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This paper is a Conference Report (ICDT-2019) organized by Rajiv Gandhi National University of Law, Patiala, Punjab, India

The whole conference is articulated in the form of a Conference Report.

The conference provided an opportunity for participants to share ideas, reach some consensus, agree on differences of opinion and create a future action agenda. All the participants from all different backgrounds were able to bring new things to light and deliberate on contemporary issues related to knowledge trends and digital strategies that will lead to smart future.

The International Conference on Digital Transformation-2019 on the theme of “A Cognitive learning towards Artificial Intelligence” was convened by Rajiv Gandhi National University of Law, Patiala, Punjab, India during September 6-8, 2019 in Patiala, Punjab, India.

During the 1960s the academic and large public libraries engaged in massive reclassification of their collections from Dewey Decimal to the Library of Congress Classification System. A search of the library literature of this decade indicates much publication activity on the topic of reclassification. While losing some momentum in the “trend to LC” during 1968‐1971 many libraries continued to reclassify into the 1970s.

Task conflict and relationship conflict are common in organizations. This paper aims to present a unique case of the use of the targeted conflict-resolution technique. The revival of positive group dynamics is aptly shown.

This descriptive case study is developed as a practice insight to showcase how a peculiar case of misunderstanding is resolved in the most unconventional way through the intervention of a mediator who unearths the real cause of contention. The mediator works through logic and emotion to remove negativity. Narration, a necessary component of the case study approach, peeps into the research subject involving flashbacks, flash forward, backstories and foreshadowing. The mediator uses reframing as a tool very efficiently, encouraging the people in conflict to understand the nothingness in their cold war and eventually prompting them to collaborate and compromise.

The shifts in communication dynamics post-mediator’s intervention are subtle and full of wisdom, encouraging introspection and constructive interaction, eventually bridging the differences. The possibility of achieving a state of homeostasis in the future magnifies. The belief in the power of affirmation and manifestation is validated. The heavy, difficult, hardened negativity loses ground and gets transformed.

Conversation/prayers at the deepest level in several meetings are the communication tools that have immense social relevance in the Indian context.

A unique combination of intermediation encompassing written communication and energy transformation is adopted to resolve ongoing conflict by stroking the positive psychology of the partakers. To some, the method may appear to have a spiritual connotation.

This study aims to examine which organisational and other factors can facilitate the adoption of artificial intelligence (AI) in Indian management institutes and their interrelationship.

To determine the factors influencing AI adoption, a synthesis-based examination of the literature was used. The interpretative structural modelling (ISM) method is used to determine the most effective factors among the identified ones and the inter-relationship among the factors, while the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is used to analyse the cause-and-effect relationships among the factors in a quantitative manner. The approaches used in the analysis aid in understanding the relationship among the factors affecting AI adoption in management institutes of India.

This study concludes that leadership support plays the most significant role in the adoption of AI in Indian management institutes. The results from the DEMATEL analysis also confirmed the findings from the ISM and Matrice d’ Impacts croises- multiplication applique and classment (MICMAC) analyses. Remarkably, no linkage factor (unstable one) was reported in the research. Leadership support, technological context, financial consideration, organizational context and human resource readiness are reported as independent factors.

This study provides a listing of the important factors affecting the adoption of AI in Indian management institutes with their structural relationships. The findings provide a deeper insight about AI adoption. The study's societal implications include the delivery of better outcomes by Indian management institutes.

According to the authors, this study is a one-of-a-kind effort that involves the synthesis of several validated models and frameworks and uncovers the key elements and their connections in the adoption of AI in Indian management institutes.

Significant advances in digital technologies impact both organisations and knowledge workers alike. Organisations are now able to effectively analyse significant amounts of data, while accomplishing actionable insight and data-driven decision-making through knowledge workers that understand and manage greater complexity. For decision-makers to be in a position where sufficient information and data-driven insights enable them to make informed decisions, they need to better understand fundamental constructs that lead to the understanding of deep knowledge and wisdom. In an attempt to guide organisations in such a process of understanding, this research study focuses on the design of an organisational transformation framework for data-driven decision-making (OTxDD) based on the collaboration of human and machine for knowledge work. The OTxDD framework was designed through a design science research approach and consists of 4 major enablers (data analytics, data management, data platform, data-driven organisation ethos) and 12 sub-enablers. The OTxDD framework was evaluated in a real-world scenario, where after, based on the evaluation feedback, the OTxDD framework was improved and an organisational measurement tool developed. By considering such an OTxDD framework and measurement tool, organisations will be able to create a clear transformation path to data-driven decision-making, while applying the insight from both knowledge workers and intelligent machines.

The purpose of this paper is to evaluate the interplay of various measures used by different governments around the world in combatting COVID-19.

The research uses the interpretative structural modelling (ISM) for assessing the powerful measures amongst the recognized ones, whereas to establish the cause-and-effect relations amongst the variables, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is used. Both approaches utilized in the study aid in the comprehension of the relationship amongst the assessed measures.

According to the ISM model, international support measures have the most important role in reducing the risk of COVID-19. There has also been a suggestion of a relationship between economic and risk measures. Surprisingly, no linkage factor (unstable one) was reported in the research. The study indicates social welfare measures, R&D measures, centralized power and decentralized governance measures and universal healthcare measures as independent factors. The DEMATEL analysis reveals that the net causes are social welfare measures, centralized power and decentralized government, universal health coverage measure and R&D measures, while the net effects are economic measures, green recovery measures, risk measures and international support measures.

