Vinay Reddy Venumuddala

Rajalaxmi Kamath

Emerging Technologies and Emergent Workplaces: Findings from an Ethnographic study at an Indian I.T Organization

Keywords: Work from Home, Artificial Intelligence, Indian IT industry

Over the past four decades, Indian Information Technology (IT) industry has been delivering traditional software and BPM (Business Process Management) services to its clients across the globe. Providing cost-optimized, yet high-quality services following standard process methodologies has made it an attractive destination to clients across industry verticals. Today, the challenge before this industry is to provide emerging technology solutions to clients in their digital transformation drive. Situated at this pivotal juncture in its journey, the 'work from home' (WFH) norm during to the recent COVID-19 pandemic posits challenges of a new kind for this sector. We explore these challenges based on our four-month ethnographic study (Jan-May, 2020) in a service-based IT organization situated in Bengaluru, which over the past five years has been developing Artificial Intelligence (AI) based solutions to its clients.

Introduction

Indian Information Technology (IT) Industry has now had over a four-decade history of providing software and business process outsourcing/management (BPO/M) services to its clients across the globe. It has moved from providing labour arbitrage driven simple software coding and BPO services, to providing high-end custom software and BPM services [START_REF] Arora | The Indian software services industry[END_REF][START_REF] Dossani | The next wave of globalization: Relocating service provision to India[END_REF][START_REF] Jalote | The growth and evolution of India's software industry[END_REF]. Providing cost optimized, yet high quality software and BPM services, following standard process methodologies has made Indian IT an attractive destination to clients across industry verticals [START_REF] Jalote | The growth and evolution of India's software industry[END_REF]. The challenge before this industry today is to provide emerging technology solutions around Artificial Intelligence (AI), Big Data, Cloud and IoT, to clients in their digital transformation drive. While developing new technology solutions that build on existing standard process workflows may be smooth, the challenge is to integrate solutions around technologies such as AI which require an overhaul of process workflows [START_REF] Amershi | Software engineering for machine learning: A case study[END_REF][START_REF] Zaharia | Accelerating the Machine Learning Lifecycle with MLflow[END_REF].

Situated at this pivotal juncture in its journey, the 'work from home' (WFH) norm during to the recent COVID-19 pandemic posits challenges of a new kind for this sector. Though the usual software services and emerging technology projects continued to be served with employees working from home [START_REF]Work From Home: If The Experiment Works, It Can Continue When The World is Better Again[END_REF], visible concerns around security and infrastructure limitations made it harder for Indian IT/BPM firms to facilitate their employees working from home [START_REF]Covid-19 impact: IT firms seek client waivers so staff can work from home[END_REF]. In our study we attempt to understand the impact of WFH as it was unfolding in an AI based project in an Indian IT organisation. We were involved in an ethnographic study in a service-based IT organization situated in Bengaluru, which has been developing and delivering customized software solutions over the past 25 years and for the past 5 years has started delivering AI based projects to its long-standing clients. As a part of this ethnographic study, during the months Jan-May, 2020, one of us worked as an intern in this organization and was a member of a running AI project team. Because of the nation-wide lock-down due to pandemic in March 2020, the researcher worked for 3 months in office and a month at home. Serendipitously, we were thus able to observe the nature of work interactions between employees both in office and while working on the project from home. Our research method, which is to carry out ethnographies at workplaces, is motivated by the works of . Our findings suggested that while the organization's efforts in streamlining workflows for AI project are still a work in progress and fraught with challenges, these challenges have further deepened because of a transition to WFH during this pandemic. We illustrate this gap by deep diving into the nature of interaction between work roles in a typical AI project carried out in office and how it changed during work from home.

Emerging Technologies Indian IT and WFH

Emerging Technologies and Indian IT sector

A major portion of Indian IT industry is constituted by organizations that cater to custom software and BPO/M services. As of the year 2016, these services together contributed to around 49% of the total service exports from the country, to over 7% of the country's GDP, and constituted over 70% of the offerings from this industry in total [START_REF] Nasscom | IT-BPM Strategic Review[END_REF]. Prior to the year 2000, Indian IT was mainly known for providing simple software programming services relying on its vast pool of engineers, with limited scope for technical innovation or managerial contributions [START_REF] Arora | The Indian software services industry[END_REF]. The key driver during this time was the reengineering movement of the 90s which necessitated a standardization of work processes and practices around software development and business process outsourcing so that the work can be distributed across the globe [START_REF] Dossani | The next wave of globalization: Relocating service provision to India[END_REF]. This service globalization facilitated Indian IT companies to provide customized software and BPO services that were mainly driven by labour or cost arbitrage deals [START_REF] Arora | The Indian software services industry[END_REF][START_REF] Dossani | The next wave of globalization: Relocating service provision to India[END_REF][START_REF] Jalote | The growth and evolution of India's software industry[END_REF]. Further global standard software platforms reduced the reliance on firm-specific investments, as individuals anywhere from the globe could now cater to the business requirements provided they have the necessary skills to work on such platforms [START_REF] Dossani | The next wave of globalization: Relocating service provision to India[END_REF]. This implied a vast manpower availability, in the form of engineering workforce, enabled the Indian IT industry to quickly reap the benefits from this global changes in service industry [START_REF] Arora | The Indian software services industry[END_REF][START_REF] Dossani | The next wave of globalization: Relocating service provision to India[END_REF]. Not only did Indian IT helped global enterprises with skilled manpower at low cost, but they also ensured quality of software and service delivery. Majority of the global IT companies that registered highest levels in their maturity of software development processes were from India [START_REF] Jalote | The growth and evolution of India's software industry[END_REF][START_REF] Amershi | Software engineering for machine learning: A case study[END_REF][START_REF] Zaharia | Accelerating the Machine Learning Lifecycle with MLflow[END_REF][START_REF]Work From Home: If The Experiment Works, It Can Continue When The World is Better Again[END_REF][START_REF]Covid-19 impact: IT firms seek client waivers so staff can work from home[END_REF][START_REF] Barley | Bringing work back in[END_REF][START_REF] Barley | Technicians in the workplace: Ethnographic evidence for bringing work into organizational studies[END_REF][START_REF] Nasscom | IT-BPM Strategic Review[END_REF][START_REF] Naidu | India: emerging knowledge base of the 21st century[END_REF].

Today, both the IT and BPM segments of the industry have to gear up to provide emerging technology solutions to its clients around Artificial Intelligence (AI) and Cloud. Immense amount of data is being generated within organizations across industry verticals, and the social world, at an unprecedented rate in terms of its volume, variety, velocity and veracity constituting what is often termed as the 'Big Data' [START_REF] Abbasi | Big data research in information systems: Toward an inclusive research agenda[END_REF]. Analysing such Big Data will rely on cloud services to manage the data storage and service requirements [START_REF] Buytendijk | Hype cycle for big data[END_REF], and AI based solutions can generate actionable insights from such data for organizational decision makers [START_REF] Abbasi | Big data research in information systems: Toward an inclusive research agenda[END_REF][START_REF] Constantiou | New games, new rules: big data and the changing context of strategy[END_REF]. Adoption of these new technologies enhance the competitive advantage of firms [START_REF] Chen | Business intelligence and analytics: From big data to big impact[END_REF], through improvements in their supply chains [START_REF] Wang | Big data analytics in logistics and supply chain management: Certain investigations for research and applications[END_REF], by enabling swift responses to prospective business opportunities [START_REF] Liu | Big data and predictive business analytics[END_REF], enhancing the likelihood to produce new products and services in comparison to their peers [START_REF] Ransbotham | Analytics as a source of business innovation[END_REF], and therefore improve their overall performance [START_REF] Liu | Big data and predictive business analytics[END_REF]. Driven by these factors clients are driven to adopt emerging technology solutions and for which they are again looking towards their long-standing service providersthe Indian IT [START_REF] Fersht | Making the leap from strategic to effective BPM[END_REF]. While Indian IT traditionally catered to a majority of clients from industry verticals like manufacturing, telecom, banking and finance, driven by digital transformation goals around emerging technologies new verticals like retail and healthcare are getting added to this list [START_REF] Nasscom | IT-BPM Strategic Review[END_REF]. The need for delivering emerging technology solutions to clients is necessitating organizations within Indian IT to incorporate new work roles, and form new teams to develop in-house capabilities around such technologies. These teams are now comprised of diverse work roles spanning both traditional and emerging technologies such as data scientists, data/cloud/software engineers, cloud/software architects, and also including traditional project management roles such as business analysts, and project managers [START_REF] Nasscom | IT-BPM Strategic Review[END_REF][START_REF] Nasscom | Uncovering the True Value of AI, Executive AI Playbook for Enterprises[END_REF]. It is now imperative for Indian IT organizations working on integrated software and emerging technology projects, to facilitate a seamless collaboration across such a diversity of work roles.

