Miriam Erne

Zhiying Jiang

Vanessa Liu
email: vanessaliusw@suss.edu.sg

Investigating the Effect of User Reviews on Mobile Apps: The Role of Customer Led Innovation

Keywords: mobile apps, user involvement, customer led innovation, user reviews

User involvement has been made easy and common in the context of mobile applications (apps), where user reviews were often collected to enlighten apps developers on novel features. However, users might not always possess the required technical expertise to make commercially feasible suggestions. The value of user reviews also varied due to their unmanageable volume and content irrelevance. In this study, over 40,000 user reviews with 50 apps were analyzed to empirically examine the association between customer led innovation and the revenues from the apps. Our findings indicated that customer led innovation alone did not lead to significant changes in revenues. Its impact was only significant if the developers responded to the user reviews faster. These results contributed to the user involvement literature by highlighting the importance of the moderating effect of developer responsiveness. Apps developers could also benefit from our empirical evidence that proved the value of user involvement that enhanced innovativeness.

Introduction

Nowadays application distribution platforms such as Apple App Store and Google Play provide millions of different mobile applications (apps) to users. As of the fourth quarter of 2019, there were around 2.57 million apps for android users and 1.84 million apps for App Store users available [START_REF] Statista | Number of apps available in leading app stores as of 4th quarter 2019[END_REF]. Survival in such a "hyper-competitive" mobile market was challenging to apps developers [START_REF] Comino | Updates Management in Mobile Applications[END_REF]. Unwanted or unpopular apps could be phased out very shortly after launch, resulting in a waste of development cost and effort. To sustain competitiveness, it is therefore becoming increasingly important for apps developers to launch novel features that meet customer needs (e.g., see [START_REF] Chen | AR-miner: Mining Informative Reviews for Developers from Mobile App Marketplace[END_REF][17] [START_REF] Maalej | On The Automatic Classification Of App Reviews[END_REF].

One key channel for customers to voice out their feedback is through user reviews. Indeed, most apps actively elicit customer comments as they are enlightening to the apps developers in terms of novel features. As user needs vary significantly and the usage of the apps could differ across contexts, users may be a good source of creative ideas for development of innovative functionalities.

Despite its potential usefulness for performance enhancement, screening through user reviews could be challenging. For instance, online gurus like Facebook could generate as high as at least 2,000 user reviews per day [START_REF] Chen | AR-miner: Mining Informative Reviews for Developers from Mobile App Marketplace[END_REF]. The aspects covered in the reviews could be highly diverse, ranging from complaints about the price of the apps to the frequency of advertisements. Manual processing of these reviews is simply impossible. Management of user reviews could be overwhelming and costly to apps developers. Tools have accordingly been developed to enable automated categorization and mining of customer reviews [START_REF] Maalej | Bug Report, Feature Request, or Simply Praise? On Automatically Classifying App Reviews[END_REF] [START_REF] Maalej | On The Automatic Classification Of App Reviews[END_REF]. However, following up on user reviews remains highly time-and money-consuming. Is it really worth the resources to act upon the user reviews? Considering not all users are technically knowledgeable about apps development, could their involvement really offer constructive and commercially feasible suggestions for apps improvement?

User involvement is only appropriate if certain involvement roles and development conditions are fulfilled [START_REF] Ives | User Involvement and MIS success: A Review of Research[END_REF]. These conditions include, who should be involved, which type of software with which the users should be involved, and in which stage (i.e., when) of the software development the users should be involved. User involvement could be totally undesirable when technical expertise is needed. While the potential value of user feedback is not deniable, it may not always be economically justified for developers to translate user feedback into actual software features [START_REF] Ives | User Involvement and MIS success: A Review of Research[END_REF].

Our study therefore aims to empirically investigate the impact of user involvement (in the form of user reviews) on apps performance. Most prior researchers focused on the development of analytical tools for categorization of user reviews (e.g., [START_REF] Maalej | Bug Report, Feature Request, or Simply Praise? On Automatically Classifying App Reviews[END_REF] [START_REF] Maalej | On The Automatic Classification Of App Reviews[END_REF]), seldom questioning the actual benefits of the ideas from the user on apps development, presuming that users could always provide useful feedback. In this study, we categorized and analyzed over 40,000 user reviews associated with about 50 apps to verify the impact on user involvement on apps performance. Specifically, we conceptualized user reviews with innovative suggestions as "customer led innovation" and examined its impact on revenues of apps. We also took into consideration the time taken for apps developers to respond to the user reviews. The value of the innovativeness of user inputs may depreciate over time as other competitors might have already launched similar features onto the market. We hence included developers' responsiveness as a moderator on the relationship between customer led innovation and apps performance. In other words, the effect of user reviews on apps performance would be significantly greater if the developers were more responsive and attend to the user feedback faster.

The remainder of this paper is structured as follows: first, we will explain the conceptual framework and the related past studies. The research methodology and the data analysis procedure will then be presented. Finally, the findings will be discussed and the theoretical and managerial implications will be drawn.

Conceptual Framework

Our research model is presented in Figure 1.

Figure 1. The Research Model

User Involvement

The notion of user involvement was well documented in the literature, referring to the level of personal relevance and importance attached by users to the system [START_REF] Barki | Rethinking The Concept of User Involvement[END_REF]. In broad terms, it is defined as "direct contact with users" [START_REF] Kujala | User Involvement: A Review of the Benefits and Challenges[END_REF]. Recently, it was observed that customers had become more and more involved in the product development [START_REF] Prahalad | The Future of Competition: Co-Creating Unique Value with Customers[END_REF].

User involvement was essential and indispensable for system/ software developers as it helped to collect more accurate user requirements and enable quality improvement, resulting in better fulfillment of user needs and higher user satisfaction [13][16]. User involvement was therefore recognized by previous researchers as beneficial to the improvement of quality and performance [START_REF] Berger | Co-Designing Modes of Cooperation at the Customer Interface: Learning From Exploratory Research[END_REF]. Terms such as co-creation or co-design had emerged to describe the collaboration between developers and users. Other terms included quality function deployment (QFD), useroriented product development, concept testing, Beta testing, consumer idealized design, lead user method and participatory ergonomics [START_REF] Kaulio | Customer, Consumer and User Involvement in Product Development: A Framework and A Review of Selected Methods[END_REF]. In the collaborative process, users may assume the roles of providers of information, commentators or objects for observations. In the context of mobile apps, users and apps developers may exchange ideas on shared platforms such as the App stores. Users could submit their desirable new features or functionalities [START_REF] Khalid | Towards Improving the Quality of Mobile App Reviews[END_REF] [START_REF] Panichella | How Can I Improve My App? Classifying User Reviews for Software Maintenance and Evolution[END_REF]. Complaints from users on lack of certain features could shed light on potential new apps development [START_REF] Barlow | A Complaint Is A Gift: Using Customer Feedback As A Strategic Tool[END_REF]. However, the number of user reviews received could be immeasurable and unmanageable. More importantly, not all feedback is useful. Almost 65% of apps reviews were found to be noisy and irrelevant [START_REF] Chen | AR-miner: Mining Informative Reviews for Developers from Mobile App Marketplace[END_REF]. Some suggestions might be solely emotional and commercially infeasible for implementation.

Many tools were therefore developed to aid the search, screening, and extraction of useful information from user reviews. A review of the current literature showed that different tools were built with different mining objectives. Examples included MARK (Mining and Analyzing Reviews by Keywords) [START_REF] Vu | Mining User Opinions In Mobile App Reviews: A Keyword-Based Approach[END_REF], MARA (Mobile App Review Analyzer) [START_REF] Iacob | Retrieving and analyzing mobile apps feature requests from online reviews[END_REF], ALERTme [START_REF] Guzman | A little bird told me: Mining tweets for requirements and software evolution[END_REF], and AR-Miner (App Review Miner) [START_REF] Chen | AR-miner: Mining Informative Reviews for Developers from Mobile App Marketplace[END_REF]. These tools made use of techniques like natural language processing, topic modeling, clustering and machine learning algorithms to search, classify, extract, group and rank user reviews based on pre-defined keywords or categories.

Customer Led Innovation

User reviews, if carefully and properly screened and processed, could be vital to innovativeness of apps development. For example, a user might point out interesting and novel features that could be added for iPhone users. With many varieties of smartphones available and varied user profiles, it was difficult for apps developers to consider all possible new features. User reviews could be a good source to identify creative solutions. Though some users may be tech-non-savvy, the imaginativeness may never be foreseen in the development process. Their feedback could help developers to visualize innovative features of the apps. We therefore conceptualize user reviews with new feature requests as customer led innovation. It denotes requests from users on new features to be added to the apps or new apps development. Customer led innovation offer insights to developers to add novel features, resulting in greater efficiency of development and higher user satisfaction [START_REF] Kujala | Effective User Involvement in Product Development by Improving the Analysis of User Needs[END_REF]. Accordingly, we hypothesize that:

H1: Customer led innovation has a significant and positive impact on apps performance.