The study includes a list of numerous government measures deployed throughout the world to mitigate the risk of COVID-19, as well as the structural links amongst the identified government measures. The Matrice d'Impacts croises-multiplication applique and classment analysis can help the policymakers in understanding measures used in combatting COVID-19 based on their driving and dependence power. These insights may assist them in employing these measures for mitigating the risks associated with COVID-19 or any other similar pandemic situation in the future.

The domain of study on mediated suffering is ensconced within an Orientalist paradigm which ideologically structures our visuality and gaze. The consignment of suffering through bodies of alterity and the geo-politics of the Global South encodes the coloniality of power as a dominant reading. It then naturalizes the West as the voyeur in its consumption of the abject bodies of the Global South. Creating a binary through this East-West polarization in the oeuvre of suffering as a realm of study, it creates the hegemony of the West as the moral guardian of suffering, imbuing it with the right to accord pity and compassion to the lesser Other. Beyond elongating the Orientalist trajectory which lodged the body politic of the Global South as a sustained ideological site of suffering, it hermeneutically seals the East as irredeemable, ordaining it through the gaze over the Other as a mode of coloniality. In countering this Eurocentric proposition, this chapter contends that this coloniality of gaze needs further rumination and new sensibilities in the study of mediated suffering, particularly following 9/11 and the shifting of the geo-politics of suffering in which the West is dispossessed through its own manufactured ideologies of the ‘War on Terror’ such that it is under constant threat of terrorist attacks and through the movement of the displaced Other into the Global North. Besieged and entrapped through its own pathologies of risks and threats, the West is projected through its own victimhood and the politics of the Anthropocene within which risks are seemingly democratized by environmental degradation as an overarching threat for all of humanity. Despite these shifts in the global politics, the scholarship of suffering is locked into this polarity. The chapter interrogates this innate crisis within this field of scholarship.

The present study aimed to explore the consequences of perceived authenticity in artificial intelligence (AI) assistants and develop a serial-mediation architecture specifying causation of loyalty in human–AI relationships. It intended to assess the predictive power of the developed model based on a training-holdout sample procedure. It further attempted to map and examine the predictors of loyalty, strengthening such relationship.

Partial least squares structural equation modeling (PLS-SEM) based on bootstrapping technique was employed to examine the higher-order effects pertaining to human–AI relational intricacies. The sample size of the study comprised of 412 AI assistant users belonging to millennial generation. PLS-Predict algorithm was used to assess the predictive power of the model, while importance-performance analysis was executed to assess the effectiveness of the predictor variables on a two-dimensional map.

A positive relationship was found between “Perceived Authenticity” and “Loyalty,” which was serially mediated by “Perceived-Quality” and “Animacy” in human–AI relational context. The construct “Loyalty” remained a significant predictor of “Emotional-Attachment” and “Word-of-Mouth.” The model possessed high predictive power. Mapping analysis delivered contradictory result, indicating “authenticity” as the most significant predictor of “loyalty,” but the least effective on performance dimension.

The findings of the study may assist marketers to understand the relevance of AI authenticity and examine the critical behavioral consequences underlying customer retention and extension strategies.

The study is pioneer to introduce a hybrid AI authenticity model and establish its predictive power in explaining the transactional and communal view of human reciprocation in human–AI relationship. It exclusively provided relative assessment of the predictors of loyalty on a two-dimensional map.

The purpose of this study is to examine the effect of individual components' reliability on a system's reliability. The system refers to the Financial Management Information System (FMIS), the US Federal Highway Administration's (FHWA) web‐based project approval and tracking software. Its components are 61 project information fields.

The analysis would view each highway project‐funding request as an activity with cycle‐dependent performance for which success probability can be calculated as Reliability, R. The reliability analysis of the 61 FMIS fields results in a series system with R_sys the “estimated reliability” of finding “true” values in all 61 information fields during one highway‐related project funding authorization review.

Of an estimated 200 projects approved, there was previously estimated a 50 percent to 80 percent unreliability rate, while the study found an unreliability rate of approximately 80 percent.

Owing to the nature of federal government software, data can be very difficult to acquire in this working environment, but a simple calculation was relatively successful in confirming the “estimated reliability” of finding “true” values and showing how the reliability could dramatically decrease.

The paper contributes to the applicability of reliability analysis to project approval software, showing the progression from estimated data to bounding the estimate using reliability theory.

This study aims to understand the challenges of 5G deployment in India from the perspectives of telecom operators. These challenges are also mapped to different contexts within the technological-organizational-environmental (TOE) framework.

The study uses a qualitative approach comprising in-depth semi-structured interviews of employees working in telecom companies in India. Thematic analysis is used to analyse the qualitative data.

The author has identified nine challenges that are categorized under three dimensions of the TOE framework. Specifically, the findings indicate three technological challenges: hardware/device challenges, security concerns and limited use cases; two organization challenges: financial challenges and lack of skilled workforce; and four environmental challenges: inadequate infrastructure, regulatory and administrative challenges, consumers’ attitudes and competitive market conditions.

The results of this study would help understand the key factors that can act as barriers to the 5G rollout in India. Based on the findings of the study, the government and regulatory bodies could design conducive policies and regulatory frameworks to successfully deploy 5G in India.

The study is one of the very few studies to empirically examine the telecom operators’ perspectives on the challenges of 5G deployment in India. The study contributes to the TOE framework as its application in the context of identifying barriers to 5G deployment is probably for the first time.