Work from Home

With the COVID-19 pandemic shifting the office work to home, a slew of changes are expected in the manner in which work might be carried out in future if the situation persists over a longer period of time. It is expected that the usage of digital platforms and the demand for expert workforce in technologies such as Cloud might increase, while the need for commute and office space may come down. Further, in the long term, working from home may also lead to detrimental effects on work-life balance of employees, when the convenience of working from home gives rise to some sort of 'permanently-at-work' culture [START_REF] De | The tech effects of Covid-19, and offices of the future[END_REF]. In addition to these expected changes, literature also discusses about the ways in which digital work can alter the forms of leadership and work autonomy [START_REF] Dittes | Toward the workplace of the future: How organizations can facilitate digital work[END_REF], how they can blur the boundaries between public and private spheres of life [START_REF] Jarrahi | A pragmatic approach to managing enterprise IT infrastructures in the era of consumerization and individualization of IT[END_REF]. Increased digital work can also result in 'techno stress' [START_REF] Ayyagari | Technostress: technological antecedents and implications[END_REF], reduce employee autonomy [START_REF] Mazmanian | The autonomy paradox: The implications of mobile email devices for knowledge professionals[END_REF] and therefore it is important that organizations carefully balance all these positives and negatives of WFH [START_REF] Richter | Locked-down digital work[END_REF].

While these impacts of WFH are discussed in the context of organizations in general, we intend to understand the concrete implications of WFH in Indian IT service-based organizations that are currently transitioning towards providing emerging technology solutions, in particular around AI. It is widely acknowledged that a move towards providing AI solutions, that require building machine learning (ML) models, requires a rather revolutionary adjustment of existing traditional software work processes [START_REF] Amershi | Software engineering for machine learning: A case study[END_REF][START_REF] Sculley | Hidden technical debt in machine learning systems[END_REF]. The absence of strict abstraction boundaries between different ML work components [START_REF] Amershi | Software engineering for machine learning: A case study[END_REF][START_REF] Sculley | Hidden technical debt in machine learning systems[END_REF], forecloses the possibility of modular development [START_REF] Amershi | Software engineering for machine learning: A case study[END_REF], warranting a constant iterative interactions between work-roles handing different stages of the ML workflow [START_REF] Amershi | Software engineering for machine learning: A case study[END_REF][START_REF] Zaharia | Accelerating the Machine Learning Lifecycle with MLflow[END_REF][START_REF] Sculley | Hidden technical debt in machine learning systems[END_REF]. In contrast to AI solution development, the key tenets of traditional software development are that it is modular, and allows for customizability and reuse of software programs or modules [START_REF] Amershi | Software engineering for machine learning: A case study[END_REF][START_REF] De Souza | Sometimes you need to see through walls: a field study of application programming interfaces[END_REF]. These tenets enable clear division of tasks between various work roles in different stages of an SDLC, i.e., software development life cycle [START_REF] Dennis | Systems analysis and design[END_REF][START_REF] Esbensen | Routine and standardization in global software development[END_REF][START_REF] Yilmaz | A systematic approach to the comparison of roles in the software development processes[END_REF]. Our ethnography provided us an opportunity to observe the WFH related challenges facing an AI team within this service-based IT organization, who have been attempting to work around the traditional standardized software development processes while working for an AI project. time resource in a running Natural Language Processing (NLP) based project, (2) to understand, identify and document a common workflow based on the team members' experience of working across multiple projects over the past 5 years, and (3) to periodically report our research findings to members from the team. The first two tasks were considered as a value-addition that the researcher would bring to the team, and the last task was to help researcher triangulate and validate his/her findings. These assigned tasks allowed researcher to become a usual member of the team.

Ethnography at workplace

According to Barley [9], study of work in organizational theory has gone into the background, and many studies either simply acknowledge the complexity of work or just gloss over the 'issue of how work might be changing'. However, according to him, pushing the complexities of work into the background while studying organization is not of much help in understanding organizational change. Because, "when nature of work in an organisation changes because of new technologies, or marketsorganisational structures have to adapt or risk becoming misaligned with the activities they organise." [START_REF] Barley | Bringing work back in[END_REF]. It is therefore argued that bringing work back into organizational studies is imperative especially in today's context where technologies are rapidly evolving and markets are constantly expanding, affecting organizations both from within and from outside. Our method of research was ethnography at a workplace motivated by these works of Barley and Kunda [START_REF] Barley | Bringing work back in[END_REF][START_REF] Barley | Technicians in the workplace: Ethnographic evidence for bringing work into organizational studies[END_REF]. According to them, ethnographic research that engages closely with work practices, allows researchers to provide 'insitu' explanations about the complexities of work and how they impact organizational responses to changes happening in the environment around. While providing a rich 'emic' perspective or the 'native point of view' about the day-to-day work and work interactions, it also motivates the researcher to simultaneously relate it with the 'etic' concepts which are the 'analytic constructs removed from the native point of view' [START_REF] Barley | Bringing work back in[END_REF].

It also provides a possibility to understand the deeper causes of organizational change with changing nature of work [START_REF] Barley | Bringing work back in[END_REF][START_REF] Barley | Technicians in the workplace: Ethnographic evidence for bringing work into organizational studies[END_REF]. Further in the context of work roles within organizations, by studying situated and actual roles rather than ideal-typical ones, researchers can have a more nuanced understanding of these roles, both in their relational and non-relational aspects.

Data

Working with team members in a project, joining them for breakfast, lunch, and coffee, engaging in impromptu conversations near cubicles, attending team meetings, knowledge transfer sessions, birthday celebrations, employee farewells, and many more events laid the basis for the researcher building a strong rapport with the team members. Further, the assigned task of conducting unstructured interviews with some of the team members to understand the project work flows, allowed the researchers to get a concrete sense of the overarching work processes within the organizational structure. Since the researcher had also worked from home for over a month, he/she was able to document the nature of interactions between different work roles that happened in the context of an AI project. The contrasting observations between work interactions at office and from home, in the context of this project was the prime motivation to carry out this particular study. Observations during interactions at work (either in office or from home), unstructured interviews, and impromptu conversations, allowed the researcher to not only internalize his experiences from the field, but also document them in rich detail on a daily basis in the field notes. This field notes were constantly shared between the two researchers, which motivated numerous discussions and reflections which guided our overall research. Our ethnographic field notes ran into over 100 single-spaced pages that amounted to over 75000 words. In total the ethnographer closely interacted with around 24 team members with their experience in this industry ranging from 6 months to over 15 years. These members occupy work roles such as data engineers, business analysts, managers, data scientists, software engineers, technical architects, and team lead.

Findings -AI Projects and Work Interactions

A majority of the AI projects taken up by this team were for the long-standing clients of this organization. Having established itself as a major service provider of IT-BPM services to its clients over two decades, it has recently ventured into providing emerging technology solutions. Standard work processes around custom software maintenance and BPM services ensured this organization to maintain quality while delivering cost optimized solutions. As a continuation of this client-service provider relationship, most of the long-standing clients of this organization now expect it to also provide AI based solutions. However, given the organizational structure built around traditional software and BPM, a majority of the projects were of an integrated nature, predominantly AI integrated on top of traditional BPO or custom software services. Nevertheless, in the process of working on AI projects for clients over the past 5 years, the AI team within this organization also managed to develop standalone IP cores related to AI, with the intention of enabling reuse for future projects.