Developer Responsiveness to User Reviews

The time taken by developers to respond to user reviews on apps innovation may matter [START_REF] Vaniea | Tales of Software Updates: Software[END_REF]. After a user submitted his/her feedback, he/she may tend to expect the developer to address the suggestion quickly. If the developer response is slow, other competing apps in the market might have already introduced the new feature and the degree of novelty to users would be diminished. Conversely, users may tend to be more positive about the apps if their ideas were implemented promptly. The new features would also be perceived as more novel with their first time to market. Developers should, however, be cautioned about the frequency of apps dates. Frequent issuance of updates upon novel features may actually cause disruptions to users. In general, reasonable responsiveness to creative solutions should lead to better quality and performance of apps [START_REF] Armerding | Why Users Don't Often Upgrade Software When They Should[END_REF]. The shorter the time taken to respond to user reviews, the greater the effect is the reviews of new features on apps performance. Accordingly, we hypothesize that: H2: Developer responsiveness significantly and negatively moderates the relationship between customer led innovation and apps performance.

3

Research Methodology

Research Context

The data was collected through App Annie, a business intelligence company. It stored data on a collection of health and fitness apps, including the apps user reviews and revenues generated from each app. In our study, only apps that had been active for at least one year were included in the sampling. Active apps should provide more valid results as it was common in the mobile apps industry that numerous apps could have been removed before their official launch. A total of 50 apps were selected for our analysis as their revenue constituted almost 75% of the total revenue in the health and fitness apps market. There were 189,527 user reviews available for these selected apps.

In order to measure the effect of user reviews on apps performance, a specific research time frame was defined. Only reviews posted after the second last updated version and before the latest version of the apps were included in our samples. This enabled us to examine whether the user reviews led to development of new features in the resultant update of the apps. The final sample consisted of a total of 40,619 user reviews, representing 21.4% of the total reviews associated with the selected apps.

Measurement

Customer led innovation

User reviews were used as proxies for innovations suggested by customers. A subtraction and categorization process were conducted to identify the reviews that specifically pertained to innovation suggestions. First, generic reviews were subtracted to isolate the specific reviews [START_REF] Chen | AR-miner: Mining Informative Reviews for Developers from Mobile App Marketplace[END_REF]. Generic reviews were noisy and irrelevant reviews that did not provide any information on creative solutions. Examples of such review were "by far the best app on meditation!" and "I love this app and have done since the moment I started using it. Potentially helped me get through a period of anxiety…". Specific reviews, on the other hand, were those that stated a wish or a new feature request, that is, a specific function that the app developer can add or incorporate in the new version of the apps. A total of 7,654 specific reviews were identified.

Next, the specific reviews were categorized to shortlist the innovation-related reviews. Consistent with previous studies, reviews concerning a new feature request were considered customer led innovation reviews [START_REF] Maalej | Bug Report, Feature Request, or Simply Praise? On Automatically Classifying App Reviews[END_REF] [START_REF] Maalej | On The Automatic Classification Of App Reviews[END_REF].

A feature request refers to the wish or idea proposed by users on a new functionality that should be added but does not exist yet [START_REF] Cheung | A Feature Request Is A Bug[END_REF] [START_REF] Wiggins | The difference between a Bug, Error and Feature[END_REF]. Examples of such review were "Needs to have a value for calories burned for strength training too" and "Missing a compatible version for Apple Watch". Keywords used to screen for bug reviews were "add", "please", "need", "prefer", "request", "suggest", and "wish" [START_REF] Maalej | Bug Report, Feature Request, or Simply Praise? On Automatically Classifying App Reviews[END_REF].

Developer responsiveness

Developer responsiveness was measured by the time interval (number of days) from the first posted date of the user review to the update date when the bug was fixed or the advertisements were removed.

Apps performance

Apps performance could be operationalized in a number of ways such as the number of downloads and apps ratings etc. In this study, apps performance was measured using the revenue generated from the apps during the research time frame. This allowed us to examine the financial impact on the apps developers more directly. Revenues could include purchases of apps, micro-transactions within an app or in-app advertisement (IADV) [START_REF] Ghose | Estimating Demand for Mobile Applications in the New Economy[END_REF]. The revenues for each app was computed by a summation of the daily revenues for the research time frame.

Data Analysis

Regression Analysis was conducted to analyze the correlations in the measurement model. It enabled us to examine the significance and the magnitude of the impact from the independent variable (customer led innovation) on the dependent variable (performance in terms of revenues) [START_REF] Mooi | A Concise Guide to Market Research[END_REF].

As the data for customer led innovation took the form of written user reviews, steps were taken to convert the text data into numerical data. Each review was enumerated with a Python code respectively according to its match with the category of "feature request".

Findings

The results of the regression analysis were presented in table 1 below. The overall model was significant with a p-value of 0.007. The overall R-square was 0.190, which was satisfactory and typical for exploratory research [START_REF] Mooi | A Concise Guide to Market Research[END_REF]. H1 was rejected (0.388), meaning that customer led innovation does not have a significant impact on apps performance in terms of revenues.

The moderating effect of developer responsiveness was supported (.012). It was negatively associated with the link between customer led innovation and apps revenues. With shortened response time, the impact of customer led innovation might increase the revenues further.

Discussion and Conclusion

User involvement has been well documented in the extant literature that the role of users plays an important part in enhancing the quality of software development. This presumption might not hold in the context of mobile apps, where hundreds or thousands of users may easily participate in the apps design through submitting reviews online. The volumes of user reviews might be hardly manageable and the usefulness of the reviews might also be impaired by the users' lack of technical expertise. It was questionable whether the users' creative solutions could lead to actual improvement of the apps performance. Our study therefore attempted to fill this gap in the literature and empirically examined the effect of customer led innovation over apps performance in terms of revenues.

Our findings provided empirical evidence on the value of user reviews on innovativeness of apps development. It was challenging for apps developers to imagine an exhaustive list of user wishes on new features. It was costeffective to draw on user reviews to gain insights into novel features desirable by users. As customers acquired hands on experience with usage of the apps, they were more able to suggestion creative solutions. In other words, co-creation of apps with users should be encouraged to enhance innovativeness and hence the apps performance [START_REF] Gustafsson | Customer Co-Creation in Service Innovation: A Matter of Communication?[END_REF].

We also tested the moderating effect of developer responsiveness on the relationship between customer led innovation and apps performance. Though significant, the effect was not very strong. One plausible explanation is that developers may have to launch apps updates very frequently after addressing each new feature suggested by users. The recurring need to update the apps may be annoying to users [START_REF] Vaniea | Tales of Software Updates: Software[END_REF] and discourage them from the continued usage of the apps. On the other hand, users might also grow impatient if new features took excessive time to be launched. Apps developers should therefore ensure user reviews on innovation were properly addressed within a reasonable time frame.

Table 1 .

 1 Results

		Unstand. Coefficients	Stand.
				Coefficients
		B	Std. Error	Beta	t	Sig
	Model 1 -	7,761.506	8,906.398	.154	.871	.388
	Customer Led Innovation
	Model 2 -	14,198.564	11,458.130	.281	-1.239	.021
	Customer Led Innovation
	Moderator of Customer Led	-37.904	14.565	-.590	2.602	.012
	Innovation


Dr T Praveenkumar

Dr A Manorselvi

Dr K Soundarapandiyan

Exploring the Students Feelings and Emotion towards Online Teaching: Sentimental Analysis Approach

Keywords: Learning analytics, Sentimental analysis, Text mining, Students Emotion and Learning

Data mining is a method to refine raw data to useful information. In education, data mining is a significant research part used to progress the value of education by observing students' performance and understanding their learning patterns. Real-time student feedback would empower faculty and students to comprehend the teaching and learning problems in the most user-friendly way for the students. This paper uses a Lexicon based sentimental analysis technique to analyze students' feelings and emotions through their feedback by correlating learning analytics to grounded theory. The sentiment analysis technique is a computational process to identify and classify subjective information such as positive, negative, and neutral from the source material. It can extract feelings and emotions from a piece of a sentence. Hence this paper aims to recognize the students' positive or negative feelings and distinguished emotions, towards online teaching. The methodology undertakes four processes. The first process is data extraction from the feedback collected from the students through open-ended questions (Text) and is used as source material and imported to R studio. The second process is data cleaning /data preprocessing, removal of annoying data, and separation of data. The third process is sentimental analysis, which divides the data into positive, negative, and neutral categories/groups. This lexicon-based method of sentimental analysis is used to classify the sentiments. The results were estimated using sentiment scores and emotional variance. The sentiment scores result found that students have positive sentiments/emotions towards online teaching and emotions vary concerning the online class timing.

