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To ensure that students are well prepared to successfully analyze this case, they should be familiar with the following concepts, theories and principles:

▪ Stakeholder theory

▪ Concept of duty to consult and accommodate

▪ Concept of social license to operate (SLO)

▪ Concept of indigenous economic self-determination

▪ Indigenous world view

▪ Seventh generation principle

▪ Cree principles

▪ Dene principles

The information provided in this case was gathered by the authors through face-to-face interviews, phone interviews, e-mail exchanges and secondary research. Meadow Lake Tribal Council (MLTC) separates business operations from council operations through use of corporate entities (as shown in Exhibit 1 of the case). Meadow Lake Tribal Council II (MLTCII) is the corporate entity that oversees two companies referenced in the case: Mistik Management and NorSask Forest Products LP. Interviews were conducted with the General Manager at Mistik Management, Certification Coordinator at Mistik Management, Chief of Buffalo River Dene Nation, Chief of Waterhen Lake First Nation, MLTC Vice-Chief, Board Member and Advisor to MLTCII, President and CEO of MLTCII, MLTCII Business Development Consultant and a former consultant with MLTC, NorSask Forest Products and Mistik Management.

Mistik Management Ltd., a forestry management company co-owned by the nine First Nations of MLTC, was a leader in economic reconciliation in 2022. However, the company had dealt with significant challenges not long after it was established in 1989. Richard Gladue, former Chief of the one of MLTCs Member First Nations and a leader in economic development at MLTC, had been actively involved in establishing the organization. Gladue loved the life and vitality of the boreal forest in the Meadow Lake region and felt a sense of responsibility to take care of the forest and the land for generations to come. This responsibility was balanced with the acknowledgement that the forest also provided vast economic development, employment and wealth generation opportunities for MLTC and its Member First Nations.

In the early 1990s, MLTC and Mistik Management dealt with a year-long blockade by a group of protesters that included members of Canoe Lake Cree First Nation, one of the Member First Nations of MLTC. They had not been consulted on Mistik’s processes and policies, and the company’s clear-cut logging had affected their ability to continue their traditional way of life and practices on the land. After the incident, Mistik Management moved more quickly to invest in a co-management process that they were still refining and using in 2022 when consulting with Indigenous groups and communities.

A natural resource economy brings together Indigenous peoples, industry and government. In this case, students will learn about the important role that relationships play and how decisions are made when balancing complex legal, environmental and economic interests. Students will learn about the history of duty to consult and accommodate in Canada; conduct a stakeholder analysis and reflect on how decisions affect stakeholder interests; and make recommendations for meaningful Indigenous engagement strategies using the concept of social license and indigenous principles.

This case is suitable for use in undergraduate courses on indigenous business, ethical decision-making, public policy and/or natural resource development. There may also be applications in other fields of study, including anthropology, economics and political science.

If the case is used in an indigenous business course, it would be best positioned in the last third of the class, after topics such as duty to consult and accommodate, social license and meaningful engagement with indigenous communities have been covered. If used in an ethical decision-making course, it would be best used when discussing stakeholder theory and engaging in stakeholder analysis. If used in a public policy course, the case could be used to start a discussion around the duty to consult and accommodate indigenous communities in Canada. If used in a natural resource development course, the case would be best used as an example of indigenous economic development.

This article summarizes the international scientific research output of global forest product models, infers future research trends and provides reference for quantitative analysis and mathematical modeling of Chinese forest product problems, with the aim of contributing to promoting domestic production of Chinese forest products and strengthening international trade competitiveness of forest products.

In 1999, Joseph Buongiorno, a scholar at the University of Wisconsin in the United States of America, proposed the global forest products model (GFPM), which was first applied to research in the global forestry sector. GFPM is a recursive dynamic model based on five assumptions: macroeconomics, local equilibrium, dynamic equilibrium, forest product conversion flow and trade inertia. Using a certain year from 1992 to present as the base period, it simulates and predicts changes in prices, production and import and export trade indicators of 14 forest products in 180 countries (regions) through computer programs. Its advantages lie in covering a wide range of countries and a wide variety of forest products. The data mainly include forest resource data, forest product trade data, and other economic data required by the model, sourced from the Food and Agriculture Organization (FAO) of the United Nations and the World Bank, respectively.

Compared to international quantitative and modeling research in the field of forest product production and trade, China's related research is not comprehensive and in-depth, and there is not much quantitative and mathematical modeling research, resulting in a significant gap. This article summarizes the international scientific research output of global forest product models, infers future research trends, and provides reference for quantitative analysis and mathematical modeling of Chinese forest product problems, with the aim of contributing to promoting domestic production of Chinese forest products and strengthening international trade competitiveness of forest products.