Typical project workflow

One of the researcher's assigned task of documenting project workflows, enabled us to observe an important aspect of AI projects within this organizationthe continued dominance of traditional software work processes. The project proposal meetings often revealed that there were inherent assumptions about customizability and reuse of AI project components previously developed. During the team meetings, those in project management roles like the business analysts, and managers, usually presented highlevel architectural diagrams that assumed some sort of modularity in AI project components. During their initial stages, many of these projects involved constant back and forth interaction between clients, and project managers and data scientists from this organization to enable AI use-case identification. However, after these initial rounds, when data engineers take up the execution tasks, status of work was often evaluated through agile status meetings. An experienced senior manager in this regard said that, 'in service-based companies where research wings on AI are setup, they often tend to follow traditional software kind of methodologies.' Since the organization has to make trade-offs between accuracy and resource usage in these projects, we find the Team Lead and Project Managers pushing for maximum reuse of existing project components. Data-scientists are typically considered costly resources, and as a result they are often engaged only during the initial stages of the project or while eventual signing-off of the work done by data engineers. In an ideal scenario, the two major work components, i.e., data pre-processing and model development need to be worked out in tandem for a given use case [START_REF] Amershi | Software engineering for machine learning: A case study[END_REF][START_REF] Sculley | Hidden technical debt in machine learning systems[END_REF]. But what we noticed in typical AI projects here, was a clear separation of tasks between data engineers who work predominantly on the pre-processing components, and data scientists who work independently on the model development tasks. Data scientists are more involved in the work of IP development from the client's business use-cases. As a result, they pick up off-the-shelf data sets relevant to similar use-cases and build standalone machine learning (ML) models as IPs to be reused in future projects. Data engineers on the other hand, encountering a changing volume and variety of data from the clients during the time of project execution, are constantly involved in sharpening the pre-processing programs for feature data-extraction. Insofar as model development is concerned, they often end up feeding their extracted data to the standalone IPs that have already been developed by the data scientists, which would now be a part of the solution stack of this team. While this strategy could result in an accuracy drop [START_REF] Amershi | Software engineering for machine learning: A case study[END_REF][START_REF] Sculley | Hidden technical debt in machine learning systems[END_REF], the 'project managers' (business analysts and managers) find it a better option because given the manual validation by BPO workforce to fill the gaps, it saves costs.

Project execution and work interactions in office

The ethnographer worked with two data engineers, business analyst, and a senior manager, during the project execution phase, where he/she worked closely with the two data engineers and their work together was periodically overseen by a senior manager and a business analyst. Weekly status meetings were therefore organized between the data engineers and the business analyst and/or project manager in order to evaluate the progress. Since the project was ultimately headed by the team lead, a monthly status meeting used to be conducted where the team lead evaluated the overall progress of the project and the work done by each of the data engineers. This project like standard AI projects involve pre-processing data, model-building, training, and deployment which ideally needs constant collaborative efforts across different work roles [START_REF] Amershi | Software engineering for machine learning: A case study[END_REF]. However, we observe a separation of tasks and very little formal collaboration between work roles in this team. At the start of the project, the data engineers' task was to build on existing pre-processing programs taken from previous projects and try to see if they can be reused for the given client use-case. It was too early in the project execution phase and the volume and variety of client data was so low that it was hard for the data engineers to even think of model building. Model building needs data that is representative of the observations related to client's business use-caseand therefore it calls for ample volume/sample size, and enough variety. Despite not having a chance to build models, the data engineers used to have informal discussions with the data scientists about the appropriate model building strategy and the fit of the existing IPs to be used in the future, for this project.

In office, such discussions happened at the convenience of the data-scientists who sat very close to our cubicles. Data-scientists, involved with IP development for the organisation, often are part of the proposal/project initiation phases of upcoming projects, and work towards streamlining AI workflows. It is not easy to get their time. Their skype (formal chat platform for employees) status used to be one of 'do-notdisturb', 'busy' or, 'in-a-meeting' all the time. To give an instance, for the very first meeting around a better way to start executing the current AI project, the researcher and the two data engineers caught the data scientist during lunch and talked to him about the project. We also asked him for his free time when we could have a discussion about this. Since he was busy, he called sometime later in the evening and explained on a white-board in a discussion room about how he developed ML models around similar use-case for a previous project that is now an IP. After an hour, he quickly realized that there is another meeting that he was required to attend, and left abruptly. But over the next couple of days, the data engineers were still able to ask him their doubts and get clarifications, whenever they found him free in his office.

However, the interaction between data engineers and business analysts/senior managers was slightly different. Their task was to oversee the data engineers' utilization and project status and these meetings were more formal and happened on a weekly basis. During these meetings each of the data engineers present the status of their work, and what they intend to do in future. A formal meeting with team lead that happened on a monthly basis also proceeded along similar lines, only that the team lead interacted mostly with the project managers, who in turn questioned the data engineers about the progress. Following quotes from our field notes summarize the kind of interactions that happened during these meetings.

Data Engineers (DE) reporting status to Business Analyst (BA)/Project Manager (PM): "We tried this out, we worked on this with the help of another person [software engineer] in the team. There are some problems that we are facing. We will ask any data scientist in the team and finalize the way to go for this problem." BA reply to the DEs: "BA seems to be guiding the implementation based on his experience from previous project. He is directing the Data Engineers for building plans for themselves and work. During the meeting he made statements like we need to identify, we need to do this, don't do everything at once, take up one task after the other, try a script or hand-based rules for this problem, you guys refer to what we did in our previous project, look at the files that were prepared." PM reply to the DEs: "If you are finding that this problem is taking too long time and needs extra hands, you can take help from any data engineer or check with any data scientist whenever he/she is free. In the previous project we did it like this. In your whiteboard discussion with data scientist he would have explained you some 3-4 approaches. Take 1 or 2 and try them out and see if they work."

Not only with respect to their own projects, but the overall work in office allowed these data engineers to gain knowledge from their team mates about other projects and also about other kind of work beyond AI, such as integrating AI modules into existing softwares and their deployment in the cloud. The researcher was part of many such informal discussions these data engineers had with their lunch mates who were also their college friends. Conversations between them always started with a common question, 'what are you currently working on?', that is posed on a daily basis. Unknowingly, in these conversations, a good amount of talk about happenings in their organization, knowledge of the projects they are working on, and the projects others were working on, got passed around. From the field notes, we were thus able to intuit these informal channels through which 'tacit' knowledge got accumulated within an organization.

Lastly, the researcher noticed that the data engineers usually worked together to complement each other's work when in office. We noticed that one of them was good at programming, while the other was reluctant to spend time on programming and instead was more interested in the model building component of their project. Anything related to the project they used to engage in it together. The one who was less interested in programming used to consciously find ways to engage with data scientists. He used to take his fellow mate and approach data scientists whenever he found them free and used to engage in informal discussions about existing ML strategies for different projects. He also used to ask business analysts to involve him in the meetings with clients so that he could understand the business use case better. The other person who was good at programming used to similarly approach software/cloud engineers and architects for getting clarifications about best ways to build their pre-processing programs, to gain a general understanding of how the software/cloud engineers usually integrate AI modules on custom software and how they deploy it in the cloud, and so on. Since both of them mostly worked together, despite one person's initiative, both the team members used to benefit from such interactions.

To summarise, we noticed that, the overarching work processes in this organisation, which are heavily oriented towards traditional software development have provided very few formal avenues for data engineers to gain understanding beyond their data pre-processing work. However, we observe that in the office spaces, informal interactions with their seniors afforded them a possibility for gaining such understanding. For example, they were able to approach data scientists about ML model building, cloud architects about model deployment in cloud and business analysts about the nature of client interactions, informally. This was less likely within the formal project setup that was largely dominated by the regular project meetings between data engineers and those executing the project in the organisation.

Project meetings during WFH

"Less or no scope for informal knowledge sharing like in office. Every call becomes a client call where one reports status and other evaluates status. Persuading supervisors through effective communications becomes most important." -Reflections from attending project status meetings online, Field Notes During the lock down period when employees of this team were asked to work from home, the dynamics of interaction between team members working on this AI project had changed. The formal status meetings with project managers and the team happened online, but still followed a similar style of interactions. But the data engineers now lost all those informal pathways to gain situated knowledge from experienced technical experts and business analysts which used to be possible while in office.

"WFH demands that individuals spend a lot more time before having to present anything in a meeting. A lot more work needs to be done in preparing the presentation so that it can be shared, read out and articulated properly so that the others could understand. Further it happened that the two data engineers were asked to present their respective works, and it became difficult for them to segregate and present. While in the meeting room last time they were able to take turns and jointly present, here it was a bit difficult for them to do that, so they divided the same presentation between the two and presented." -Reflections from attending project status meetings online, Field Notes Another conspicuous observation we could identify based on the online project status meetings was the mode of communication. We noticed that the two data engineers who used to work together while preparing their status presentations in office, failed to do that when the meetings shifted online. Each one of them independently prepared very detailed presentations indicating their share of work done in the project. Hitherto in office, especially during the status meetings with the business analysts and managers, we noticed that the two data engineers used to go onto the board intermittently while presenting their progress of work in the project. Sometimes when their presentations weren't ready, they used to present their status on the board, extempore. However, with work from home, we noticed that in every weekly status meeting and the monthly meeting, they came with detailed presentations describing what they have done over that particular week or month and what will they do in future.