Introduction

Students Feedback is an essential component in the global education system, especially more so for the Indian higher education system. The apex accreditation agencies of the country like NBA, NAAC, etc. have included the students' feedback as one of the measuring criteria for assessing the quality of the teaching-learning process, students' feedback supports progresses in teaching (Ann Poulos et.al 2008). Students may have different issues concerning their class lectures (S. Cummins et.al 2012). Evidence of this is once a student does not know the content of the lecture or an exact case, the Students' feedback can explore what students understand and what they do not understand; what they like and dislike about the class. Moreover, students' feedback can support the faculty to realize the students' learning behavior (Toon Calders et.al 2012). Collecting the feedback is usually taken after a semester or course (Jeff Cain et.al 2007, Toon Calders et.al 2012). Students are requested to express various problems that they may have encountered in the course (Brijesh Kumar Baradwaj et.al 2011). But, students' feedback is highly helpful when it is taken in real-time, as it permits them to report problems promptly (D Rogers et.al 2013). Students' feedback is very significant as it can help the faculty to realize the students learning behavior. Now and then students do not understand what the faculty is tiresome to describe, students can indicate this to the faculty through their feedback. Student's feedback is mostly measured on Likert-scale through a closed-ended question. However, there are some open-ended questions where the responses cannot be analyzed structurally. Therefore, these descriptive answers are analyzed thoroughly. This analysis will become a tedious task when numbers are huge. In a higher education institution in India where the students mostly come from different states, there are differences in languages used when the students expressing their feelings for the semester that recently passed. So To answer these questions, we conducted a literature review, from which three objectives were formulated. The data used for the user is the student's feedback (text form) collected through google forms. Lexicon-based Sentimental analysis was used to obtain the result. R Studio was used in this study to do the analysis. This paper is categorized as follows: first, based on the research question the literature is reviewed and discussed, second, the objectives identified, Then, the study methodology and analysis are described. Finally, results are obtained and discussed.

Related work and Research Gap

Sentimental analysis in education

Ortigosa et al (2014). studied the Facebook data collected from Spanish students, the sentiments in expressions of polarity (positive, negative, and neutral) was detected by using the two methods in sentimental analysis, lexicon approach and combine approach, noticed the polarity in terms of in the perspective of e-learning and pointy out that noticing polarity could be used in adaptive e-learning systems to support personalized learning. Troussas et al (2016). used a machine learning approach like Naive Bayes, Rocchio, and Perceptron classifiers to discovered students' divergence through Facebook in the circumstance of a language learning application. Troussas et al (2020) say that students who are functioning to manage with sentiments may not have appropriate resources accessible to engage in learning. Besides, emotions such as anger, anxiety, and sadness harm students' learning and can disturb students. Further happy students typically perform better. So noticing these feelings can help teachers to take action by concentrating on particular students. Song et al, (2018). also reconnoitered sentiment analysis on online learning systems to discover students 'opinions about courses, lecturers, and online learning systems, this study was conducted using Chinese language data to segregate polarity (i.e. positive and negative) from the sentiments through the SVM classifier machine-learning approach. Tian et al (2017). spotted e-learners' emotions (i.e. Love, Joy, Anger, Frustration) from their texts in a chat system using POS-tags features and C4.5 decision tree as a classifier this study emphasized the benefits of identifying emotions through text, which is a less expensive method compared to others. Munezero et al (2014). discovered students' emotions from their learning diaries using lexicon-based sentimental analysis and identified the eight emotions are joy, sadness, fear, anger, anticipation, surprise, disgust, and trust. Utmost all the research on sentiment analysis in the educational area has been focused on the e-learning area. Though, there are differences between e-learning and classroom-based sentiment analysis given the words used differently in each area. Only one study was related to classroom-based sentiment analysis where the authors applied sentiment analysis to students' diaries Munezero et al ( 2013), Francis F. Balahadia, Ma. Corazon G. Fernando and Irish C. Juanatas (2016) developed a Sentimental analysis system to appraise English and Filipino course teacher performance. They estimated sentiment scores from student's ratings both in terms of qualitative and quantitative using a Naïve Bayes algorithm and vividly represented the percentage of positive and negative sentiments of students to help university administrators be aware of students' concerns.

The literature exposes that there is limited research on sentiment analysis for students' real-time classroom feedback that uses machine learning techniques. But the student's feedback will have varied in the online mode of teaching so based on this the objectives of the study were formulated.

Objectives of the Study

• To recognize the students' positive or negative feelings, distinguished emotions, towards online teaching.

• To measure the sentimental word association • To express the results and generate sentiment visualization.

Data Selection and Extraction

The data used for the study was mined from student's feedback for online courses conducted in selected Top B Schools in Bangalore. The nonprobability sampling method of purposive sampling was used in this study. Purposive sampling technique is an approach where members conform to certain criteria. The main criteria for collecting students' feedback is the student must have attended at least 20 hours of online classes in a particular course handled by the concerned faculty. About 350 written feedbacks were taken for the study to do the sentimental analysis. Feedback from the students collected through Google forms.

Data Analysis and Discussion

The machine learning technique of Lexicon based sentimental analysis was used to do the sentimental analysis. The analysis of this study is separated into three-steps, the first is text mining, the next step is estimating sentiment score and the third step is the classification of student sentiment. R Studio was used in this study to do the analysis. R contains a huge set of Natural Language Processing (NLP) packages. The initial stages contain stacking the feedback (text file) into an R, then data preprocessing was done beforehand executing the sentimental analysis. This analysis demonstrates the following phases and analyses like Data preprocessing, Word Frequency, Word Cloud generation, Word Association, Sentiment Scores, and Emotion Classification

Text mining

Data Reading

The input case for this analysis has "Raw text" of replies (student's feedback), and is a text file. so here we used to read Lines function to read the data. The read Lines function merely abstracts the text from the Text document and consider each line as a character string. To read a subset of lines from the text document the n= argument logic was used

Data Preprocessing

Data preprocessing is starts from cleaning the text data, which means removing distinct characters from the text. By using the tm_map function, the distinct characters like @, /, and | converted into space. The succeeding process is to eliminate the excessive space and convert the full-text data into lower case. Then the stop words are eliminated. The stop words are the most frequently occurring words in any language, example in English "the", "is", "are" have very minute value for NLP and it should remove for further processing. Then SnowballC package was used to do the stemming process.

The stemming procedure shortens the word to its common source. The previous studies related to sentimental analysis in the educational domain were used this all mentioned preprocessing technique (J.M. Martin, A.et al 2013& Erik Tromp, 2014).

Word frequency

After data preprocessing, the following phase is to build the document matrix, this table contains the frequency of words detected form the input text document, this frequency of words used to detect general or trending words in the feedback. By using the TermDocumentMatrix () function from the text mining package, the below Document Matrixa table was built. The resulting table1 shows the word frequency of input text document (students feedback). The maximum frequently occurring word is "good". Also noticed in the table there are no negative words like "not", which shows there are no negative prefixes to change the context or meaning of the word "good" (In short, this shows most replies don't comment negative phrases like "not good"). Pattern, Lecture, student, interact are the subsequent repeatedly occurring words, which direct that utmost the students feel good about their lecture and pattern. Finally, the root "interact" and "lectur" for words like "interaction"

and "lecturer", etc. is also on the chart, and further analysis is needed to identify whether the perspective is positive or negative

Word Association

Word association means to find the relationship between the pairs of words, Correlation is a statistical technique used to find out the magnitude of the relationship between two variables. This tool was used to examine the association between the most recurrently occurring words in the student's feedback and is used to view the student's perspective about these words. The above table2. Shows the words which are most often associated with the selected three terms (The Lower corlimit = 0.25 is set). The output specifies that "teach" (which is the root for the word "teacher "or "teaching") has the highest of 35% association with good lecture and the lowest of 25% association with the student. So the most frequently occurring word ("teach") is positive. Similarly, another important word "Punctual" is extremely associated with the word "interact". These results show that most feedback of students is mentioning the faculty "Punctual and interaction" and can be taken from a positive perspective.

Sentiment Scores

Student sentiment towards online teaching was measured through sentiment scores.

