smart cities [5], communications [6] and manufacturing [7]. Recently, AI has started capturing attention in the realm of health care innovations [8]. This is supported by an Accenture report, 2017 estimating a CAGR of 40 per cent in AI health market through 2021. Another study by [9] reckon AI health market will grow ten times during 2020-25.

Since healthcare possess industry specific features [10,11], the context and challenges of exploring AI adoption in healthcare is markedly different than other industries [12]. While assessing the technology adoption challenges in healthcare sector, [START_REF] Kuo | Opportunities and challenges of cloud computing to improve health care services[END_REF] found issues pertaining to management, technology, security and legal aspects. Organisational planning and inter departmental integration [START_REF] Wang | Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations[END_REF], quality of patients' care [START_REF] Tsai | Acceptance and resistance of telehealth: The perspective of dual-factor concepts in technology adoption[END_REF], management support [START_REF] Tsai | Acceptance and resistance of telehealth: The perspective of dual-factor concepts in technology adoption[END_REF] and an assessment of financial and economic benefits [START_REF] Varabyova | The determinants of medical technology adoption in different decisional systems: A systematic literature review[END_REF] have been identified as few of the other challenges in AI adoption in healthcare.

Recent studies have examined the transformational impact of using AI technology in healthcare industry. AI technology has immense potential for healthcare organisations-improving service quality [START_REF] Martins | An artificial-intelligence-based method for assessing service quality: insights from the prosthodontics sector[END_REF], efficiency and productivity [START_REF] Kelly | Key challenges for delivering clinical impact with artificial intelligence[END_REF]; effective resource management [START_REF] Dwivedi | Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy[END_REF]; accelerating health responses [START_REF] Noorbakhsh-Sabet | Artificial intelligence transforms the future of health care[END_REF]; automating clinical documentation and upgrading medical records management [START_REF] Luh | Clinical documentation and patient care using artificial intelligence in radiation oncology[END_REF]; improving communication and coordination among users [START_REF] Hamet | Artificial intelligence in medicine[END_REF]; reducing errors [START_REF] Wiljer | Developing an artificial intelligence-enabled health care practice: rewiring health care professions for better care[END_REF] and costs [START_REF] Bini | Artificial intelligence, machine learning, deep learning, and cognitive computing: what do these terms mean and how will they impact health care?[END_REF]. At the country level, this would improve public health surveillance [START_REF] Bhattacharya | Strengthening public health surveillance through blockchain technology[END_REF] and encourage targeted research and development. For a developing and world's second most populous country, India, currently undergoing a demographic, economic and epidemiological transition and imposing an increased pressure on existing healthcare infrastructure, such outcomes are much awaited.

Although extant literature highlights the positive impact of AI technology on healthcare, its benefits in Indian context are yet to be capitalized. Existing studies have focused on other segments of healthcare like diagnostics, telemedicine, medical insurance, pharmaceuticals and medical equipment. To the best of our knowledge, there is no systematic study exploring the impact of AI applications on hospitals from an administrator's glance. While studying healthcare centres, though few studies have focused on the existing challenges and barriers to implementation, limited attention has been given to discuss the factors affecting the decision to adopt AI technology. The current study adopts a multi-dimensional approach towards adoption of AI technology in Indian hospitals.

Literature Review

Artificial intelligence (AI) is gradually changing business functions in various sectors particularly, healthcare [START_REF] Yu | Artificial intelligence in healthcare[END_REF]. In line with recent technological innovations, the role of Artificial intelligence (AI) and its incorporation with various applications such as IoT [START_REF] Alsamhi | Survey on artificial intelligence based techniques for emerging robotic communication[END_REF], robots [START_REF] Zengul | Longitudinal analysis of high-technology medical services and hospital financial performance[END_REF], data mining [START_REF] Ye | Psychosocial factors affecting artificial intelligence adoption in health care in China: Cross-sectional study[END_REF] are discussed in various studies. According to [START_REF] Cubric | Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study[END_REF], several empirical studies have been written on the role of AI in the last two decades in sectors such as IT, financial services, healthcare, etc. These studies focused mainly on the drivers for the AI adoption largely economic and technical drivers [START_REF] Zayyad | Factors affecting sustainable adoption of e-health technology in developing countries: an exploratory survey of Nigerian hospitals from the perspective of healthcare professionals[END_REF][START_REF] Reddy | Artificial intelligenceenabled healthcare delivery[END_REF]. According to [START_REF] Maita | Process mining through artificial neural networks and support vector machines[END_REF], an increase in productivity and innovation are the potential drivers of AI adoption. Similarly, [START_REF] Merkert | A survey of the application of machine learning in decision support systems[END_REF] mentioned about the efficiency of AI enables a system in reducing costs related to human errors, equipment, and taking the decision and making accurate predictions at various points.

Although, these studies confirmed that AI brings revolution, but, many challenges lie ahead too [START_REF] Jiang | Artificial intelligence in healthcare: past, present and future[END_REF][START_REF] Cubric | Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study[END_REF]. To this end, few significant studies advanced their work by elaborating on the barriers of AI mainly technical and social aspects of implementation [START_REF] Rao | Sizing the prize: What's the real value of AI for your business and how can you capitalise[END_REF][START_REF] Yu | Artificial intelligence in healthcare[END_REF]. [START_REF] Memeti | Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review[END_REF] confirmed that the availability of expertise human assistance and extensive infrastructure support are few main barriers due to the excessive cost associated with it. Another important barrier that is largely discussed is the serious social implications of AI adoption in the healthcare sector [START_REF] Paré | Knowledge barriers to PACS adoption and implementation in hospitals[END_REF][START_REF] Zengul | Longitudinal analysis of high-technology medical services and hospital financial performance[END_REF][START_REF] Alhashmi | Implementing artificial intelligence in the United Arab Emirates healthcare sector: an extended technology acceptance model[END_REF]. [START_REF] Rao | Sizing the prize: What's the real value of AI for your business and how can you capitalise[END_REF] confirmed that lack of knowledge, trust, and ignorance of stakeholders are a few main social barriers to the adoption. Also, increased job insecurity among doctors or nurses is largely discussed in this domain [START_REF] Maalouf | Robotics in nursing: a scoping review[END_REF].

