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The COVID-19 pandemic severely impacted tourism, and the hotel and restaurant industry was the most affected sector, which faced issues related to business uncertainty and unemployment during the crisis. The analysis of recovery time and the influence factors is significant to support policymakers in developing an effective response and mitigating the risks associated with the tourism crisis. This study aims to investigate numerous factors affecting the recovery time of the hotel and restaurant sector after the COVID-19 crisis by using survival analysis.

This study uses the quarterly value added with the observation time from quarter 1 in 2020 to quarter 1 in 2023 to measure the recovery status. The recovery time refers to the number of quarters needed for the hotel and restaurant sector to get value added equal to or exceed the value added before the crisis. This study applies survival models, including lognormal regression, Weibull regression, and Cox regression, to investigate the effect of numerous factors on the hazard ratio of recovery time of hotels and restaurants after the COVID-19 crisis. This model accommodates all cases, including “recovered” and “not recovered yet” areas.

The empirical findings represented that the Cox regression model stratified by the area type fit the data well. The priority tourism areas had a longer recovery time than the non-priority areas, but they had a higher probability of recovery from a crisis of the same magnitude. The size of the regional gross domestic product, decentralization funds, multiplier effect, recovery time of transportation, and recovery time of the service sector had a significant impact on the probability of recovery.

This study contributes to the literature by examining the recovery time of the hotel and restaurant sector across Indonesian provinces after the COVID-19 crisis. Employing survival analysis, this study identifies the pivotal factors affecting the probability of recovery. Moreover, this study stands as a pioneer in investigating the multiplier effect of the regional tourism and its impact on the speed of recovery.

The tourism multiplier effect (TME) is the total economic impact of tourism demand, representing the linkages between tourism and other businesses in an area. However, study about it is limited in Indonesia, especially at the provincial level and after the COVID-19 crisis. This study aims to estimate the TME in all provinces of Indonesia, test its differences in priority and non-priority areas before and after the COVID-19 crisis, analyze its spatial distribution and examine the determinant factor of TME

This study applies an input-output model to measure the TME of all provinces in Indonesia, an independent sample t-test to examine the similarity of TME in priority and nonpriority areas, a paired sample t-test to examine the similarity of it before and after the COVID-19 crisis, and spatial analysis to check its spatial relationship.

The result shows that regional TME ranges from 1.25 to 2.05 in 2019, which changed slightly over time. The empirical result shows the TME difference before and after the COVID-19 crisis, and there is a spatial correlation in terms of TME with the hot spots are clustered in the eastern region of Indonesia, However, there was a slight change in the position of hot spots during the COVID-19 crisis. Moreover, the spatial model shows that value-added and employment in agriculture, manufacturing, trade and transportation affect the size of TME.

This study contributes to the academic literature by providing the first estimate of the TME at the provincial level in Indonesia, comparing the it in priority and non-priority areas before and after the COVID-19 crisis, and mapping its spatial distribution.

Tourism is an essential industry in Indonesia, and understanding its inter-sectoral and inter-regional connections is critical for policy development. This study examines the economic impact of regional tourism in Indonesia and the connections between different tourism-related regions and industries.

This study uses a non-survey method to estimate the inter-regional input-output table (IRIOT) in 2019, backward and forward linkage to identify the role of tourism in the economy, and the structural path analysis (SPA) to identify the inter-sectoral and inter-regional flow of tourism effect. The benchmark IRIOT 2016 published by Badan Pusat Statistik (BPS) serves as the primary data source.

The findings indicate that tourism has a relatively high impact on the overall national economy and plays an essential role in nine provinces. However, this study uses four provinces to represent Indonesian tourism: Jakarta, Jawa Timur, Bali, and Kepulauan Riau. The SPA result captures that Kepulauan Riau Province has the highest tourism multiplier effect and Jawa Timur has the highest coverage value. Moreover, the manufacturing sector receives the most benefit from the tourism effect, followed by trade, construction, agriculture, transportation, and electricity-gas. From a spatial perspective, tourism connections are not solely based on geographical proximity. Instead, they are established through an intricate supply chain network of manufactured goods. This emphasizes the significance of considering supply chain dynamics when investigating inter-regional relationships in the tourism sector.

This research contributes to the literature by estimating the IRIOT in 2019, disaggregating tourism activities from related economic sectors, constructing tourism-extended IRIOT, and identifying the critical path of tourism effect in numerous provinces with different economic structures. This novel approach offers valuable insights into the full spectrum of tourism’s economic impact, which has not been previously explored in this depth. This study is useful for policymaking, investment insight, and disaster mitigation.

The learning outcomes of this study are as follows:

1. understand the role of financial inclusivity in the sustainable development of a nation;

2. examine the concept of social entrepreneurship and identify the skills needed to be a social entrepreneur;

3. analyze the opportunities and challenges faced by social entrepreneurs, especially in an emerging economy; and

4. assess the feasible options with respect to upscaling and expansion.