Here the sentiments are categorized as positive, negative, and neutral. And its denoted by a numeric scale. The numeric scale provides better details in identifying the unit of positive or negative strength of the sentiment enclosed in a student's feedback. In this, we used the Syuzhet package for generating sentiment scores, which has four sentiment The Sentiment scores scale for bing is a binary scale with -1 representing negative and +1 representing positive sentiment and afinn is an integer scale ranging from -5 to +5.

From the above table 4. summary results of bing and afinn vectors also show that the Median value of Sentiment scores is above 0 and inferred as the overall average sentiment across all the student's responses (feedback) is positive.

Comparison of Three Vectors

Meanwhile, the study used three different methods and three diverse scales, it's well to change their output to a common scale before relating them. By using sign function in R, the basic scales were converted, the sign function converts all positive numbers to 1 and all negative numbers to -1, and all zeros remain 0.

Table 5. Normalize scale and compare three vectors

1 2 3 4 5 6 1 1 1 1 1 1 1 2 1 1 1 1 1 1 3 0 1 1 1 1 1
Form the above table 5. it's Noted the first element of the first two rows (vector) is 1, indicating that all three methods have calculated a positive sentiment score, for the first response (line) in the text.

Emotion Classification

This study used the NRC Word-Emotion Association Lexicon to classify the emotion of students towards online teaching. The NRC Emotion Lexicon is a list of English words and their associations with eight basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) and two sentiments (negative and positive).

get_nrc_sentiments function was used in this study. This function, which returns a data frame with each row representing a sentence from the original text.

0 0 0 0 0 0 0 0 0 0 2 0 1 0 0 0 0 0 2 0 3 3 0 1 0 0 1 0 1 1 0 1 4 0 1 0 0 1 0 0 2 0 2 0 1 0 0 1 0 1 1 0 1 6 0 1 0 0 1 0 0 1 0 2
From the above table 6, it inferred that Zero existences of words related to emotions of disgust, anger, sadness. the lengthiest bar and displays that words linked with this positive emotion found just over 25% of all the expressed words expressed by the students in their feedback. The next emotion is "disgust" has the shortest bar and displays that words linked with this negative emotion found less than 1% of all the expressed words by the students in their feedback. Overall, words linked with the positive emotions of "trust" and "anticipation" account for almost 50% of the significant words in the text, and it can be interpreted as a good sign of emotion towards the online teaching.

After applying machine learning algorithms and estimate sentiment score and classified the emotion of students towards online teaching., the teaching activities to be examined were identified. Potential sentimental words or related words to the online teaching identified were: good, pattern, lecture, student, interact, paper, teacher, exam, the universe, check. Detecting these words, the objective is to explore which words impact students' sentiment scores the most, and which ones provide insight into student emotion towards online teaching. Good is a vastly said word, occurring in many student responses, in positive sentiment. This could mean that online teaching is good concerning the particular faculty and hour in emotional classification Trust is the highly mentioned word, this could mean the students trusted the content and faculty. Over some time, the emotion falls under the positive category also.

Conclusion

This study identifies and recognizes the students' positive and negative feelings, distinguished emotions, towards online teaching. and also measured the sentimental word association and Emotional variance over online teaching. Using a lexicon sentimental analysis method and affin, bing, and syuzhet vector concept the sentiment was classified into positive and negative. The results showed from all techniques the sentiment falls under the positive side. It necessity be noted here that student feedback data are only one source of data in general towards online teaching, but syllabus/module content and delivery, and should be used in combining with other teaching and learning evaluation strategies for further study.

  structural analysis is needed for open-ended answers, by adopting a sentiment analysis technique this study to detect the students' positive or negative feelings and distinguished emotions, texted feedback. The question posed in the present study is: How the student's real-time feedback helps in identifying the student's sentiments and emotion towards online teaching? we ask two research questions RQ1. Would student's sentiments and emotions vary in online teaching? RQ2. Would students' sentiments and emotions indicate their satisfaction and engaged level in online teaching?

  and fear. 1 incidence each of the words linked with emotions of anticipation, joy, and surprise. And 2 incidences of words linked with emotions of trust. No incidences of words are linked with negative emotions. Maximum 3,2,1 existences of words in positive emotions. The subsequent phase is to generate charts to help visually examine the emotions of students. The below chart shows the entire amount of occurrences of words in the text, related to each of the eight emotions.

Fig. 3 .

 3 Fig.3. Emotions in Text -Bar plot The above fig 3, bar plot permits a quick and easy assessment of the proportion of words linked with each emotion of students towards online teaching. The emotion "trust" has

Table 1 .

 1 Word

	Frequency

Table 2 .

 2 Word Association

	Words	goodlectur	lectur	student	style	understand
	teach	0.35	0.3	0.25	0.29	0.28

Table 4 .

 4 Summary Results of Bing and Affin Vector

	Words	interact	teacher	lectur	delivery	Knowledge	proper	staff
	punctual	0.54	0.52	0.48	0.44	0.3	0.25	0.25

From the above table 3., Summary results of the Syuzhet vector show the first component has a value of 2.35. It means the sum of the sentiment scores of all important words in the first response(line) in the text file, adds up to 2.35. The scale used for measuring sentiment scores ranges from -1(indicating most negative) to +1(indicating most positive). The summary statistics table showed that the median value is 0.7500 and the mean value is 0.8665, which is above zero and inferred as the overall average sentiment across all the responses (student's feedback) is positive. It means students' sentiment towards online teaching is positive.

Table 6 .

 6 Data frame S.No Anger Anticipation Disgust Fear Joy Sadness Surprise Trust Negative Positive 1

	The data frame has

• Yoo, S., Song, J., & Jeong, O. Social media contents based sentiment analysis and prediction system. Expert Systems with Applications, 105, 102-111,2018


Rajesh G Pillai

Poonam Devrakhyani
email: p.devrakhyani@tbs-ducation.org

A data driven approach for Customer Relationship Management for Airlines with Internet of Things & Artificial Intelligence

Keywords: Smart Customer Relationship Management, Smart Airport, Internet of Things, Artificial Intelligence

Customer Relationship Management is a critical aspect for all service industries and extremely important in aviation industry. With the changing aviation scenario, the travel industry is facing more challenges. Identifying and retaining the profitable customers is very essential for survivability. Customers are well informed about the services that offer them the maximum value proposition and retaining high value customers is very challenging. Airlines currently use many techniques for CRM, but there are drawbacks in the system which can be complimented with emerging technologies. Artificial intelligence & Internet of Things are evolving domains, which have gained lot of importance during the last decade, predominantly due the capacity of systems to gather, store, process & transfer huge amount of data. This paper is indented to improve CRM with the prudent use of AI & IOT and involve airports in implementing smart CRM, ensuring long term profitability and sustained revenues.

Introduction

Customer relationship management (CRM) is a critical factor across all the service industries. CRM is defined as the approach of managing an organization's interaction with its current and potential customers based on customer data, collected from customer's interactions with the organization. The customer data is analyzed using specialized tools to improve relationships with customers, focusing on customer retention and generating additional revenues. The data required for CRM comes from different communication channels, like official website of firm, call centers, emails, combined loyalty programs with other organizations etc. Recently many organizations have started data mining and use of social media to gather customer information, so that better service can be offered to the customer. Whatever be the CRM implemented by the organization, data and accurate analysis of data is very critical for successful CRM implementation.

Airlines are operating on very narrow margins due to competition and global economic crisis due to pandemic. Many airlines are trying to reduce the operating costs by improvements in operation, optimization of fleet and routes and reduction in manpower. However, customer cannot be ignored in the process of regaining operational efficiency. The core service for all airlines is providing basic transportation. All other services like choice of in-flight entertainment, onboard meals, quality of cabin crew, special assistance etc are supplementary services aiding the core service. Supplementary services help in product differentiation of the core product and create a competitive advantage by facilitating the use of core service and enhancing the value of the core service. To retain customers and improve customer retention, emerging technologies can be used, which can assist airlines in enhancing both the core and supplementary services. An airline's effective CRM program can be a significant factor for sustainable revenues, product differentiation, achieving operational efficiency and long term growth of the organization.

Research towards new paradigm shift for customer relationship management in airlines was carried out by IBM institute of Business Value [START_REF] Boland | The future of airline CRM[END_REF].The use of data and data mining techniques for customer relationship management [START_REF]Application of data mining techniques in customer relationship management: A literature review and classification[END_REF] has been researched. Studies on behavior of airline customers based on their perception of choice and importance of those factors to develop the appropriate marketing strategies were also carried out [START_REF] James | A novel decision rules approach for customer relationship management of the airline market[END_REF]. CRM studies at regional level for different airlines at was also done by many researchers [4 5 6]. Many airlines uses social media for data collection and improvements in CRM [7 8 9 10].However, CRM methods are on the verge of radical changes with emerging technologies [11 12 13 14]. This paper tries to propose a smart data driven method for improvement of CRM with prudent use of Artificial Intelligence and Internet of Things and make an airport a key stakeholder of CRM activities.