We also noticed that clarity of presentation is very important during these online meetings. For example, the two data engineers during their presentations in office were able to complement each other while presenting, since they worked on it or at least discussed with each other in office. However, during online meeting we noticed that this did not happen. Since each of them prepared their presentations independently and didn't get a chance to informally discuss, like they would have done in office, we noticed that while one of them was presenting the other was silent.

Discussion and Conclusion

Our situated description of project work flows at the organizational level indicate that there is a clear separation of tasks between data scientists and the data engineers, which is not ideal while developing an AI project [START_REF] Amershi | Software engineering for machine learning: A case study[END_REF][START_REF] Sculley | Hidden technical debt in machine learning systems[END_REF]. Further, based on the interaction between work roles during the regular project status meetings, we observe that the traditional project managers continue to push for customization and reuse of prior project components. As a result, in this organisation, AI projects are still being carried through the traditional software workflows and processes. Building workflows suiting AI projects, which allow for constant back and forth collaboration between work roles, is still a work in progress. We observed that data scientists who are engaged in developing standalone AI related IPs, were also actively trying to promote workflow changes suiting the requirements of AI projects. Data Engineers on the other hand are making efforts to go beyond the task separation envisaged within traditional software methodologies, and collaborate with other work roles. Office spaces provided them avenues for having informal interactions with data scientists which enabled them to reflect about ML model building while working on their usual pre-processing tasks. However, during WFH, data engineers were unable to gain this situated understanding of ML model development. On the other hand, the influence of traditional software project management continued undeterred even when project meetings shifted online. From our ethnographic findings and discussion, it is clear that this IT based organization, is largely embedded in traditional software work processes, despite its avowed move towards delivering AI based projects. However, we surmise that the lack of formal AI workflows was actually being made good, tacitly, through informal interactions among its employees. In the absence of a well laid out AI structure, the beginning of what goes in the name of 'tacit' AI structure was being actualized in this organization. To that end, the Organization had done the right thing in having a nascent 'AI team', irrespective of work domains, where all employees working on such AI projects sat and worked together. This is bound to change with the shift towards WFH, as replicating such tacit structures online is a challenge facing this organization. While for established MNCs, that specialize in emerging technologies, and provide platformised AI products to the industry, transition to WFH might not be that difficult. However, for Indian IT industry which is newly venturing into these emerging technologies, we find that the extant organizational workflows and processes are posing unique challenges that needs attention.

Figure- 1

 1 illustrates the interaction between work roles and changes during WFH diagrammatically.

Figure 1 :

 1 Figure 1: Work Interactions during AI project

Research Context and Methodology3.1. Empirical ContextOur empirical context is a service-based organization based in Bengaluru, India, providing IT-BPM services to its clients across the globe for over 25 years. In addition to traditional software and BPM services, this organization in the past few years has been actively responding to the emerging technology needs of its clients. By gathering members from traditional software teams and recruiting employees for new roles, this organization has formed an in-house team specifically for this purpose. This team, which we call AI team, has been developing customized AI solutions for clients seeking business process automation, and in the process accumulated several proprietary AI solutions (IPs) over the past 5 years. Solution stack constituted by such IPs has enabled this organization to not only offer turnkey services around AI for its future clients, but also to deploy them over cloud platforms such as AWS (Amazon Web Services), Azure and GCP (Google Cloud Platform) allowing their generic usage in the online marketplace. While client centric projects formed the major source of building IPs (for eventual customization and reuse), research projects related to AI utilizing off-the-shelf data is another source. Since our research objective was to understand the changing nature of work and mobility in the Indian IT industry, working as a resource in a project was beneficial over formal interviews or questionnaires, because it allowed us to understand the situated work-practices as they unfolded. Taking into account our research objectives, and the researcher's prior qualifications (industry experience, and knowledge of machine learning from doctoral coursework) the head of the team assigned three tasks to the intern while he/she worked in this team.[START_REF] Arora | The Indian software services industry[END_REF] to work as a full-


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A Study on the Factors influencing Behavioral Intention of Indian consumers in adopting Voice Assistants

Keywords: Internet of Things (IoT), Voice Assistants, Behavioral intention, System quality, perceived usefulness, perceived ease of use, Behavioral Control, intention to use

In the past few years, Internet of Things (IoT) has led to multiple devices interacting with humans as well as other devices, helping create a huge network of these entities that share data on a regular basis. The use cases of IoT technology have become synonymous with devices used to enable "Smart Home", wherein various applications inside the house are usually controlled using smartphones or smart speakers.These Smart Speakers and Smart Phones are enabled using Voice Assistants developed by various Tech Giants from all over the world. Various Tech Companies have developed their own Voice Assistants that are integrated in multiple devices, ranging from Smart phones, Smart Speakers to Smart TVs. Using their ability to connect to the Internet and the eco-system formed by the Tech Giants, Voice Assistants are helping to improve the quality of life.

Despite the extensive coverage in media, review of Literature showed that no such research about adoption of Voice Assistants by Indian Consumers has been conducted. There hasn't been any study that evaluates factors that influence the adoption of Voice Assistants by Indian Consumers. That is the gap which this research addresses and tries to fill. Through this study, we aim to build a model which finds out Indian consumers' acceptance of Voice Assistants for this technology to reach commercialisation.

The purpose of this study is to explore the user attitudes, satisfaction, and other factors governing and encouraging or discouraging the intention of Indian Consumers to adopt Voice Assistants. Through analysis, we can find out the inconveniences of adoption and usage process and propose the direction of product improvement.

1

Introduction:

In recent years, the use of devices such as Smart phones and speakers has become widespread in India. According to a recent research carried out by Edison research-a US based research firm, smart speakers are influencing a wide range of consumer habits like reduction in their time spent with screens, consumption of audio content like news, music, documents, podcasts, commerce etc. This revolution has been accelerated by lower data costs over the past few years in the country. The market for smart speakers was established in India in 2017 with the successful launch of Amazon Echo. Since then, many companies have launched various products that compete with each other. But the response of these consumers in India hasn't been as positive as that of foreign users. It really brings up the question of whether the Voice Assistants that meet the needs and requirements of foreign users can really adapt to the Indian Market. Each country is different and the consumers differ extensively as well. If the Voice Assistant usage in India has to increase beyond the initial uptake, then companies like Amazon and Google will have to adapt for the Indian audiences to increase acceptance and improve experience. Given the huge investment that has been made by Technology Companies in creating their own respective ecosystems, it is very important to understand and explain the intention of Indian consumers to adopt Voice Assistants.

Literature Review:

This ( Berdasco, López, Diaz, Quesada, & Guerrero, 2019) study evaluates two dimensions of Voice Assistants: how natural the answers sound to the users and correctness of the answers. The findings showed that Google Assistant and Alexa are way better as compared to Cortana and Siri. Cortana and Siri provided the poorest performance according to the research. Siri is one of the most popular Voice Assistants being used in the US, due to the popularity of iPhones there and yet showed poor performance as compared to Google Assistants and Alexa.

The study conducted by [START_REF] Hwang | Would satisfaction with smart speakers transfer into loyalty towards the smart speaker provider? The 22[END_REF] examined the kind of effect that consumer satisfaction towards smart speakers may have on usage intentions and expression of loyalty towards the brand. User Satisfaction was calculated based on 2 parameters: Information Quality and System Quality of the smart speaker. The findings showed that the ones who were satisfied with smart speakers were using the service continuously and expressed loyalty towards the brand. Frequency of use of these devices also had a positive influence on intention to use.

A recent study carried out by [START_REF] Xiao | A Study on the User Experience of Smart Speaker in China-Focused on Tmall Genie and Mi AI Speaker[END_REF] showed a comparison between the experience of users with two of the most popular Smart Speakers in China: MI AI Speaker and Tmall Genie. The users were found to be uncomfortable with the functional aspect of the Smart Speakers. These users also expressed their discomfort when it came to the usability aspect of these devices. The research also showed that very few participants use the smart speakers to control smart home devices due to the fact that penetration of smart home appliances in China is very meagre. This is actually a big selling point of smart speakers which cannot be made use of in China.

