title: Investigating boredom in social commerce: Definition, function, and consequences

authors: Ali, Rebecca

abstract: Boredom, a ubiquitous emotion, has garnered extensive research attention in digital communication and business, yet remains underexamined in the context of social commerce (s-commerce). This dissertation, comprising three studies (with studies 2 and 3 extending the first), aimed at comprehensively understanding the role of boredom in s-commerce and its implications for consumer behavior. The first study employs grounded theory, reviewing 103 studies to construct an inductive framework identifying five key building blocks. It highlights boredom as stemming from individual, situational, and mismatch factors, influencing individuals to seek stimulation from s-commerce, yielding both positive and negative outcomes. The study suggests constructive intervention strategies for positive development and market performance in s-commerce.
The second study investigates the interaction between boredom and impulse buying behavior, considering the influence of consumer personality and external s-commerce stimuli. Analyzing data from New Zealand consumers (n=243) with SmartPLS, findings reveal that boredom, influenced by personality, triggers impulse buying. Among s-commerce stimuli social value positively moderates the boredom-impulse buying relationship, while atmosphere shows a negative moderation effect, and price offering is found to be insignificant.
The third study explores the potential link between boredom and consumer brand-cyberbullying (CBCB) in s-commerce. Analyzing data from New Zealand consumers (n=253) with SmartPLS, it establishes that boredom, influenced by personality factors, motivates CBCB behavior. Also, this relationship is positively moderated by s-commerce stimuli. This research provides valuable insights for academics, policymakers, and marketers seeking to understand and address the complexities of bored consumers in the evolving landscape of s-commerce.

text: Investigating boredom in social commerce: Definition, function, and consequences Boredom, a ubiquitous emotion, has garnered extensive research attention in digital communication and business, yet remains underexamined in the context of social commerce (s-commerce). This dissertation, comprising three studies (with studies 2 and 3 extending the first), aimed at comprehensively understanding the role of boredom in s-commerce and its implications for consumer behavior. The first study employs grounded theory, reviewing 103 studies to construct an inductive framework identifying five key building blocks. It highlights boredom as stemming from individual, situational, and mismatch factors, influencing individuals to seek stimulation from s-commerce, yielding both positive and negative outcomes. The study suggests constructive intervention strategies for positive development and market performance in s-commerce.
The second study investigates the interaction between boredom and impulse buying behavior, considering the influence of consumer personality and external s-commerce stimuli. Analyzing data from New Zealand consumers (n=243) with SmartPLS, findings reveal that boredom, influenced by personality, triggers impulse buying. Among s-commerce stimuli social value positively moderates the boredom-impulse buying relationship, while atmosphere shows a negative moderation effect, and price offering is found to be insignificant.
The third study explores the potential link between boredom and consumer brand-cyberbullying (CBCB) in s-commerce. Analyzing data from New Zealand consumers (n=253) with SmartPLS, it establishes that boredom, influenced by personality factors, motivates CBCB behavior. Also, this relationship is positively moderated by s-commerce stimuli. This research provides valuable insights for academics, policymakers, and marketers seeking to understand and address the complexities of bored consumers in the evolving landscape of s-commerce.

year: 2024-09-18

title: Determining estuarine seagrass density measures from low altitude multispectral imagery flown by remotely piloted aircraft

authors: Martin, Ross David

subjects: Seagrass

abstract: Seagrass is the subject of significant conservation research. Seagrass is ecologically important and of significant value to human interests. Many seagrass species are thought to be in decline.  Degradation of seagrass populations are linked to anthropogenic environmental issues. Effective management requires robust monitoring that is affordable at large scale. Remote sensing methods using satellite and aircraft imagery enable mapping of seagrass populations at landscape scale. 
Aerial monitoring of a seagrass population can require imagery of high spatial and/or spectral resolution for successful feature extraction across all levels of seagrass density. Remotely piloted aircraft (RPA) can operate close to the ground under precise flight control enabling repeated surveys in high detail with accurate revisit-positioning.
This study evaluates a method for assessing intertidal estuarine seagrass (Zostera muelleri) presence/absence and coverage density using multispectral imagery collected by a remotely piloted aircraft (RPA) flying at 30 m above the estuary surface (2.7 cm ground sampling distance). The research was conducted at Wharekawa Harbour on the eastern coast of the Coromandel Peninsula, North Island, New Zealand.
Differential drainage of residual ebb waters from the surface of an estuary at low tide creates a mosaic of drying sediment, draining surface and static shallow pooling that has potential to interfere with spectral observations. The field surveys demonstrated that despite minor shifts in the spectral coordinates of seagrass and other surface material, there was no apparent difference in image classification outcome from the time of bulk tidal water clearance to the time of returning tidal flood.
For the survey specification tested, classification accuracy increased with decreasing segmentation scale. Pixel-based image analysis (PBIA) achieved higher classification accuracy than object-based image analysis (OBIA) assessed at a range of segmentation scales. Contaminating objects such as shells and detritus can become aggregated within polygon objects when OBIA is applied but remain as isolated objects under PBIA at this image resolution. There was clear separability of spectra for seagrass and sediment, but shell and detritus confounded the classification of seagrass density in some situations.  High density seagrass was distinct from sediment, but classification error arose for sparse seagrass.
Three classifiers (linear discriminant analysis, support vector machine and random forest) and three feature selection options (no selection, collinearity reduction and recursive feature elimination) were assessed for effect on classification performance. The random forest classifier yielded the highest classification accuracy, with no accuracy benefit gained from collinearity reduction or recursive feature elimination. Spectral vegetation indices and texture layers substantially improved classification accuracy. Object geometry made a negligible contribution to classification accuracy using mean-shift segmentation at this image-scale.
The method achieved classification of seagrass density with up to 84% accuracy on a three-tier end-member class scale (low, medium, and high density) when using training data formed using visual interpretation of ground reference photography, and up to 93% accuracy using precisely measured seagrass leaf-area. Visual interpretation agreed with precisely measured seagrass leaf area 88% of the time with some misattribution at mid-density. Visual interpretation was substantially faster to apply than measuring the leaf area. A decile class scale for seagrass density correlated with actual leaf area measures more than the three-tier scale, however, was less accurate for absolute class attribution.
The research demonstrates that seagrass feature extraction from RPA-flown imagery is a feasible and repeatable option for seagrass population monitoring and environmental reporting. Further calibration is required for whole- and multi-estuary application.

