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The public distribution system (PDS) is an Indian food security system established to manage the distribution of food grains at affordable prices. As a result of the population explosion, the long-established PDS system finds it challenging to maintain operational efficiency, quality, trust and transparency. This paper explores the possibility of leveraging blockchain technology to overcome these operational hurdles.

Through a literature review and expert interactions, the present research identifies critical success factors in terms of enablers and barriers that influence the adoption of blockchain technology in PDS. Furthermore, we propose two independent interpretive structural models (ISM) and MICMAC to characterize these attributes.

The research identifies 15 distinct enablers and ten barriers that influence the diffusion of the latest technology in the sector at focus. The analyses disclose the interrelationships/dependencies among these enablers and between barriers, along with their individual driving power and dependence power.

The research showcases the importance of automating the system and illustrates how the features of blockchain technology can assist in augmenting stakeholder satisfaction levels. However, poor or nonexistent government regulations and patronage are found to be the major impediments to adoption. The research also delineates the cost implications of this barrier through its interrelationships with other barriers.

Interesting inferences are drawn from the models that offer actionable insights for the industry, government and technologists for improving PDS performance. Such interventions will ensure national food security through enhanced trust and transparency, which can further improve efficiency and effectiveness.

This study aims to determine the effects of lodging infrastructure development (LID) on Cordillera Administrative Region’s (CAR) environmental quality and natural resource management and its implication to globally responsible leadership. Specifically, this study sought to determine the contribution of LID to environmental deterioration and natural resource degradation in the CAR. As a result, a mathematical model is developed, which supports sustainability practices to maintain the environmental quality and natural resource management in CAR, Philippines.

This study used a descriptive research design using a mixed-methods approach. Self-structured interview and survey were used to gather the data. The population of this study involved three groups. There were 6.28% (34) experts in the field for the qualitative data, 70.24% (380) respondents for the quantitative data and 23.47% (127) from the lodging establishments. 120 respondents from the Department of Tourism – CAR (DOT-CAR) accredited hotels. Nonparametric and nonlinear regression analysis was used to process the data.

The effects of LID on the environmental quality and natural resource management in CAR as measured through carbon emission from liquefied petroleum gas (LPG), electricity and water consumption in the occupied guest rooms revealed a direct correlation between the LID. Findings conclude that the increase in tourist arrival is a trigger factor in the increase in LID in the CAR. The increase in LID implies a rise in carbon emission in the lodging infrastructure. Any increase in tourist arrivals increases lodging room occupancy; the increased lodging room occupancy contributes to carbon emissions. Thus, tourism trends contribute to the deterioration of the environmental quality and degradation of the natural resources in the CAR. A log-log model shows the percentage change in the average growth of tourist arrival and the percentage increase in carbon emissions. Establishments should observe standard room capacity to maintain the carbon emission of occupied lodging rooms at a minimum. Responsible leadership is a factor in the implementation of policy on standard room capacity.

The result of the study has some implications for the lodging businesses, the local government unit (LGU), the Department of Tourism (DOT) and the Department of Environment and Natural Resources (DENR) in the CAR. The study highlights the contribution of the lodging establishments to CO₂ emission, which can degrade the quality of the environment, and the implication of responsible leadership in managing natural resources in the CAR. The direct inverse relationship between energy use and CO₂ emission in hotels indicates that increased energy consumption leads to environmental degradation (Ahmad et al., 2018). Therefore, responsible leadership among policymakers in the lodging and government sectors – LGU, DOT and DENR – should abound in the CAR. Benchmarking on the model embarked from this study can help in designing and/or enhancing the policy on room capacity standardization, considering the total area with its maximum capacity to keep the carbon emission at a lower rate. Furthermore, as a responsible leader in the community, one should create programs that regulate the number of tourists visiting the place to decrease the number of overnight stays. Besides, having the political will to implement reduced room occupancy throughout the lodging establishments in CAR can help reduce the carbon emissions from the lodging businesses. After all, one of the aims of the International Environment Protection Organization is to reduce CO₂ emissions in the tourism industry. Hence, responsible leadership in environmental quality preservation and sustainable natural resource management must help prevent and avoid greenhouse gas (GHG) emissions.

Most studies about carbon emission in the environment tackle about carbon dioxide emitted by transportation and factories. This study adds to the insights on the existing information about the carbon emission in the environment from the lodging establishments through the use of LPG, electricity and water consumption in the occupied guest rooms. The findings of the study open an avenue for globally responsible leadership in sustaining environmental quality and preservation of natural resources by revisiting and amending the policies on the number of room occupancy, guidelines and standardization, considering the total lodging area with its maximum capacity to keep the carbon emission at a minimum, thus contributing to the lowering of GHG emissions from the lodging industry.

This study aims to identify an automated machine learning algorithm with high accuracy that sport practitioners can use to identify the specific factors for predicting Major League Baseball (MLB) attendance. Furthermore, by predicting spectators for each league (American League and National League) and division in MLB, the authors will identify the specific factors that increase accuracy, discuss them and provide implications for marketing strategies for academics and practitioners in sport.

This study used six years of daily MLB game data (2014–2019). All data were collected as predictors, such as game performance, weather and unemployment rate. Also, the attendance rate was obtained as an observation variable. The Random Forest, Lasso regression models and XGBoost were used to build the prediction model, and the analysis was conducted using Python 3.7.

The RMSE value was 0.14, and the R2 was 0.62 as a consequence of fine-tuning the tuning parameters of the XGBoost model, which had the best performance in forecasting the attendance rate. The most influential variables in the model are “Rank” of 0.247 and “Day of the week”, “Home team” and “Day/Night game” were shown as influential variables in order. The result was shown that the “Unemployment rate”, as a macroeconomic factor, has a value of 0.06 and weather factors were a total value of 0.147.

This research highlights unemployment rate as a determinant affecting MLB game attendance rates. Beyond contextual elements such as climate, the findings of this study underscore the significance of economic factors, particularly unemployment rates, necessitating further investigation into these factors to gain a more comprehensive understanding of game attendance.

The purpose of this study is to explore the strength and value-relevance of social capital in an entrepreneurial ecosystem. Entrepreneurial ecosystem (EE) provides a new perspective to explaining the configurations and interactions that shape entrepreneurial outcomes in regions. Research on the nature of interactions in EEs is still an ongoing debate. The authors draw from “organisational fields” studies to critically examine the interactions among actors in a non-transparent EE using the case of the Lagos region.

The methodology is based on a qualitative study of 40 semi-structured interviews with various ecosystem actors in the Lagos region, including financiers, government officials, universities, founders and venture capitalists. Additionally, data from the semi-structured interviews were triangulated with data obtained from a two-day focus group discussion Summit where Lagos’ EE issues were raised. This study analysed both data using thematic analysis.

This study suggests that in a non-transparent EE, four types of interactions are apparent: collaborative, stratified, clustered and unleveraged. Authors argue that in a non-transparent EE, there are blockages and distortions in the flow of resources to entrepreneurs and a higher proportion of entrepreneurs are unable to plug into the ecosystem to extract value for their businesses without a strong social capital.

The authors argue that entrepreneurs require deliberate effort to improve structural and relational social capital to plug into their ecosystem to extract value for their businesses.

The focus on interaction in a non-transparent EE is a novel approach to studying interactions within EEs. In addition, the study is an early attempt to explore entrepreneurial interactions within the Lagos region.