This study conducted by [START_REF] Kowalczuk | Consumer acceptance of smart speakers: a mixed methods approach[END_REF] was primarily to identify the factors that motivate or discourage consumers from using Smart Speakers in their daily lives. In order to conduct this research, acceptance models were tested, whilst following a mixed approach. The study found out that enjoyment had a strong effect on usage intention of consumers. Also, the results showed that security/ privacy risk, system diversity, technology optimism of consumers, and system quality had a huge impact on consumer acceptance. Apart from these factors, perceived ease of use and perceived usefulness had effect on consumer acceptance. [START_REF] Chu | Why would I adopt a smart speaker? Consumers' intention to adopt smart speakers in smart home environment[END_REF] conducted research in order to find out the factors that would affect a consumer's intention to adopt a smart speaker. A new model that was extended and developed using the Technology Acceptance Model (TAM) was proposed. The characteristics that were analysed in the model were product characteristics, social factors, consumer characteristics and financial factors. Some of the variables considered included attitude, enjoyment, Internet of Things (IOT) skills, social influence, cost, consumer self-innovativeness, trust, and reliability. Findings showed that Factors like Social Influence and Trust had a big impact on consumer intention to adopt smart speakers. Factors like security/privacy, IOT Skills, social influence also had an effect, Trust proved very useful in predicting attitude and perceived ease of use (PEOU). .

Conceptual Model

The The model to be used for estimating the behavioural intentions towards usage of voice assistants and smart speakers is a hybrid model which considers the parameters of both TAM and TPB theories

Exogenous variables:

1. System Quality-System Quality is defined as the observed quality of the various performance features of the system. The sound of the speakers, Response time, Robustness, Quality of responses etc. are the variables which contribute the system quality of smart speakers and voice assistants.

H4. System Quality is positively associated with attitudes to using smart home services.

Security/privacy risk:

Smart voice assistant systems collect data about residents' lifestyles, such as purchase preferences, in order to support them effectively. Consequently, these systems face the challenge of ensuring the safety of personal data (Balta-Ozkan et al., 2014). Kalakota and Whinston (1997) defined security risk as a "circumstance, condition, or event with the potential to cause economic hardship to data or network resources in the form of destruction, disclosure, modification of data, denial of service, and/or fraud, waste, and abuse." Security risk includes the risk of the violation of a user's privacy. In this study, therefore, security/privacy risk refers to voice assistant users' fear that their personal data will be leaked or that systems will be hacked by criminals.

H5. Security/privacy risk is negatively associated with attitudes toward using voice assistant services.

Perceived Cost

The perceived costs of systems strike a balance between the ease that a particular system caters to the price of installing the particular system. A rational person will implement a solution only if the perceived cost is less than the benefits catered by the solution. It is very difficult to enlist the benefits linked to cost or monetary savings in case of using voice assistants or smart speakers.

H6. Perceived Cost is negatively associated with the attitudes towards using smart speakers.

Self-Innovativeness

Self-innovativeness is the motivation (internal/external) to try using new technologies as an experiment. It is the behaviour trait where people use newer technologies on their own and find experimentation worth the time spent. The people who are innovators, having an inclination towards technology use latest technology devices. H7. Self-innovativeness is positively associated with the attitudes towards using voice assistant.

Embarrassment

This is an important factor as far as using technology is considered. People face social difficulties when trying to communicate with a device in public. It's this embarrassment that restricts many people from using voice assistants in public. Man people especially in India consider talking to a voice assistant in public to be socially weird or unusual.

H8. Embarrassment is negatively associated with the attitudes towards using voice assistants.

Research Methodology:

Primary as well as secondary research was carried out to understand the phenomenon of behavioural intention of using Voice Assistants in India. Survey Instrument: The mentioned relationships have been examined by the means of a structured questionnaire. The questionnaire consists of 24 questions as shown in Table 7. Other demographic variables such as age, gender, profession, and level of education are also considered.

Target Respondents:

The sample size consists of 100 respondents. Both males and females are taken into consideration. The respondents are included from diverse age groups, and levels of education practicing different professions.

Procedure: An online questionnaire consisting of all the questions was circulated amongst a sample set and they were asked to answer the questions. The responses were recorded for interpretation, analysis and discussion. SPSS was used to understand the effects of independent variables on the dependent ones and also to understand the strength of the relationships between the variables.

Data Analysis:

Data Analysis was done using SPSS and AMOS20. All hypotheses relationships in this study are estimated using Structural Equation Modelling (SEM).

Validity and Reliability Check:

Exploratory Factor Analysis was carried out to check reliability and validity of the model. The research instrument involved modified validated instrument scales from previous empirical studies for measuring of variables as shown in Table 7. A Likert Scale (1-7) was used to rate all the items on the scale. Principal Component Axis and Varimax Rotation were used in EFA. The Kaiser-Meyer-Olkin measure of sampling adequacy was 0.743 (greater than 0.6.) establishing sampling adequacy as shown in Table 1. The results of Bartlett's Test of Sphericity as given in Table 1 is also considered appropriate as value p<0.05 indicating that the test is significant and sample is adequate.

The correlation results show that the questions are mutually exclusive, mutually independent and not leading into each other. A highest correlation of 0.641 was found which shows that those questions are similar in nature. The correlation matrix had dimensions of a higher degree. All the models have a significance coefficient less than the determined 0.05 which shows that all the cases are statistically significant. The 6th case takes into consideration the maximum factors i.e. six. Hence we consider the model with six independent variables. The model has an R value as 0.789 and adjusted R-square 0.598, which shows that 59.8% of the variance in the dependent variable is due to the change in these independent variables as shown in Table 3 The result shown in Figure1 below indicate that all the predictor variables like attitude, subjective norm and perceived behavioural control have a direct and positive effect on the outcome variable behavioural intention.

KMO and Bartlett's Test

  Research Model of this study uses Technology Acceptance Model (TAM) penned down by Davis in 1989 alongwith Theory of Planned Behavior (TPB) by Ajzen (1991) in order to understand and predict the behavioural Intention of Indian Consumers in regards with adoption of Voice Assistants. The following factors are examined in this Research: System Quality, Security, Perceived Cost, Self-Innovativeness, Perceived Usefulness, Perceived Ease of Use, Attitude, Social Norms, Perceived Behavioral Control and Intention to Use and their relationship with each other. The conceptual model of study is shown as under:4Research Model and Hypothesis Development:

Figure 1 :

 1 Figure 1: Measurement Model and Structural Model

Table 1 :

 1 KMO and Bartlett's TestThe extraction of the factors clearly shows that all the factors have fairly good extraction in the analysis. Some technical factors like "compatibility with other devices" have low extraction i.e. 0.367. Other factors show fairly high extraction coefficients, which depicts their significance.The Analysis shows that 58.98% i.e. approximately 60% of the total variance can be accounted for five principal variable factors. Thus 24 questions can be grouped together into five different factors, which would properly explain the variance.

	Kaiser-Meyer-Olkin Measure of	0.743
	Sampling Adequacy.
	Bartlett's Test of	Approx.	974.508
	Sphericity	Chi-
		Square
		df	276
		Sig.	0.000

Model Summary

Model

The coefficients were analysed. The multiple regression model has a p-value of 0.042 which is less than 0.05, which shows that the results are significant.

Based on the regression analysis model given, the following coefficients can be used for the regression model. Y = -0.293 + 0.280X1 + 0.379X2 + 0.261X3 -0.205X4 + 0.203X5 + 0.195X6

Hence, according to the model used for determination of the intention to use voice assistants, it is clear that factors like the ease of use, system quality, lack of self-innovativeness (especially in India) and subjective norm coupled with peer pressure are the significant factors, considering the Indian context.