Research Methodology

The research started with interactions with some customers who travel frequently on international airlines, and it was observed that frequent travelers mind each small factor in service of airlines and are not hesitant to switch their loyalty. The existing CRM systems followed by airlines were analyzed and to understand how major airlines try to retain the customers and what are the drawbacks in these systems. Interviews and surveys were carried out among customers to understand the major factors that causes passenger to switch the airlines. The scope of emerging technologies like AI and IoT was further investigated to improve the CRM. The research methodology is briefly described in Figure 1.

Importance of CRM for Airlines

Customers are profitable over a period of time and studies shows that profit can increase by 35 to 85 percentage by retaining just 5 percentage of customers [START_REF] Jagdish | Customer Relationship Management, A strategic Perspective[END_REF]. Getting a customer and retaining him as a loyal customer in long term is an extremely challenging, but highly rewarding activity for airlines. Identifying a potentially profitable customer is a tricky task as customer has lot of options for travel. Airlines use different ways to collect customer information. Most of the airlines use loyalty programs individually or as a group of airlines to get customer data and use them for customer relationship management activities. Promotional offers, discounted rates, upgrade of travel class and accessibility to premium facilities are provided to customers based on this data to retain the customer. But there are few drawbacks in the existing method of implementation of CRM based on loyalty programs.

Table 1 shows the behavior of a passenger X, who made 4 international journeys from Bangalore International Airport Limited (BIAL) in a year to different destinations using different airlines all the times. The passenger has also made multiple journeys inside the country which has not been considered for the analysis. A few points can be clearly understood from the travel pattern.

• A passenger may use different channels for airline ticket booking. It can be the airline official website or mobile app or third-party travel portals or agents, depending on the convenience and cost. So the data obtained from the official sites alone is not sufficient to understand the travel pattern of a customer.

• Travel history at Si No.1 shows after travelling, the customer has decided not to travel by the airline anymore. The data indicates that the passenger had used the official app for booking ticket on the airline (Airline A). The booking was done for a higher fare compared to travel portals, but it was done due to convenient procedures for visa approval. But the baggage experience with the airline was very poor, which made the customer decide that he will not prefer this airline anymore. The customer has done lot of travel after that, but never preferred this airline again. Airline keeps sending mails and promotions in the passenger's mail, without any response. In this case, the airline A is not aware of losing the customer and why the customer switched.

• Travel history at Si No.2 shows booking on third party portal as the inward and outward journey was done on different airlines and customer preferred paying in his local currency. Onward journey shows passenger was extremely happy with Airline B, because of the special attention provided to the kid, quality of food and the choice of in-flight entertainment. He may prefer this airline on future journeys. But as the booking was done on third party site and not linked to any official site, the airline is unaware of a happy and satisfied customer. • The return journey of this trip is on a different airline (Airline C). The customer linked his booking to the official site for choice of seat and registered to the loyalty program. But as the customer was not satisfied by the cabin crew service offered by the airline, particularly the delay in serving meals to the kids travelling along with, this airline was never preferred again. The customer was also unhappy with the seating comfort, and the lack of in-flight entertainment provided on the connecting flight. The passenger has made journeys after that, but never preferred this airline. This airline also is unaware that it has lost this customer, but still tries to attract the customer with constant communication, as he has registered in loyalty program.

• Travel history at Si No 3, indicates travel to the same destination, but on a different airline (Airline D). Previous journey also, the customer wanted to book on the same airline, which was the fastest mode of travel on that route. But it could not be done because the journey was planned in a short time and the cost offered by the particular airline was very high compared to fare offered by connection flights. When the customer got sufficient time to plan his next journey, he booked it on the Airline D. The customer was happy with the timings and food, but slightly unhappy with the in-flight entertainment and seating & cabin comfort provided by the airline as this was an old aircraft. But the customer has travelled on business class compared to his previous journeys in economy class.

• Travel history at Si No 4 indicates, the customer has developed his travel choices and is ready to spend more money for travel and his comfort. Again the passenger prefers to fly on business class. All the airlines mentioned in the case have their own loyalty programs and methods of data collection which act as the base for CRM implementation. The customer here can be considered as a profitable customer based on the travel pattern and willingness to spend. Airline A & C, who still try to attract and retain the customer are clearly losers in this context. Airlines B, D & E haven't lost the customer, but even these airlines have to be careful with CRM to retain this customer and offer the best services.

No CRM methods currently used by airlines can predict this type of customer behavior. The customer mentioned in the case has the potential to become a profitable customer, but drawbacks in the system fail to identify him as s profitable customer due to deficiency in data collection and identifying the reasons for the passenger to switch between airlines. There can be improvements to CRM with use of emerging technologies like Artificial Intelligence & Internet of things, which can be a game changer for airlines in long term.

Improved Smart Data Driven CRM for Airlines

The airline market is oligopolic with many airlines providing more or less similar services to the passenger, which make the competition very high and choice abundant for a passenger. An airline with an efficient consumer-centric approach to relationship management will be better positioned to acquire, develop and retain high value customers. An airline's CRM program is the key for product differentiation, achieving operational efficiency and long term business development. To achieve this, the airline has to clearly understand what prompts a customer to switch the airline and who has to be treated as a high value customer. Decision by a passenger on which airline to travel depends on a lot of factors like cost, convenience & other amenities. The initial choice of an airline by a passenger can be mostly advices from travel agents or friends or based on internet research. Once the passenger uses the airline, he gets a real feel of the services offered by the particular airline. After a journey, a passenger may be fully or partially satisfied by the services offered by the airline or may not be satisfied by the services. Once the passenger is not satisfied, he may decide not to travel by the airline any more or prefer it as the last option. This paper focuses on two major factors that require attention in CRM and how CRM can be improved by use of emerging technologies ensuring long term revenues.

i.

Identification of the factors which results in dissatisfaction to the customer and loss of business and how these can be improved with emerging technology concepts like Internet of Things (IOT) and Artificial Intelligence (AI).

ii. Identification of the high value passengers based on travel history by using advanced technologies at the airport. An airport has to become smart airport using technologies like face recognition and become a centre for data collection.

Factors leading to customer dissatisfaction & loss of business

There are many factors which make a passenger feel dissatisfied by an airline. Identification of factors leading to passenger rejecting an airline was arrived based on surveys and interactions with passengers. For many passengers, cost of travel was the major factor deciding choice of airline. But there are lots of passengers who are willing to spend little more for comfort and convenience. Airlines have to focus on such passengers for sustained revenues, while not neglecting the cost conscious passengers. Figure 2 shows revenues from a small percentage of high value customers are much higher than the revenues from other passengers.

Choice of Food

Food option on board is another complex factor that needs attention of airlines. The choice of food also varies from passenger to passenger and depends on many factors. Many airlines cater different options of food, but majority of passengers do not provide the choice of food to airlines prior travel. This sometimes leads to situation in which some dishes are not sufficient and some of dishes are wasted. Special Attention to Kids An airline that does not provide special attention to kids has all chances of losing a family passenger. There are many parents who consider the care provided to kids as a very important factor for air travel.

Comfort of seats

Comfort of a seat is very important in long haul flights. Seats lose its comfort as the aircraft ages and can result in discomfort to the passenger and rejection of airline by passenger as the passenger may prefer a new comfortable aircraft.

Type of aircraft/age of aircraft

There are many passengers who are technically knowledgeable and can select the aircraft based on the seating layout, cabin pressurization, humidity etc. Offering an uncomfortable or old aircraft to a technically educated customer may end up in losing the customer.

Reliability & Dependability of the airlines

Issues like delays in journey, trip cancellations also lead to customer dissatisfaction. In many cases, passenger loose connection flights due to delay in one leg of the journey.

Special care for old aged/ sick and disabled passengers

There are passengers who are old, disabled and suffering from medical conditions who require special assistance for travel. The requirement of these passengers includes wheel chairs and personal attention till the journey is completed. The airlines have to provide preferential treatment to these passengers in coordination with airports Safety With emergence of pandemic, safety has become a major concern for most of the passengers. Passengers require clean and sanitized cabin for safer travel, which has become a standard norm for travel.

Cleaning and sanitizing the cabins as per standard safety norms adds additional costs to airlines, increase the turnaround time and reduce the operational efficiency of the airlines.