Yamuna Sastry, a young woman from a traditional Indian family, had set out to achieve her dream of financial inclusivity by helping the underprivileged in her country gain financial independence and credibility. When she was approached by a cab driver to file tax returns for him, a new venture took shape in her mind, and along with a partner, CabDost, a socially driven financial advisory start-up was created to provide financial advisory services exclusively for cab drivers. CabDost had been instrumental in making over 15,000 cab drivers financially literate, instilling in them a culture of compliance, getting them tax refunds and enabling the Indian Government recover eight crores in taxes. The success of financial inclusivity among cab drivers inspired CabDost to extend its financial services to truck drivers, auto drivers, housekeeping staff and other contractual workforce. The company found it challenging to address the demands of the increasing customer base with its available technical resources. The absence of an in-house tech team and the need for an all-in-one tech platform to provide a wide variety of financial services induced CabDost to explore other options. Dvara Money, a neo bank offering financial services, approached CabDost with a merger proposal. Though it was a lucrative offer, the founding members were apprehensive as they knew that most of the mergers failed because of myriad reasons. They were contemplating on their next move as they were in a dilemma about whether to develop a technical team in-house or to go ahead with the merger.

The case can be taught to business management students as a part of the introductory course on entrepreneurship or social entrepreneurship. The case can be used specifically to make the students understand the role of financial inclusivity in the sustainable development of a nation, the concept of social entrepreneurship, the journey of social entrepreneurs in the financial inclusivity space, right from ideation to execution, the challenges faced in the bargain, survival mechanisms adopted and the various options available for further growth and expansion.

Teaching notes are available for educators only.

CSS 3: Entrepreneurship.

This paper aims to study the inhibition effect of 2-pyridinealdazine on the corrosion of mild steel in an acidic medium. The inhibition effect was studied using weight loss, electrochemical impedance spectroscopy, and Tafel polarization measurements.

Weight loss measurements, potentiodynamic tests, electrochemical impedance spectroscopy, X-ray diffraction and spectral and conformational isomers analysis of A (E-PAA) and B (Z-PAA) were performed were investigated.

2-pyridinealdazine (PAA) acts as a good inhibitor for the corrosion of steel in 2.0 M H₃PO₄. The inhibition efficiency increases with an increase in inhibitor concentration but decreases with an increase in temperature.

This paper is intended to be added to the family of azines which are highly efficient inhibitors and can be used in the area of corrosion prevention and control.

A series of composite solders in 63/37 Sn/Pb was prepared: Cu₆ Sn₅ (10, 20, 30 wt%); Cu₃ Sn (10, 20, 30 wt%); Cu (7.6 wt%); Ag (4 wt%); and Ni (4 wt%). These composite solders were prepared by two procedures: (A) admixture with solder paste; and (B) admixture with molten solder. The original particulates and the final composite solders were analysed and characterised by SEM (scanning electron microscopy) EDX (energy dispersive X‐ray), and ESCA (electron spectroscopy for chemicals analysis); or XPS (X‐ray photoelectron spectroscopy). A variety of morphological characterisations, intermetallics and porosities were noted. Good wetting was noted in all cases, and porosity was greater for method (A). The particulates all exhibited excellent binding to the solder matrix.

Composite solders were prepared by mechanically dispersing 15v% of Cu or Ag particles into the eutectic Sn‐3.5Ag solder. The average sizes for the nominally spherical Cu and Ag particles were 6 and 4 microns, respectively. Two different processing methods were used to prepare the composite solders: blending the powdered particles with solder paste, and adding particles to the molten solder at 2808C. The composite solders were characterised by studying the morphology, size and distribution of the reinforcing phase. Particular interest and emphasis are given towards the modifications of the reinforcements during the reflow process. Microstructural features and chemical analysis of the composite solders were studied using optical and scanning electron microscopy (SEM), and energy dispersive x‐ray (EDX) analysis. The effect of reflow and isothermal ageing on the microstructure as well as the morphological changes in the interfacial IM layer of the composite solders were extensively analysed. A mechanism for IM layer growth is proposed for solid state isothermal ageing.

The integration of artificial intelligence (AI) in computer science education is transforming teaching methodologies, particularly through AI-assisted programming. This chapter highlights AI’s impact on programming education by providing personalised learning, immediate feedback, and using technologies like NLP, ML, and LLMs. It discusses the shift from traditional to AI-enhanced approaches, including competitive programming where AI automates tasks such as template generation, unit testing, and edge case analysis. The chapter also explores AI’s role in promoting self-regulated learning and enhancing classroom engagement with generative AI and virtual tutors. While noting benefits like increased accessibility and personalised instruction, it addresses ethical considerations and technical limitations. The chapter underscores the need for continuous innovation and collaboration in AI-assisted programming to equip students with modern technological skills.