On the basis of summarizing and analyzing the international scientific research output of GFPM, sorting out the current research status and progress at home and abroad, this article discusses potential research expansion directions in 10 aspects, including the types, yield and quality of domestic forest product production, international trade of forest products, and external impacts on the forestry system, in order to provide new ideas for global forest product model research in China.

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 paper presents a new and well-structured framework that aims to assess the current environmental impact from a Greenhouse Gas (GHG) emissions perspective. This tool includes a new set of Lean Key Performance Indicators (KPIs), which translates the well-known logic of Overall Equipment Effectiveness in the field of GHG emissions, that can progressively detect industrial losses that cause GHG emissions and support decision-making for implementing improvements.

The new metrics are presented with reference to two different perspectives: (1) to highlight the deviation of the current value of emissions from the target; (2) to adopt a diagnostic orientation not only to provide an assessment of current performance but also to search for the main causes of inefficiencies and to direct improvement implementations.

The proposed framework was applied to a major company operating in the plywood production sector. It identified emission-related losses at each stage of the production process, providing an overall performance evaluation of 53.1%. The industrial application shows how the indicators work in practice, and the framework as a whole, to assess GHG emissions related to industrial losses and to proper address improvement actions.

This paper scrutinizes a new set of Lean KPIs to assess the industrial losses causing GHG emissions and identifies some significant drawbacks. Then it proposes a new structure of losses and KPIs that not only quantify efficiency but also allow to identify viable countermeasures.

Environmental performance (EnPerf) needs to be critically studied so organizations can understand enhancing it. The purpose of this study is mainly to examine and explain the influence of beliefs and values of the human resources regarding religiosity (REL) and workplace spirituality (WS) on shaping an environmentally friendly work culture comprising environmental ethics (EE) and environmental passion (EP), to enhance EnPerf.

A survey methodology was used, and 316 responses were collected from the employees working in industries on the top list of polluting the environment using purposive sampling. Structural equation modeling was deployed to test the hypotheses.

This research is conducted to identify specific relationships of variables with the environment. It was discovered that WS affected EP and EE, positively affecting EnPerf.

This study guides organizations and their management to adopt WS, EE and EP, as these all increase EnPerf in the organization.

Not much work has been conducted on the environmental culture based on REL and WS, using the ability-motivation-opportunity theory. This research analyzes employees’ intrinsic factors, such as REL and WS, to develop EP and EE. Thus helping to comprehend how they can use to enhance EnPerf, which is the current priority for the organizations.

This study aims to introduce and evaluate a model of host community support for sustainable tourism development (SSTD) based on the influential factors contributing to community support in a biosphere reserve.

Partial least squares structural equation modeling using SmartPLS 3 software is applied to analyze data collected from 473 residents of the Miankaleh Biosphere Reserve, Iran.

The findings reveal that the locals’ community support is affected by their level of environmental awareness, opportunity and attitudes toward SSTD. However, the results do not reveal an influence of environmental knowledge and community attachment on SSTD. The findings enrich the existing literature on community attitude predictors by showing that locals’ SSTD level is not consistently based on common predictors. Such support strongly depends on host communities’ attitudes toward supporting tourism, which is definitely not the same among residents. It is imperative to know whether people’s attitudes arise from a desire to protect the area or for reasons of self-interest.

The findings provide further support for the tenets of the segmentation approach and challenge existing knowledge on host communities’ attitudes about factors influencing residents toward SSTD. The findings have several practical implications regarding community participation for regional and national authorities and destination policymakers.

采用SmartPLS3软件建立偏最小二乘结构方程模型, 对从伊朗米安卡莱生物圈保护区的473名居民获得的数据进行分析。

本研究旨在介绍和评估基于影响促进生物圈保护内社区支持的可持续旅游发展(SSTD)的东道社区支持模型。

结果表明, 本地社区的环境意识水平、机会和对SSTD的态度影响着社区支持。然而, 研究结果并没有揭示环境知识和社区依恋对SSTD的影响。

这一发现丰富了现有的关于社区态度预测因子的文献, 表明当地人的SSTD并不一致基于共同的预测因子。这种支持很大程度上取决于东道社区对支持旅游的态度, 这在居民中肯定是不一样的。有必要了解人们的态度是出于保护该地区的愿望还是出于自身利益的原因。此外, 研究结果为细分方法的原则提供了进一步的支持, 并挑战了关于东道社区对影响居民SSTD态度的现有认知。

Se aplica un modelo de ecuaciones estructurales por mínimos cuadrados parciales con el programa SmartPLS 3 para analizar los datos recogidos de 473 residentes de la reserva de la biosfera de Miankaleh (Irán).