Use of Emerging Technologies for enhancing Customer Experience

Many of the existing implementation of CRM fails to get the accurate data for building an efficient CRM. Data and analysis of data is the major building block for any CRM implementation. Some of the attributes that cause passenger switching are intangible. The technologies like Artificial intelligence & Internet of things has gained lot of prominence in the current decade due to their ability to gather and process huge amount of data. With its data handling and data communication capabilities, Artificial Intelligence (AI) and Internet of Things (IOT) are the ideal candidates for improvement in areas where passengers look for satisfaction. Table 3 shows the scope of AI & IOT in retaining the customer for airline along with the suggested improvements in each of the key areas. RFID tagging can also provide information to the airlines to identify and off-load the specific bags in case the passenger is denied boarding or offloaded before flight. In many cases, the delay in off loading bags has resulted in delay to the flight and losses to airlines. This can improve the operating efficiency of the airline. The cost of implementation for airlines is around US$ 0.1 per passenger, with cost savings of US$ 0.2 per passenger. The change of technology is beneficial for the airports too with improvement in parameters like time for arrival of bags, number of bags handled and decrease in baggage mishandling rate.

In flight Entertain ment IOT IOT concepts can be very useful in providing appropriate in-flight entertainment to the passengers. Personal choice of the passenger like films, serials etc can be found from previous history of the passenger. The in-flight view history of a passenger can be collected by small storage devices in IFE which can be mapped to the particular passenger. There can be a central storage of movies and entertainment in the flight, which the cabin crew can load to the IFE of the passenger with connected IFEs.

Choice of food AI

Choice of food is highly dependent on person to person, but there can be clues about it food choice from personal information like age, gender and nationality. The typical food usage pattern in a route, the quantity of food wasted etc can be used as inputs to AI. Artificial intelligence algorithms can be used to predict the food options to be provided based on the details of the travelers, which can reduce the quantity of food wasted Factor Enabling Technology Scope of Emerging Technology and provide each customer the food of his choice. This will allow airlines to reduce the catering expenditure and keep the flyers happy. Special Attention to Kids

IOT

Information about kids travelling has to be handled carefully. The IFE contents in the case have to be interactive games and contents that kids like to watch. This can be done by the connected IFE concept & IOT.

Comfort of seats

IOT Sensors can be fitted on aircraft seats to gather data about the performance of the seats. It can identify occupancy status of seats, the number of times the seat is reclined, the number of times tray table is used and the seatbelt status. An uncomfortable passenger will be moving in the seat and cabin crew can provide special attention to the passenger based on the data from the sensors. The airline can also identify that a seat has lost its comfort and refurbish it based on the sensor data [START_REF] Renata | An approach to aircraft seat comfort using interface pressure mapping[END_REF]. The cabin crew also will have reduced workload as they do not have to instruct and inspect each passenger about seatbelts.

Type of aircraft

AI

AI technologies can be used to find out the type of aircraft that has to be used on the route. Profile of the passengers can be analyzed using AI technologies. A route like Trivandrum-Dubai consists of majority on middle class working people and cost is the most important criteria for these passengers. Providing a slightly older aircraft on the route will not affect the revenues. The passengers on a route like Bangalore-Dubai mostly consist of mostly software professionals who are careful about the aircraft amenities. Providing an old aircraft on the route can result in passenger switching the airline. Passenger profiling can be regularly done using AI technologies for optimal use of the aircrafts in the fleet. Reliabilit y & Dependa bility of the airlines AI Unscheduled maintenance is a major concern for all airlines and these results in delays and trip cancellations.

The unscheduled maintenance can be avoided with the use of predictive maintenance. This can be implemented using AI techniques and can predict the failure of a component before actual failure. This can reduce unscheduled maintenance and delays to a large extend and improve the reliability of the airlines. Special care for old aged/ sick and disabled passenger s

AI & IOT

AI & IOT can play an important role in providing comfortable travel to these classes of passengers. Motorized cars using AI & IOT which can carry two passengers can be used in airport for seamless travel. These vehicles can be parked at designated parking lots at the airports and the passengers can scan their tickets to get boarding pass on the dedicated facility built in on the vehicle without waiting at the normal check in counters. AI techniques can be used to automate the vehicles and move them through the airport with minimum human assistance. Safety Measures

AI

The cleaning and sanitation of the cabin is currently a human intensive process. AI powered ultra violet technology based cleaning robots can be used by airlines Factor Enabling Technology Scope of Emerging Technology for faster and efficient cleaning of the aircraft cabins. This would also reduce the turnaround time for the aircraft and improve the operational efficiency. Additionally AI based thermal body scanners can be used by airlines to check the health status of the passengers before boarding the flight. Machine learning algorithms can be used to predict the symptoms of covid, based on the images from the thermal scanners.

With introduction of emerging technologies in data collection and processing, the factors that cause dissatisfaction to the customer can be reduced to a large extent. This can result in better customer pleasure and better customer retention for sustained revenues. Data will be continuously available for the airlines, which can continuously improve its processes. The privacy issues related to data can be a constraint in some of these proposed improvements. The terms and conditions of data collection and sharing of passenger data and its usage for various activities have to be evolved and this can be included in the terms and conditions of travel. Some of these initiatives like baggage handling require co ordination with the airport to attain full efficiency of the system. Some initiatives like in-flight entertainment, choice of food are completely under the control of the airline. Each of these initiatives have efforts required for implementation which has to take care of factors like cost of implementation, technology readiness level of the systems, co ordination with stakeholders like airport, regulatory approvals for use of technology and constraints on the operations. The impact on CRM from of each of initiatives also will be different based on factors like revenues generated, customer satisfaction, efficiency in operations and customer retention. Based on these factors the relative efforts for implementing each of these technologies and their impact on CRM were evaluated. The relative impact on CRM and the effort for implementation for these are provided in Figure 4.

Identification of high value customers

Another area in CRM which requires attention is identification of a high value customer. A person who does multiple journeys in a year can be treated as a high value customer. The more number of journeys the person does, the customer becomes more valued and the airlines have to ensure the customer is retained. But as suggested by travel history mentioned in para 2, it is difficult to understand the number of journeys a person does, in case the journeys are performed by multiple airlines and in case the booking is done through different booking channels. An airport enabled with smart technologies can act as a data collection point which can be the baseline for CRM implementation.

An airport traditionally acts the arrival and departure point for the passengers for their air travel. But, an airport can play an important role in analyzing the customer behavior as it is the gateway of a passenger to the system. A passenger will mostly begin the journeys from airport of his home town. Whatever mode a ticket is booked or whichever airline the passenger select, the passenger ultimately travels through the airport and passes through the mandatory check points in the airport. To clearly identify how many times a person travels by air, the best available option is the airport of his home town. Face recognition techniques can be implemented at the airports at security check points. A face recognition system at airport can be useful in identifying a frequent flyer and generating lot of data for CRM activities. The images of the passengers taken from the airport security can be a reliable source of how many times a person travels. This can serve two purposes:

• Quick identification of passengers using the face recognition techniques and reduce the time in travel procedures like verification of documents. This will improve the efficiency of the airport operations.

• Create a database for travel history of passengers, which can be used for identifying the high value passengers or the frequent travelers. The second point can be the input for many of the CRM activities for airlines which enable them to decide which the passengers to be retained.

Benefits of Data Driven Smart CRM

A new smart CRM implementation plan powered by data has been envisaged based on the studies carried out, which can overcome many of the drawbacks in the existing CRM systems. Implementation of the new CRM scheme benefits airlines, airports and passengers. The specific benefits for each of the stakeholders involved are provided in Table 4. The implementation of features for disabled passengers can improve the goodwill factor of the airports along with airlines. Safety features like AI based thermal scanners installed at airports will increase the confidence of many passengers, who are afraid that they are sharing an aircraft with an infected passenger.

3.

Passenger Reduction in time spent for activities like check-in procedures and security checks will be beneficial for the passenger. A personalized experience for the passenger in flight leads to customer satisfaction and customer delight. It can prompt passenger to spent little more money on air travel.

Conclusion

The aviation industry in the world has seen unforeseen turbulences in the recent times. Many low cost airlines have stopped operations and full service carriers are struggling with huge losses. Retaining the profitable customer is of critical importance in aviation under the current circumstances. Investments have to be made on data and data driven technologies for sustained revenues in the future. Safety of travel is a major concern for all passengers and use of technology to ensure safer cabins is a requirement for all airlines. Full service carriers have to invest in AI & IOT to enhance the customer experience and product differentiation to retain the customers. Investment in AI and IoT is required even for low cost carriers to reduce the losses due to delays and improve the operational efficiency. The new smart CRM implementation is a win-win situation for both airlines and airports.