Another important social barrier is the emotional dependence of patients on hospitals staffs that are missing in non-human caregivers [START_REF] Paré | Knowledge barriers to PACS adoption and implementation in hospitals[END_REF][START_REF] Yu | Artificial intelligence in healthcare[END_REF][START_REF] Cubric | Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study[END_REF]. According to [START_REF] Jiang | Artificial intelligence in healthcare: past, present and future[END_REF], the biggest challenge to the healthcare sector is the social acceptance of AI and robotics models among the staff and patients. Accuracy and effective monitoring of disease assessment by robots and AI models are few other social challenges discussed in previous studies [START_REF] Malhotra | Software maintainability: Systematic literature review and current trends[END_REF]. Also, the privacy of data and health safety issues were stressed in a few significant healthcare studies [START_REF] Ye | Psychosocial factors affecting artificial intelligence adoption in health care in China: Cross-sectional study[END_REF]. Most importantly, behavioral change challenges have been emphasized in most of the studies [START_REF] Laranjo | Conversational agents in healthcare: a systematic review[END_REF].

There is a lack of reviews outside of the technical and social barriers in the literature [START_REF] Cubric | Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study[END_REF]. Studies largely focused on technical aspects/barriers of the adoption [START_REF] Yu | Artificial intelligence in healthcare[END_REF]. But, pieces of evidence are available where the effectiveness of new technologies have been challenged due to cultural biases and environmental issues despite their technical benefits are established [START_REF] Reddy | Artificial intelligenceenabled healthcare delivery[END_REF][START_REF] Sun | Mapping the challenges of Artificial Intelligence in the public sector: Evidence from public healthcare[END_REF]. Recently, [START_REF] Alhashmi | Implementing artificial intelligence in the United Arab Emirates healthcare sector: an extended technology acceptance model[END_REF] used discussed various organizational, managerial, operational, strategic aspects for the successful implementation of AI in the healthcare sector.

Based on the findings of a systematic literature review done by [START_REF] Cubric | Drivers, barriers and social considerations for adoption in business and management: A tertiary study[END_REF],

there is a need to increase the understanding of strategic, cultural, political, environmental implications and challenges when selecting AI and other disruptive technologies for the healthcare sector. Technical, cost, and social barriers are given high significance in the academic literature [START_REF] Reddy | Artificial intelligenceenabled healthcare delivery[END_REF]. But, a few studies also indicated the impact of AI on management and environment by including variables like job insecurity, safety, errors, trust and dependence [START_REF] Alhashmi | Implementing artificial intelligence in the United Arab Emirates healthcare sector: an extended technology acceptance model[END_REF][START_REF] Sun | Mapping the challenges of Artificial Intelligence in the public sector: Evidence from public healthcare[END_REF]. Taking in a clue, the present study is a pioneer in its attempt to suggest a comprehensive model by including strategic, cultural, environmental, and operational challenges to the AI adoption in hospitals.

Research Method

In order to have first-hand understanding of the challenges, possibilities and expectations of the various stakeholders regarding the introduction of AI in their organization, we intend to conduct grounded theory by conducting semi-structured in-depth interviews of the decision makers of various healthcare organizations across the country. The interviews will be audio recorded or video recorded. The recorded interviews will be converted into transcripts (textual data). We intend to use NVivo software to analyze the data in order to generate primary codes followed by axial coding which further leads to themes which helps us create a thematic model.

In addition, we also intend to carry out an "external audit" in which a separate coding is done by an external subject matter expert (SME) to verify that findings of the study are impartial and free from the bias and prejudice of the researchers. To achieve that we will conduct inter-rater reliability test and mention the kappa coefficient which offers the degree of commonality between the study scholars' codes and themes and that of the codes and themes of the external SME. The findings of the study are then discussed and elaborated further by the existing literature.

Probable Outcomes

A thorough review of strategic, cultural, environmental and operational factors would contribute in identifying the factors influencing the decision to adopt AI technology in Indian hospitals. Strategic factors like the level of competition between players in industry and an overview of cost components in respective healthcare centre along with an understanding of leader's education and level of exposure would contribute in developing this understanding. Other dimensions like a scanning of external environment in terms of government support, human resource availability and the level of technological advancement coupled with operational factors like addressing interoperability and scalability challenges and reviewing customer support would help in addressing the research questions. Further, the efficacy of AI technology in hospitals in terms of data standardization and integration in clinical workflows, patient safety, cyber security and return on capital employed would create a ground work for implementing AI in hospitals.

Probable Implications

The present study offers few implications. First, the study helps to identify crucial factors for the successful implementation of AI in hospitals. Project managers and administrators in the health domain should review these contexts while using AI for their administrative and medical purpose. Hospital management should understand the role and significant of AI to fulfil the missing of the firm and gain competitive edge in the society. The study is beneficial as it assesses hospital administrators' views about AI technology and their preparation for the adoption in the long run. The study also indicates drivers and barriers of AI adoption in hospitals. Understanding of these variables is crucial from management perspective as AI adoption considered as a capital expenditure which involves huge cost and commitment by the stakeholders.

Darko

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