Esta investigación pretende introducir y evaluar un modelo de apoyo de la comunidad anfitriona al desarrollo del turismo sostenible (SSTD) basado en los factores que contribuyen al apoyo de la comunidad en una reserva de la biosfera.

Los resultados revelan que el apoyo de la comunidad local se ve afectado por su nivel de concienciación medioambiental, sus oportunidades y sus actitudes hacia el desarrollo del turismo sostenible. Sin embargo, los resultados no muestran una influencia del conocimiento medioambiental y el apego de la comunidad sobre el desarrollo turístico sostenible.

Los resultados enriquecen la literatura existente sobre predictores de la actitud de la comunidad al mostrar que el nivel de desarrollo turístico sostenible de los autóctonos no se basa únicamente en factores tradicionales. Dicho apoyo depende en gran medida de las actitudes de las comunidades anfitrionas hacia el apoyo al turismo y éste no es el mismo entre los residentes. Es necesario conocer si la actitud de los individuos surge de un deseo de proteger la zona o por razones de interés propio. Además, los resultados corroboran los principios del enfoque de segmentación y retan los conocimientos existentes sobre las actitudes de las comunidades anfitrionas acerca de los factores de los residentes sobre el desarrollo turístico sostenible.

This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?

Purpose: The study aims to analyse and discuss the effect of COVID-19 on businesses. The chapter discusses the various machine learning (ML) tools and techniques, which can help in better decision making by businesses in the present world.

Need for the Study: COVID-19 has increased the role of VUCA elements in the business environment, and there is a need to address the challenges faced by businesses in such environment. ML and artificial learning can help businesses in facing such challenges.

Methodology: The focus and approach of the chapter are in the context of using artificial intelligence (AI) and ML techniques for decision making during the COVID-19 pandemic in a VUCA business environment.

Findings: The key findings and their implications emphasise the importance of understanding and implementing AI and ML techniques in business strategies during times of crisis.

Practical Implications: The chapter’s content is in the context of using AI and ML techniques during the COVID-19 pandemic and in a VUCA business environment.

The primary objective of this research is to chronicle how the Environmental Protection Agency (EPA) and other United States Federal Government Agencies (USFGA) agencies have played a role in shaping the trajectory of financial reporting for sustainability, with a particular emphasis on triple bottom line (TBL). This exploration extends to other indexes reporting sustainability data encompassed within financial, social and environmental reporting.

This study adopts an illustrative methodology, utilizing data sourced from governmental, business and international organizational documents.

Sustainability accounting predominantly finds its place within the framework of TBL. However, it is crucial to note that sustainability reporting remains voluntary rather than mandatory. Nevertheless, accounting firms and professional accounting societies have embraced it as a supplementary facet of financial accounting reporting.

The research highlights the historical evolution of sustainability within the USFGA and corporate entities. Corporations’ interest in accounting for sustainability performances has significantly contributed to the emergence of voluntary sustainability accounting rules, as embodied by the TBL.

This paper aims to apply several data mining techniques for predicting the daily precision improvement of Jakarta Islamic Index (JKII) prices based on big data of symmetric volatility in Indonesia’s Islamic stock market.

This research uses big data mining techniques to predict daily precision improvement of JKII prices by applying the AdaBoost, K-nearest neighbor, random forest and artificial neural networks. This research uses big data with symmetric volatility as inputs in the predicting model, whereas the closing prices of JKII were used as the target outputs of daily precision improvement. For choosing the optimal prediction performance according to the criteria of the lowest prediction errors, this research uses four metrics of mean absolute error, mean squared error, root mean squared error and R-squared.

The experimental results determine that the optimal technique for predicting the daily precision improvement of the JKII prices in Indonesia’s Islamic stock market is the AdaBoost technique, which generates the optimal predicting performance with the lowest prediction errors, and provides the optimum knowledge from the big data of symmetric volatility in Indonesia’s Islamic stock market. In addition, the random forest technique is also considered another robust technique in predicting the daily precision improvement of the JKII prices as it delivers closer values to the optimal performance of the AdaBoost technique.

This research is filling the literature gap of the absence of using big data mining techniques in the prediction process of Islamic stock markets by delivering new operational techniques for predicting the daily stock precision improvement. Also, it helps investors to manage the optimal portfolios and to decrease the risk of trading in global Islamic stock markets based on using big data mining of symmetric volatility.

This research is a pioneer in using big data mining of symmetric volatility in the prediction of an Islamic stock market index.