Figure 1 :

 1 Figure 1: Research Methodology

Figure 2 :

 2 Figure 2: Airline Customer Value (IBM Analysis)The core service for airlines is to provide basic transportation. Other services like in-flight entertainment, onboard meals, quality of cabin crew, special assistance etc are supplementary services aiding the core service. A flower of service model, shown in Figure3was prepared based surveys and interaction with passengers.

Figure 3 :

 3 Figure 3: Flower of Services for AirlinesThe importance of these factors and why these supplementary factors can cause passengers to switch airlines are provided in Table2.

Figure 4 :

 4 Figure 4: Impact to CRM& effort in Implementation

Figure 5 :

 5 Figure 5: Face Recognition at Airport as a data source for CRM

Table 1 :

 1 Typical Passenger Travel Pattern

	Si	Origin	Destination Airline Mode of	Feedback
	No				booking
	1	Bangalore City A	A	Official	Very	disappointed	with
					website	baggage handling of the
		City A	Bangalore	A		airline and decides not to fly
						with this airline anymore.
	2	Bangalore City B	B	Travel	Prefer Airline B, though it's a
					Portal	connection flight and happy
						with the experience. Not
						booking return on this airline
						as it's a long connection.
						Booking was not linked with
						official site as travel portal
						offered free seat selection.
		City B	Bangalore	C	Travel	Very unhappy with Airline C,
					Portal	due to poor care provided to
						kid and lack of inflight
						entertainment (IFE) on the
						connection flight. Booking
						was linked with official
						portal for seat selection.
	3	Bangalore City B	D	Official	Prefer Airline D as it is the
					website	fastest flight on route.
		City B	Bangalore	D	Official	Compromises	on	IFE
					website	contents of the European
						carrier, but travels by
						business class.
	4	Bangalore City C	E	Official	Prefer Airline E as it's the
					website	fastest direct flight and
		City C	Bangalore	E	Official	travels by business class
					website

Table 2 :

 2 Key reasons for customer switching in Airlines lot of time in aircraft requires entertainment. The choice of in-flight entertainment varies from passenger to passenger and it is dependent on the country of origin, age, personal preferences. Providing good in flight entertainment is an important supplementary factor in passenger retention.

	Factor	Remark
	Baggage	Baggage of a passenger is important and requires very careful
	Handling	handling. Loss of baggage, delay in baggage, damages to baggage
		and theft of items from baggage are few things which create a poor

opinion about an airline in customer's mind and can result a customer avoiding an airline. The number of mishandled bags in 2018 was 24.8million according to SITA.[16] Out of 24.8million bags mishandled, 18% are damaged and pilfered bags and 5 % are stolen. Transfer mishandling is a major reason for delayed bags.

Table 3 :

 3 Brief Scope of AI & IOT for CRM

	Factor	Enabling	Scope of Emerging Technology
		Technology
	Baggage	IOT	Baggage handling is concern for most of the passengers
	Handling		and most airlines use simple barcodes for baggage
			handling. Barcodes are cheap, simple and proven
			technology but require line-of-sight reading by barcode
			scanners. To reduce the number of lost or delayed
			baggage, airlines have to comply with IATA Resolution
			753, which requires change of technology.
			Radio Frequency Identification (RFID) tags is an
			application evolved from the internet of things concept.
			RFIDs are small electronic chips that can store &
			transmit the data about the baggage. Handling of
			baggage can be easily done with low cost RFID tags
			[17]. The tags can provide real time information of the
			baggage and it can be easily tracked This information
			can be provided to the smart mobiles carried by
			passengers as desired by majority of passengers
			surveyed. Switching to conventional baggage tagging to
			RFID baggage tagging can result in customer
			satisfaction and retention.

Table 4 :

 4 Beneficiaries of CRM Data from the airport can assist airlines in clearly identifying the frequent travelers and IOT & AI can assist in identifying what are the areas of concern for the passengers and offer improvement in these areas. Improvement in Customer Relationship Management can result in increase of revenues by customer retention. The proper use of CRM tools can ensure that the resources are planned and utilized in the optimum way. Providing a more personalized travel experience would make many customers spend little more on air travel which can be very critical in sustainability of the airlines. 2. Airport Implementation of face recognition techniques in airports offers dual benefits to airports. The techniques can assist in contactless and fast movement of passengers during the check-in & departure process. This can improve the number of passengers travelling through the airport and increase in revenues. Additional revenues can be obtained from providing data to the airlines for their Customer Relationship Management activities. The technologies like RFIDs in baggage handling can improve the efficiency of the airports.

	Si No	Beneficiary	Benefits
	1.	Airline	Existing CRM has drawbacks cannot capture the entire
			passenger behavior.


Praveen R S Gummadidala

Nanda Kumar Karippur
email: kumar.karippurnanda@spjain.org

Maddulety Koilakuntla
email: k.maddulety@spjain.org

S P Jain

Analysis of Factors Influencing the Adoption of Artificial Intelligence for Crime Management

Keywords: Artificial Intelligence, Crime management, AI Adoption

Despite the benefits of Artificial Intelligence (AI) and its potential to produce deep insights and predictions, its adoption and usage are still limited in the area of crime management. Over the years, crime rates have been increasing in India, and law enforcement agencies face enormous challenges given the increasing population, urbanization, limited resources, and ineffective conventional models of reactive and investigative policing. There is an unprecedented opportunity for AI to be leveraged together with new policing models such as intelligence-led policing and predictive policing for effective crime management. In this research-in-progress paper, we offer a deeper understanding of factors significant for the adoption intention of AI for crime management in India. Further, on the practical front, the study will help law enforcement agencies to effectively leverage AI and implement innovative policing models for crime management.

Introduction

Law enforcement agencies (LEA) in India are facing tremendous challenges in crime management due to several factors such as increasing crime rates, low police per person ratio, increasing population, and ineffective models of policing. The nature and methods of committing crimes are also changing rapidly, posing serious challenges to law enforcement agencies and other stakeholders of the criminal justice system in India. The models of reactive, investigative policing are not enough to contain the increase in crimes. There is a compelling opportunity for implementing new policing models such as Intelligence-led policing (ILP) and Predictive policing to be more effective with limited resources. For example, there are vast volumes of data that are being collected internally that could be used for crime analysis and to influence police decision making. Predictive policing aims to rely on computer algorithms to see patterns, predict the occurrence of future events based on large quantities of data and aims to carefully target police presence to the necessary minimum to achieve desired results [START_REF] Sanders | Policing, crime and 'big data'; towards a critique of the moral economy of stochastic governance[END_REF]. The main objective of Intelligence-led policing is to apply crime data analysis to objectively inform policy, policing strategies, and tactical operations to reduce and prevent crime emphasizing the proactive use of police resources [START_REF] Ratcliffe | Intelligence-led policing[END_REF]. However, such approaches are not easy to implement using manual processes and existing infrastructure.

There is growing interest in using digital technologies to address the challenges faced by law enforcement agencies in India. The use of data to detect crimes has long been a central feature in the security policies' decision-making process [START_REF] Peeters | Machine justice: Governing security through the bureaucracy of algorithms[END_REF]. The fast availability of data has brought AI closer to commercial use [START_REF] Kruse | Artificial Intelligence for Financial Services Industry: What challenges Organizations to Succeed[END_REF]). AI integrated with other technologies such as Machine learning, Big data, Geospatial technologies could bring out innovativeness to the approach of solving problems in crime management and contribute to a safer environment. While there is growing interest in exploiting the potential of AI, several challenges in adoption have been reported, such as unclear relative advantage, issues in technology readiness, disconnect between strategies and automation, and regulatory support.

To the best of our knowledge, there is no empirical study done to understand the factors critical for the adoption intention of AI for crime management. To fill this research gap, we intend to use Technology-Organization-Environment (TOE) framework [START_REF] Tornatzky | Processes of Technological Innovation[END_REF] as the comprehensive theoretical lens along with other relevant theories such as Diffusion of Innovation [START_REF] Rogers | The diffusion of innovation, 5th edn[END_REF], Routine Activity Theory (Fernando, 2014) and Near repeat phenomenon (Bowers & Johnson, 2005). We seek to explore the key factors that influence the adoption intention of AI in crime management. Our primary research question is:

RQ: What are the key factors that influence AI adoption intention by Law enforcement agencies for effective Crime management in India.