CMIN is the chi-squared coefficient. The values of CMIN/DF must lie between 1.0 and 5.0 in terms of the acceptable limits. Here, the independence model has a ratio of CMIN/DF of 4.660 which shows that the model is a proper fit as shown in Table 5 below:


Ambuj Anand
email: ambuj@iimranchi.ac.in

Exploring Net Benefits in the context of an E-Government Project

Keywords: Benefits, Conceptual Analysis, E-Government, Adoption

A typical e-Government project will undergo an impact assessment several times in its lifecycle. An important aspect of impact assessment is to measure benefits from the project, especially to the end-users. Often, when these benefits are measured, projects have positive scores, but when it comes to voluntary adoption of e-Government project on a large scale, the rate of adoption is slow. Not all the people who use it for the first time are willing to continue with the digital alternative. In this paper, we argue that one source of such contradictory findings is a unitary view of an important constructbenefits. In the extant literature, the constructbenefits -has been widely regarded as a single-dimensional construct. We propose that a multi-dimensional approach to measuring benefits will facilitate the respective government department to reach out to all the target audience with significant relative advantage. In order to identify the dimensions of benefits, we use the theory of Maslow's hierarchy of needs. Accordingly, we propose five dimensions of benefits which would cater to different kinds of individuals among the target users.

Introduction

In the domain of e-Government, impact assessment has been an important area of concern for academicians and practitioners alike [START_REF] Madon | Evaluating the developmental impact of e-governance initiatives: An exploratory framework[END_REF]; Bhatnagar & Singh 2010; De 2006; Lin, Fofanah & Liang 2011). [START_REF] Heeks | Success and Failure in eGovernment Projects[END_REF] advocates a five-step approach to assess an e-Government project -identify the stakeholders, identify each stakeholder's goals for the eGovernment project, measure the achievement of each goal, identify other impacts and finally classify the project outcome. The accuracy of each of the subsequent step is dependent on the preciseness of the previous step. The first step is relatively simple. In a typical e-Government project, leadership (political and bureaucracy), government employees, citizens, technology partners, vendors, businesses, and NGOs are some of the key stakeholders. The next step is challenging. For instance, not all citizens will have the same expectations from an e-Government project. Different members of the project leadership would also have different goals from the project. Each member of this group may value one or more stated objective of the project. The challenge is to identify the goals for each group such that the goals of most of the members are reflected in the final list. In this paper, we attempt to create one such list of goals for one of the most important stakeholders -the citizens.

Most of the theories related to adoption/diffusion of information systems deal with expected benefits as a unified construct. In the Delone and McLean success model, the construct -net benefits follow the use of the system. Similar to the expected benefits in adoption literature, Delone's model also treats net benefits as a unified single construct. In this paper, we attempt to extend these theories by identifying dimensions related to expected benefits. We believe that the dimensions of net benefits will also be the same as that of expected benefits. Citizens would seek a variety of benefits from a typical e-Government project. Savings in terms of time, cost, the effort could be a few most commonly desired benefits. However, not all the citizens would be satisfied with mere savings along these three dimensions. It is no surprise that when a group of citizens would be asked to evaluate benefits from an e-Government project on a scale of 1 to 5, some of them would choose each of these categories on the Likert scale. In this paper, we attempt to explain this variance with the help of Maslow's hierarchy of needs [START_REF] Maslow | A theory of human motivation[END_REF]). We argue that citizens seek benefits from an e-Government project along each of the dimension of Maslow's hierarchy viz. physiological, safety, love/belonging, esteem and self-actualization. This is a theoretical paper based on conceptual analysis as defined by [START_REF] Järvinen | The new classification of research approaches[END_REF]. Future work will involve testing the hypothesis developed in this paper. The paper is structured as follows. We discuss the key theories on the adoption of information systems. These theories also form the basis of e-Government adoption literature.

Based on this discussion, we highlight the gap in the extant literature. This forms the basis of our next section -the research question. It is followed by a brief discussion of the research method. It is followed by conceptual analysis and conclusion.

Literature Review

In the domain of Information Systems, a popular research theme has been centred around the question of acceptance and adoption of digital systems and their continued use. In this context, several theories have been popular among researchers. Some of these theories have also borrowed from each other. In the following section, we briefly discuss five such theories.

According to the theory of reasoned action (TRA), individual behaviour in terms of use of an information system is determined by the behavioural intentions towards such a system. The behavioural intention is a function of an individual's attitude toward the behaviour and the prevalent subjective norms related to the same [START_REF] Fishbein | Belief, attitude, intention, and behavior: An introduction to theory and research[END_REF]). Among other factors that would influence the attitude towards the behaviour, expected benefits perceived by individuals would have a significant impact. However, this theory does not explicitly talk about expected benefits to the us-ers. Theory of planned behaviour (TPB) is an extension of TRA. TPB adds a new construct -Perceived Behavioural Control -to the list of independent variables [START_REF] Ajzen | The theory of planned behavior[END_REF]). In the context of the information system, perceived behavioural control captures the individual's perception of the ease with which the new system can be used.

To some extent, this would also capture the expected benefits from the new system. Similar to TRA, this theory also does not explicitly talks about expected benefits from the new information systems or from performing a particular behaviour. Technology acceptance model (TAM) is an adaptation of TRA to the field of Information Systems. In this model, the two independent variables are replaced by perceived usefulness and perceived ease of use [START_REF] Davis | Perceived usefulness, perceived ease of use, and user acceptance of information technology[END_REF]. Both these variables reflect some aspects of benefits to the end-users. This theory deals with all different kinds of benefits at an aggregate level. Variability of perception of expected benefits by the end-users has not been explored in this theory. We highlight this as one of the key gaps in the extant literature. The next evolution in this domain was proposed by [START_REF] Venkatesh | User Acceptance of Information Technology: Toward a Unified View[END_REF]. This was an attempt to consolidate eight existing theories viz., the theory of reasoned action, technology acceptance model, motivational model, the theory of planned behaviour, a combined theory of planned behaviour/technology acceptance model, the model of PC utilization, innovation diffusion theory, and social cognitive theory. The consolidated model was termed as the unified theory of acceptance and use of technology (UTAUT). In this theory, the dependent variable remains same as TAM, but there is now a revised list of independent variables -performance expectancy, effort expectancy, social influence, and facilitating conditions -and four new moderating variables. In UTAUT, expected benefits are captured by performance expectancy and effort expectancy. In this paper, we argue that these two variables miss out on several possible dimensions of benefits from an e-Government project. The theory highlights that benefits are accrued from the use of information systems. Use follows the intention to use, which in turn is impacted by the quality triad. An end-user may expect certain benefits from each of these dimensions of the quality triad of Delone and McLean. For instance, better usability, extra availability, added reliability, more adaptability and superior response time are some of the potential expected benefits from an information system with greater system quality. Similarly, personalized content, complete information at one place, more crisp and relevant information, easy to understand data, and information security are some of the expected benefits from an information system with high information quality. Likewise, service quality would proxy the expected benefits related to the role of intermediaries, especially those dealing with the technical aspects of the solution. In this paper, we further explore these possible dimensions of the construct -net benefits -in the context of an e-Government project.

Most of the research that follows the above set of theories (like Prybutok et al. (2008); Bertot et al. ( 2008)) considers net benefits as a single unified construct. Consequently, they fail to explain the variance in citizens' perception of an e-Government project. Some scholars have used alternative approaches to capture and classify benefits from e-Government projects. These approaches are equally divergent. In the following section, we attempt to demonstrate some such variations in capturing benefits from e-Government project. Guo (2010) identifies four aspects of benefits from e-Government projects viz., 'democratization', 'environmental bonuses', 'speed, efficiency, and convenience' and public approval. Gilbert et al. ( 2004) have captured 'avoid personal interaction', 'control', 'convenience', 'cost', 'personalization' and 'time' as the major benefits from the e-Government projects. If we compare the above two approaches, only one-dimension overlaps, i.e. speed, efficiency, and convenience.

Research Question

As discussed in the previous section, the dominant part of e-Government literature considers benefits as a single construct. Few researchers who have taken a more micro perspective at the benefits have introduced more dimension to the construct of benefits, but such efforts are far from standardization and lack theoretical grounding. Accordingly, we formulate the following research question. Identify the key dimensions of benefits in the context of e-Government projects in India.

Research Method

The key research method used in this paper is conceptual analysis as defined by Jarvinen (1997). According to this conceptual-analytical approach, earlier works in the form of constructs, models, theories are analyzed considering newer realities. Generalizations of earlier empirical studies are theorized in light of new assumptions of a certain part of reality. Similar work has been done by [START_REF] Ives | A Framework for Research in Computer-Based Management Information Systems[END_REF] to derive a comprehensive MIS research model from a review of extant literature. In this paper, we identify possible dimensions of benefits from the extant literature and further classify them based on the theory of Maslow's hierarchy of needs.

