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The purpose of this study was to observe the people’s response regarding rooftop farming in one of the rapidly developing area of Kavrepalanchok district, Dhulikhel, as rooftop farming is aimed in solving food security problem in urban area by providing quality materials for nutritional requirements.

The research design of this study was random sampling survey with replacement techniques as respondents without concrete roof were not selected for the study. This study was aimed at recording the people’s response in one of the most accessible way, which would be easy for interpretation and analysis.

The major finding was that all of the respondents found rooftop farming beneficial but not all could practice it because of many constraints associated with rooftop farming. Most of them have fear of roof damage, so they are not adopting it. However, the respondents who are practicing rooftop farming find it difficult to manage because of lack of proper knowledge. Planting materials include plastic bags, crates, polythene and many other non-recyclable components.

To the best of the authors’ knowledge, this research is the first ever conducted in their country. Surveys related to rooftop gardening have not been done in the authors’ country till date. This is one of the present needs to improve the urban farming status, thus survey on rooftop farming and solving its constraints is necessary.

This study/paper aims to evaluate the floral richness of the central part of Chattogram city, Bangladesh. Chattogram is recognized as the largest port city and the commercial capital of Bangladesh, which confronts faster urbanization and swift infrastructure development. Green spaces in and around Chattogram city are shrinking sharply, which resulted in rapid loss of floral and faunal resources in this area. The present study was carried out from February 2018 to January 2019 to enumerate the vascular plant species of the Sulakbahar ward located in the central part of Chattogram City, Bangladesh.

The study area was categorized into 10 habitats to assess the variation of floral composition. The extensive whole area survey method was applied to record the flora from all sorts of plant habitats of the research area.

The study enumerated 418 vascular plant species under 315 genera and 120 families including natural, planted and cultivated from the study area. The habit form of the recorded plant composition indicated that herbs (35%) constitute the major plant category followed by trees (34%), shrubs (17%), climbers (12%), ferns (1%) and orchids (1%). The study also indicated that exotic species (50.3%) became dominant than native species (49.7%) in Chattogram city because of their scenic beauty, easy propagation and ornamental value to the city planners and inhabitants.

It appeared that floral resources of the Chattogram city area are in great threat due to aggressive and unplanned infrastructure development for housing, offices and institutions by replacing the green spaces. The study recommended that urgent protection measures should be taken to conserve and protect the existing floral resources for the well-being of the urban people.

The purpose of this paper is to extend the knowledge on business model transformation (BMT) by developing an integrative framework for BMT dilemmas, including strategies for shaping and stabilizing market structures.

The study uses a case-based approach, with data from the Swedish electric utility industry.

The findings uncover practices related to both shaping and stabilizing market structure. The study contributes with insights for firms to overcome the BMT dilemma. Shaping strategies involve disruptive innovations while stabilizing strategies concerns incremental improvements in existing structures; by balancing these efforts, firms can find ways toward successful BMT.

With a focus on incumbent firms and the balancing act of BMT in a network, the study covers areas that have scarcely been addressed in the existing literature. Even though most business model literature has focused on shaping consumer markets, the need to consider BMT as a dual-directional process in an industrial context is emphasized in this study.

This paper investigates how manufacturers can develop a learning-to-learn capability for enabling Industry 4.0 adoption.

This research design is guided by our research question: How can manufacturers develop a learning-to-learn capability that enables Industry 4.0 adoption? The authors adopt action research to generate actionable knowledge from a two-year-long action learning intervention at the Danish rooftop window manufacturer VELUX.

Drawing on emergent insights from the action learning intervention, it was found that a learning-to-learn capability based on lean was a core construct and enabler for manufacturers to adopt Industry 4.0 successfully. Institutionalizing an organizational learning scaffold encompassing the intertwined learning processes of systems Alpha, Beta and Gamma served as a significant way to develop a learning-to-learn capability for Industry 4.0 adoption (systematic problem-solving abilities, leaders as learning facilitators, presence of a supportive learning environment and Industry 4.0 knowledge). Moreover, group coaching is a practical action learning intervention for invoking system Gamma and developing leaders to become learning facilitators – an essential leadership role during Industry 4.0 adoption.

The study contributes to theory and practice by adopting action research and action learning to explore learning-to-learn as a core construct for enabling Industry 4.0 adoption and providing a set of conditions for developing a learning-to-learn capability. Furthermore, the study reveals that leaders are required to act as learning facilitators instead of relying on learning about and implementing Industry 4.0 best practices for enabling adoption.

The Gulf Cooperation Council (GCC) of countries has some of the highest electricity consumptions and carbon dioxide emissions per capita in the world. This poses a direct challenge to the GCC government’s ability to meet their CO₂ reduction targets. In this review paper the current household electricity consumption situation in the GCC is reviewed.

Three scenarios for reducing energy consumption and CO₂ emissions are proposed and evaluated using strengths, weaknesses, opportunities and threats (SWOT) as well as the political, economic, social, technical, legal and environmental (PESTLE) frameworks.

The first scenario found that using solar Photovoltaic (PV) or hybrid solar PV and wind system to power household lighting could save significant amounts of energy, based on lighting making up between 8% to 30% of electricity consumption in GCC households. The second scenario considers replacement of conventional appliances with energy-efficient ones that use around 20% less energy. The third scenario looks at influencing consumer behavior towards sustainable energy consumption.

Pilot trials of these scenarios are recommended for a number of households. Then the results and feedback could be used to launch the schemes GCC-wide.

The proposed scenarios are designed to encourage responsible electricity consumption and production within households (SDG12).

All three proposals are found viable for policymakers to implement. However, to ensure successful implementation GCC Governments are recommended to review all the opportunities and challenges associated with these schemes as laid out in this paper.

This research proposes to organise and distil this massive amount of data, making it easier to understand. Using data mining, machine learning techniques and visual approaches, researchers and managers can extract valuable insights (on guests' preferences) and convert them into strategic thinking based on exploration and predictive analysis. Consequently, this research aims to assist hotel managers in making informed decisions, thus improving the overall guest experience and increasing competitiveness.

This research employs natural language processing techniques, data visualisation proposals and machine learning methodologies to analyse unstructured guest service experience content. In particular, this research (1) applies data mining to evaluate the role and significance of critical terms and semantic structures in hotel assessments; (2) identifies salient tokens to depict guests' narratives based on term frequency and the information quantity they convey; and (3) tackles the challenge of managing extensive document repositories through automated identification of latent topics in reviews by using machine learning methods for semantic grouping and pattern visualisation.

This study’s findings (1) aim to identify critical features and topics that guests highlight during their hotel stays, (2) visually explore the relationships between these features and differences among diverse types of travellers through online hotel reviews and (3) determine predictive power. Their implications are crucial for the hospitality domain, as they provide real-time insights into guests' perceptions and business performance and are essential for making informed decisions and staying competitive.

This research seeks to minimise the cognitive processing costs of the enormous amount of content published by the user through a better organisation of hotel service reviews and their visualisation. Likewise, this research aims to propose a methodology and method available to tourism organisations to obtain truly useable knowledge in the design of the hotel offer and its value propositions.