2

Theoretical Background

Adoption Theories

TOE framework has emerged as a valuable theoretical lens for understanding technology adoption. Diffusion of Innovation theory can explain intra-firm innovation diffusion [START_REF] Hsu | Determinants of e-business use in US firms[END_REF]. TOE is also not limited by industry and company size (Wen and Chen, 2010). TOE has been applied successfully as key contextual elements that determine new IT adoption at the Organization level [START_REF] Baker | The technology-organization-environment framework[END_REF]. Moreover, the TOE framework has been used for the adoption of technologies such as Customer-based IOS, Open Systems, EDI, RFID, Enterprise systems, and E-Business [START_REF] Chandra | Exploring factors influencing Organizational Adoption of Augmented Reality in E-Commerce: Empirical Analysis using Technology-Organization-Environment Model[END_REF]. We apply the TOE framework for understanding the facilitators of AI adoption in Crime management. On the other hand, Diffusion of Innovation theory (DOI) seeks to explain 'how, why, and at what rate new ideas and technology spread' (Rogers, 1995). Both of these theories are applied to understand the adoption intention of AI for Law enforcement agencies in India.

Crime management Theories

The potential to predict crime follows from the empirical observation that most crimes do not happen randomly, and often tend to be concentrated in time and space in socalled crime hotspots. Routine activity theory is, in short, an attempt to identify, at the macro-level, criminal activities and their patterns through explanation of changes in crime rate trends [START_REF] Miró-Llinares | Routine Activity Theory[END_REF]. Routine activity theory argues that three fundamental "almost-always" elements define criminal acts: a likely offender, a suitable target, and the absence of a capable guardian (Felson, 2002). So, to prevent crimereduce the suitability of the victim, demotivate offenders, and improve guardianship. The nearrepeat phenomenon states that if a location is the target of a crime such as a burglary, the homes within a relatively short distance have an increased chance of being burgled for a limited number of weeks (Jerry H. Ratcliffe and George F. [START_REF] Jerry | Near-Repeat Patterns in Philadelphia Shootings[END_REF]. This pattern of space-time clustering has been referred to as the 'near repeat' phenomenon to reflect the association with repeat victimization. Policing strategies and models such as predictive policing and intelligence-led policing are based on these theories and are factored in building the research framework discussed.

Research framework and Hypotheses

In our study, the framework was operationalized using TOE framework, as depicted in Fig. 1 below. The rationale for the selection of the factors under each context for AI adoption is specified below. amount of quality data for training the algorithms and for better predictability that needs to be deployed across the law enforcement agencies. Hence, we hypothesize, H1: Technology readiness is positively impacted by LEA's intention to adopt AI for crime management

Compatibility

Compatibility is the degree to which an innovation is perceived as consistent with the existing values, past experiences, and needs of potential adopters [START_REF] Rogers | The diffusion of innovation, 5th edn[END_REF]. Compatibility is an important determinant of innovation adoption [START_REF] Oliveira | Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors[END_REF]). In the Indian crime management context, compatibility of AI applications with data sources, IT Infrastructure, and systems such as Crime and Criminal Tracking Network System (CCTNS) and National Intelligence Grid (NATGRID) is essential. Hence, we hypothesize, H2: Compatibility is positively impacted by LEA's intention to adopt AI for crime management

Relative Advantage

Relative advantage is the degree to which an innovation is perceived as being better than the idea it supersedes [START_REF] Rogers | The diffusion of innovation, 5th edn[END_REF]. The perceived benefit of innovation has a significant effect on an organization's intention to adopt an innovative technology (Rogers, 2003). Prior research found that there is a positive relationship between the relative advantage of new technology and the acceptance of an innovation [START_REF] Ifinedo | Measuring Africa's E-Readiness in the Global Networked Economy: A Nine-Country Data Analysis[END_REF]; Yang et al, 2015; [START_REF] Kumar | Factors Influencing Adoption of Augmented Reality Technology for E-Commerce[END_REF]. AI allows an organization to reduce costs, increase efficiency, and amplify human intelligence (Curran and Purcel, 2017). Hence, we hypothesize, H3: Relative advantage is positively impacted by LEA's intention to adopt AI for crime management

Top Management Support

Top management support refers to the engagement of a top-level leader for IS/IT implementations [START_REF] Ifinedo | Measuring Africa's E-Readiness in the Global Networked Economy: A Nine-Country Data Analysis[END_REF]. Applying AI for transformation is a strategic decision, and top management plays a critical role. Top management commitment can also have a significant positive influence on new technology adoption in terms of articulating a vision [START_REF] Yang | Understanding SaaS Adoption from the Perspective of Organizational Users: A Tripod Readiness Model[END_REF], providing capital funds [START_REF] Hung | Factors Influencing Business Intelligence Systems Implementation Success in the Enterprises[END_REF]) and allocating resources. Hence, we hypothesize, H4: Top management support is positively impacted by LEA's intention to adopt AI for crime management.

Organizational Readiness

Organizational readiness refers to the extent to which organizational divisions are prepared to adopt new technology and implement organizational change. The barriers to AI adoption are not limited to the technical context, but also relate to the skills, organizational resources, processes, structures, and agility of the organization [START_REF] Alsheibani | Artificial intelligence adoption: AI-readiness at firm-level[END_REF]. Hence, we hypothesize, H5: Organizational readiness is positively impacted by LEA's intention to adopt AI for crime management

Policing Strategy Orientation

Policing strategies extend beyond conventional models of responding to calls for service and seek to increase crime prevention, intervention, and response effectiveness. The ability to forecast where and when the next crime or series of crimes take place (Uchida, 2014) has become of utmost importance to prevent crime from happening by effectively using new strategies such as Intelligence-led policing and Predictive policing. Greater emphasis on tackling criminals would be more effective than focusing on Crime (Ratcliffe, 2003). Hence, we hypothesize, H6: Policing strategy orientation is positively impacted by LEA's intention to adopt AI for crime management

Availability of AI Vendors

AI vendors refer to the companies that possess the necessary knowledge, skills, and experience in AI technologies that offer products or solutions for the organizations seeking an opportunity to transform their business. Vendor partnership has been empirically supported as one of the critical determinants for innovation adoption [START_REF] Ahmadi | Exploring potential factors in total hospital information system adoption[END_REF]. If agencies know they can collaborate effectively with vendors, they are more likely to adopt the new technology [START_REF] Huang | ERP implementation issues in advanced and developing countries[END_REF]. Hence, we hypothesize, H7: Availability of AI vendors positively impacts the adoption intention of AI by LEA for crime management

Regulatory Support

Regulatory support refers to the policies and regulations that encourage and accelerate the adoption of innovative systems at the organizational and community levels. Government regulatory issues are the main environmental factors that can affect new innovation adoption [START_REF] Ifinedo | Measuring Africa's E-Readiness in the Global Networked Economy: A Nine-Country Data Analysis[END_REF][START_REF] Yang | Understanding SaaS Adoption from the Perspective of Organizational Users: A Tripod Readiness Model[END_REF]. Governments need to put in place necessary data protection, ethical and legal frameworks to ensure that AI systems are developed and applied based on the values and citizen's fundamental rights. This leads to the following hypothesis: H8: Supportive regulatory environment is positively impacted by LEA's intention to adopt AI for crime management

E-Government Initiatives

E-Government initiatives target increasing accessibility to the needed information, enhancing the efficiency of operations, facilitating collaboration, lowering maintenance and operating costs, and improving organizational (and inter-organizational) performance and productivity [START_REF] Moore | Emotional intelligence for school administrators: A priority for school reform?[END_REF]. Many governments across the world have since developed or in the process of developing National AI strategy and action plans, such as access to data through open platforms that will help drive the adoption of AI systems. This leads to the following hypothesis: H9: Supportive e-Government initiatives are positively impacted with LEA's intention to adopt AI for crime management.

4

Proposed Methodology and Future Work

We will be using a survey methodology for testing the proposed hypotheses. Validated scales from existing literature will be adapted to the research context to formulate the questionnaire. To measure the items, we would use a 5-point Likert scale. Questionnaires so formulated would be distributed to middle and senior-level members of law enforcement agencies and relevant AI technology providers.

Expected Contribution

This Research-in-progress paper is expected to make three significant contributions.

First, the validated model will help provide insights into how the adoption theories such as TOE and DOI could be extended in the context of AI adoption and crime management and set the direction for future research in this area. Second, this would help in formulating and updating AI policies and regulations. Third, on the practical front, the results of the study will be useful for law enforcement agencies to strategize and focus on factors and capabilities which will drive AI adoption, contributing to significant crime reduction and management.

Fig. 1 .

 1 Fig. 1. Adoption of Artificial Intelligence for Crime management