Conceptual Analysis

In this paper, we argue that the use of Maslow's hierarchy of needs would facilitate understanding the variance in citizens' perceived benefits from an e-Government project. It would also help in unifying various approaches that were adopted to capture and classify net benefits.

According to Maslow's Hierarchy of Needs, individuals have five different levels of needs -physiological, safety, love/belonging, esteem, and self-actualization [START_REF] Maslow | A theory of human motivation[END_REF]. We argue that the benefits from an e-Government project should correspond to each of these levels. Every individual has a varying degree of these five needs. Ac-cordingly, he/she would seek different kinds of benefits from an e-Government project. We use extant literature to classify the benefits into each of these five levels. Physiological needs in the context of e-Government could mean convenience and cost. Convenience is a perceived attribute (Chan, Thong, Venkatesh, Brown, Hu & Tam 2010). An end-user could perceive convenience in the use of an e-Government project because of ease of use [START_REF] Berry | Understanding service convenience[END_REF] as well as ease of access (Meuter, Ostrom, Roundtree & Bitner 2000). Several scholars have highlighted that convenience plays an important role in helping citizens choose the digital channel over the traditional ones (Torkzadeh & Dhillon 2002; Everett & Peirce 1993). Convenience would also mean some savings in the time required to avail the service. Cost savings through the use of e-Government would also be part of physiological benefits from such projects. Such savings would not only include the direct cost of application and intermediary fees but would also include a reduction in the time cost of availing the service. Ashaye and Irani (2014) have identified improvement in the quality of service delivery as one of the key e-Government implementation benefits. Improvement of quality of service delivery may include service guarantee and service traceability. These benefits can be mapped as safety needs. Safety needs related benefits would also include safety of transactions, privacy controls and scope of grievance redressal with respect to the above concerns. The third level in Maslow's hierarchy of needs is love/belonging. Such need of an individual can be satisfied by facilitating connectedness. In this context, connectedness would mean allowing end-users to interact amongst themselves beyond the e-Government transactions. Benefits under this category should also involve facilitating provision for avoiding personal interactions (Chan, Thong, Venkatesh, Brown, Hu & Tam 2010) especially with government employees (applicable for interactions which are often less pleasant).

Esteem, the fourth level of the hierarchy of needs include the desire for achievement, for adequacy, for confidence, for independence, and for prestige. Citizens who are early adopters of e-Government systems may achieve a sense of achievement, confidence and prestige [START_REF] Rogers | Diffusion of innovations[END_REF]). Some such users also become motivators and guides for the late adopters. Thus, the use of e-Government may improve the image of the end-user in the social network. E-Government features like personalization and 24X7 access will give users a sense of control and hence will help them fulfil their need for esteem.

Finally, the last level of needs -self-actualization -could be fulfilled by doing what one is capable of doing. One such aspiration is to contribute to the overall development of the society (at times the nation). The perception of contributing to the reduction in overall corruption in the society, to increase the transparency and fairness in the system, and to develop overall trust among the stakeholders may help in achieving the self-actualization needs of citizens. Thus, we argue that benefits from an e-Government system can be classified along the five dimensions emerging from Maslow's Hierarchy of Needs. Since every individual can seek different degrees of each of these five types of needs, e-Government systems should be designed such that real benefits to the citizens are realized along all these five dimensions. Such a system design would also reduce the variability in citizens' perceived net benefits. The above discussion has been captured in figure 1.

Thus, five propositions may be developed from the above argument.

1. Physiological needs related benefit would impact the use of the e-Government application. 2. Safety needs related benefit would impact the use of the e-Government application. 3. Belonging needs related benefit would impact the use of the e-Government application. 4. Esteem needs related benefit would impact the use of the e-Government application. 5. Self-actualization needs related benefit would impact the use of the e-Government application.

Conclusion

Most of the e-Government projects in India are focused on providing benefits along the first two dimensions (Berry, Seiders & Grewal 2002; Meuter, Ostrom, Roundtree & Bitner 2000). A few projects also provide benefits along the other three dimensions. However, benefits provided along the last three dimensions are more incidental than planned. We argue that an e-Government project should be designed such that it provides significant benefits along all the five dimensions. The management mechanisms and the benefits listed under each of these dimensions are not exhaustive, but the dimensions themselves are exhaustive. Hence, executives and designers may focus on the five key dimensions while they plan for potential benefits to getting embedded in the new system. Two key contributions of this paper include first -identifying dimensions of net benefits in the context of the adoption of e-Government projects (DeLone & McLean 2003) and secondextending the theory of Maslow's hierarchy of needs [START_REF] Maslow | A theory of human motivation[END_REF] in the context of benefits derived from information systems project. The proposed framework is theoretical in nature. We intent to test the proposed model in our future work.

  [START_REF] Delone | Information Systems Success: The Quest for the Dependent Variable[END_REF] proposed information systems success model first in 1992 and later updated it in 2002-03. Updated Information Systems Success Model (DeLone & McLean 2002; DeLone & McLean 2003) has three independent variables -information quality, system quality and service quality -also termed as the quality triad.


Pushpa Raj
email: pushpapush@gmail.com

Dr B Shyamala
email: shyamalb@srmist.edu.in

Investor's Perception Towards Mutual Fund Investing On The Rise Of Digitalization In Indian Mutual Fund Industry

Keywords: Mutual Funds in India, Digital Payments, Digital Footprint, Importance of Mutual Funds, Asset Management, Robo Advisors

Technological advancement has been playing an essential role in the development of any sector. Even mutual fund industries are more exposed to the advent in the field of financial technology. Advancement in the FinTech arena has been witnessed in every aspect of the mutual fund industry. Asset Management Companies (AMC's) are started using robotics to increase their efficiency of fund management and ease their transaction processing or customer servicing. Investors can access the information online, track or redeem their investment any time of the day. Unified Payments Interface (UPI) and Electronic Clearing Services (ECS) are the online modes of payment facilities adopted by most fund houses. Association of Mutual Fund India (AMFI) has been consistently deploying considerable efforts to ensure that every investor has sufficient information to make informed decisions. Even the Know Your Customers (KYC) process has been completed online and more superficial than earlier days. In this paper, an effort has been made to understand the importance of Mutual Fund investments, the awareness among different investors about the mutual fund, the impact of Digitalization on mutual fund investment, and the information availability to every investor related to the mutual funds.

Introduction:

The Mutual Fund industry in India is undergoing tremendous growth in recent years. With the adoption of newer technologies, Asset Management Companies (AMCs) have managed to reach every corner of the nation to reach millions of investors. Digitalization has created more transparency, which has gained investors' confidence level and made them invest more confidently than before. Each investor has the freedom to get the information online and start investing online by completing a simple online eKYC process. Investors can purchase mutual funds with the click of a button through their mobile application by making electronic payments linked to their accounts. Paperless transactions are increasing, making life easier for the Investment Advisors to concentrate on the returns rather than indulging in any paperwork.

Soon, the Financial Advisors will be replaced by Robo-Advisors with access to the affluence of information online to perform the prediction based on the investors' risk appetite. Nowadays, the use of online calculators for calculating the returns yielded by any specific mutual funds can be computed efficiently. These calculators are available free of cost on every fund house's websites to make investors make confident decisions on their investments according to their future needs. The flow of information between Financial Advisors to their investors about the purchase of redemption decisions has been made easier with the technology's advent. Also, the communication between AMCs to their distributors regarding the new set of policies or product launches has been relatively more straightforward in recent days. Mutual Funds are gaining popularity among investors because of their ability to generate higher returns in the long term than any other investment avenues. Mutual Funds can yield greater returns when compared to your savings bank account or your bank fixed deposits during longer tenure. Mutual Funds can be generating higher returns based on the compounding interest. Also, the investment approach based on SIP can be providing you the advantage of rupee cost averaging, which is even more powerful. Mutual Funds are also considered as one of the less risky investment options as it utilizes the power of diversification.

Literature Review:

A most recent study undertaken by Kishore Kumar Das and Shahnawaz Ali during 2020 confirmed that the adoption of Digitalization in the mutual fund industry had shown a positive trend in the increased participation of retail investors. Distribution channels are making use of this financial advancement to make their work more efficient and investor-friendly. A study conducted by Sujit G Metre and Pranay Prashar during Jan 2019 has concluded that the availability of information and knowledge about mutual funds because of Digitalization has no impact on the investor's perception of online MF investment. An investigation performed by Dikkatwar Ramakrishna, De Tonmay, and Satya Prasad T K during Dec 2019 has concluded that digital interventions are mostly targeted towards distributors and large investors like corporate and other HNIs. Another research study conducted by Prasada Rao and Manda V K. during 2018, the examination revealed that Blockchain technology would help every stakeholder of the mutual fund industry. It can also provide the advantage of transparency, accountability, privacy, and decentralization. These transparencies could help in increasing the confidence level among the different investors because of this Digitalization.

Need for the Study:

Digitalization has provided an excellent platform for the Financial Services industry to operate more efficiently. This has also paved the way for the distributors' increased capacity to reach more customers without much paperwork or involving any physical transactions. Though we are already witnessing much of the digital impact on the FinTech application, much has been left over to explore shortly. With these advancements in place, are the investors taking advantage of these avenues to decide about their mutual fund investment? The perception of investors towards mutual fund investment, their knowledge level about the risk & returns relevant to their assets is still challenging for many to identify or interpret. In this study, we have attempted to understand the importance of investing in mutual funds alongside to study awareness among the investors on risk & return tradeoff based on their investments. We have also analyzed the impact of Digitalization on investors' perceptions of mutual fund investment and even their understanding of the associated fund manager.

Objectives of this Study:

The objective of this study is 1. To analyze the importance of mutual fund investment.

2. To understand the investor's awareness about Risk and Returns in mutual fund investment.

3. To identify the impact of Digitalization on Mutual Fund investments 4. To analyze the information available about Mutual Funds amongst investors

Sampling Information:

The research has been conducted with the help of Primary data. Primary data has been collected using a structured questionnaire. Statistical tools and graphical methods have been employed for data analysis. Analysis has been performed under different broad categories, as follows: They believe that the returns yielded my mutual funds are superior, and hence they wanted to continue their investments into mutual funds. It has also been observed that close to 58% of respondents consider mutual fund the safest investment option. Only 15% of respondents either disagree or strongly disagree with this question. Around 48% of the respondents considered mutual funds as the only investment option. The rest of them have diversified its investment across different available options. It has also been observed that 42% of respondents considered mutual fund investment as their income's tax saving objective. While remaining people are invested primarily for generating capital gain on their invested amount. More than 53% of respondents strongly agree that mutual fund investments are essential for long term investments. Hence we accept the hypothesis and conclude that Mutual Fund investments are a crucial long-term investment option. Risks involved and the corresponding returns associated with the invested amount should be clearly communicated to the investors. This information is considered to be one of the essential things while making decisions about any investment. From Fig. 2 below, it is observed that only 36% of respondents are aware of the risk associated with mutual investments.

Investor's awareness about Risk & Returns in Mutual

Fig. 2: Investors awareness about Risk & Returns

The remaining 43% of the respondents disagree, and they were still unclear about the risks associated with mutual fund investments. But on the contrary, 52% of respondents answered that they were well informed about the returns they would get at the time of maturity. Only 23% of the respondents were not very clear about the returns they will get at the time of maturity. Also, more than 50% of respondents informed us that they clearly understand the mutual fund they have invested in their money. Around 30% of the respondents were not clear about the investments they made in mutual funds. It is astonishing to see that considerably more people have invested their money without clearly understanding the scheme that they were investing their money. More than 50% of respondents Strongly Agree and Agree as they have the risk and return information about their investment in the mutual fund. Hence we accept the hypothesis and conclude that Investors are aware of the Risk and Return information of the mutual fund investment.

Impact of Digitalization on Mutual Fund investments:

Null Hypothesis (H0): Mutual fund investments have grown due to Digitalization.

Alternative Hypothesis (H1): Mutual fund investments have NOT grown due to Digitalization.

Recently Digitalization has been introduced in every financial platform. Also, we are seeing more people are interested in online payments and online investments. This is because of their convenience and to save the efforts involved in other modes of physical transactions. From Fig. 3 below, it is noticed that more than 63% of respondents consider digital payments to have more comfortable, and even they considered that their money is safe while making digital transactions. Only 22% of respondents think that digital transactions are not the safest option for them. More respondents (60%) communicated that digital transactions are faster than their physical transactions used before. It is also interesting to note that around 69% of respondents have made fresh investments into mutual fund post digitalization. Even the same amount of respondents considered that their queries related to any investments are clarified at the earliest with introducing the digital medium. Only 22% of the respondents are still not comfortable with digital transactions and the online mode of query resolutions. More than 65% of respondents Strongly Agree and Agree that mutual fund investments are increased post digitalization. Hence we accept the hypothesis and conclude that Mutual Fund investments are grown due to Digitalization. Alternative Hypothesis (H1): All the investment information is available to the mutual fund investors.

As digital technology is emerging, most people tend to get information online. They used to research online to get the information they wanted before making any decisions. With this advancement, we requested respondents about the information they have about their mutual fund investments. In Fig. 4 below, it is surprising to see that only a limited number of respondents, 42%, have informed that they believe that transparency in the information they get from mutual fund investments. Around 38% of respondents believe that there is no transparency in their knowledge about mutual fund investment. Also, 40% of the respondents did not trust AMC on the initial period's marketing information. Another vital thing to be noticed here is more than 68% of the respondents are not aware of their fund manager who manages their mutual fund investment. With the advancement in the field of digital technology, still, most of the investors are not aware of their fund managers. Even 59% of the respondents didn't trust the skills of their fund manager. They believe that fund performance is based on the market volatility and not based on their fund manager's skills. Only 21% of the respondents believe that the returns generated by their fund are because of the skills of their fund manager. More than 54% of respondents Strongly Agree and Agree as the mutual fund manager or their skills are unknown. Hence we accept the hypothesis and conclude that all the investment information is NOT available to the mutual fund investors.

Conclusion:

Based on this study, we observed that most investors are long-term investors in mutual funds. They firmly believe that Mutual Funds investments are yielding greater returns when compared to other investment avenues. They also consider mutual funds as one of the safest investment options which carry a lesser risk. Though we observe that few of the investors are not aware of the risks associated with the investment they make into mutual funds, most of the investors seem to be having the information related to their associated investment. The introduction of technological improvements in the mutual fund industry has paid significantly, making more investors drift towards mutual fund investments by making digital payments. The paperless eKYC process and the purchase, redemption, and other online activities, make investors' lives more manageable in their home comfort. With Digitalization taking deeper inroads, investors are still not aware of their Fund Manager information who manages their mutual fund. Also, the investors are not entirely in the belief that the returns generated from their investments are not associated with the fund manager's skills. Though the information's about every scheme of mutual funds is available online, the distributors and the fund houses should powerfully communicate the importance of the Fund Managers and their skills in handling the crises. We can gain the investors' confidence level to a greater extent, which in turn can result in increased participation of the investors.

6. Data Analysis and Inferences: 6 . 1

 61 Importance of Mutual Fund investments: Null Hypothesis (H0): Mutual Funds are considered as the safest long term investment option. Alternative Hypothesis (H1): Mutual Funds are NOT considered as the safest long term investment option.

Fig. 1 :

 1 Fig. 1: Importance of Mutual FundsMutual fund investments are considered as one of the safest investment options for long term investors. Most of the investors are parking their amount in mutual funds as long term investments. Based on the Fig.1below, it is evident that close to 58% of respondents are long term investors in mutual funds.

  Investors are aware of the Risk and Returns in mutual fund investment. Alternative Hypothesis (H1): Investors are NOT aware of the Risk and Returns in mutual fund investment.

Fig. 3 :

 3 Fig. 3: Impact of Digitalization on Mutual Funds 6.4 Information availability about Mutual Fund amongst investors: Null Hypothesis (H0): All the investment information is NOT available to the mutual fund investors.

Fig. 4 :

 4 Fig. 4: Information availability of Mutual Funds

  Data is collected by distributing this questionnaire to relevant participants and getting this filled using online methods. Questions covered under this questionnaire have been based on Likert scale measurement. Likert scale is balanced on both sides of the neutral option. We used the Likert scale as the measurement unit, as this is one of the reliable techniques for understanding users' attitude or opinion. Respondents are selected based on their investing patterns. The researcher has collected the data from 144 respondents those who are already invested in mutual funds for the period of short to long term.

	4. Information availability about Mutual Fund amongst investors
	1. Importance of Mutual Fund investments
	2. Investors awareness about Risk & Returns in Mutual Fund investments
	3. Impact of Digitalization on Mutual Fund investments