text: Determining estuarine seagrass density measures from low altitude multispectral imagery flown by remotely piloted aircraft Seagrass is the subject of significant conservation research. Seagrass is ecologically important and of significant value to human interests. Many seagrass species are thought to be in decline.  Degradation of seagrass populations are linked to anthropogenic environmental issues. Effective management requires robust monitoring that is affordable at large scale. Remote sensing methods using satellite and aircraft imagery enable mapping of seagrass populations at landscape scale. 
Aerial monitoring of a seagrass population can require imagery of high spatial and/or spectral resolution for successful feature extraction across all levels of seagrass density. Remotely piloted aircraft (RPA) can operate close to the ground under precise flight control enabling repeated surveys in high detail with accurate revisit-positioning.
This study evaluates a method for assessing intertidal estuarine seagrass (Zostera muelleri) presence/absence and coverage density using multispectral imagery collected by a remotely piloted aircraft (RPA) flying at 30 m above the estuary surface (2.7 cm ground sampling distance). The research was conducted at Wharekawa Harbour on the eastern coast of the Coromandel Peninsula, North Island, New Zealand.
Differential drainage of residual ebb waters from the surface of an estuary at low tide creates a mosaic of drying sediment, draining surface and static shallow pooling that has potential to interfere with spectral observations. The field surveys demonstrated that despite minor shifts in the spectral coordinates of seagrass and other surface material, there was no apparent difference in image classification outcome from the time of bulk tidal water clearance to the time of returning tidal flood.
For the survey specification tested, classification accuracy increased with decreasing segmentation scale. Pixel-based image analysis (PBIA) achieved higher classification accuracy than object-based image analysis (OBIA) assessed at a range of segmentation scales. Contaminating objects such as shells and detritus can become aggregated within polygon objects when OBIA is applied but remain as isolated objects under PBIA at this image resolution. There was clear separability of spectra for seagrass and sediment, but shell and detritus confounded the classification of seagrass density in some situations.  High density seagrass was distinct from sediment, but classification error arose for sparse seagrass.
Three classifiers (linear discriminant analysis, support vector machine and random forest) and three feature selection options (no selection, collinearity reduction and recursive feature elimination) were assessed for effect on classification performance. The random forest classifier yielded the highest classification accuracy, with no accuracy benefit gained from collinearity reduction or recursive feature elimination. Spectral vegetation indices and texture layers substantially improved classification accuracy. Object geometry made a negligible contribution to classification accuracy using mean-shift segmentation at this image-scale.
The method achieved classification of seagrass density with up to 84% accuracy on a three-tier end-member class scale (low, medium, and high density) when using training data formed using visual interpretation of ground reference photography, and up to 93% accuracy using precisely measured seagrass leaf-area. Visual interpretation agreed with precisely measured seagrass leaf area 88% of the time with some misattribution at mid-density. Visual interpretation was substantially faster to apply than measuring the leaf area. A decile class scale for seagrass density correlated with actual leaf area measures more than the three-tier scale, however, was less accurate for absolute class attribution.
The research demonstrates that seagrass feature extraction from RPA-flown imagery is a feasible and repeatable option for seagrass population monitoring and environmental reporting. Further calibration is required for whole- and multi-estuary application.

year: 2020

title: Defining indigenous trade: A working paper incorporating Māori and indigenous perspectives

authors: McLellan, Georgia

abstract: This working paper is part of a New Zealand Government–funded research project titled ‘Tauhokohoko: Enabling mana motuhake through Indigenous trade’ (Tauhokohoko), which was developed by the University of Waikato and Te Rōpū Taumata Charitable Trust (Te Taumata). The paper was prepared by the Tauhokohoko Research Aim 1.3 team and sets out a working definition of ‘Indigenous trade’.

The Tauhokohoko Research Aim 1.3 team acknowledges that the term ‘Indigenous’ can be problematic, since it collectivises the experiences of all Indigenous peoples. The project acknowledges the diverse realities of Indigenous peoples and strives to use the term in an inclusive way. Despite the existence of fraught relations between Indigenous peoples and postcolonial trade practices, Indigenous peoples have always had their own trade practices and relations grounded in Indigenous ways of being, knowing and doing. Within contemporary economies, Indigenous peoples are increasingly turning to international trade to meet their economic, social and cultural aspirations.

The Tauhokohoko Research Aim 1.3 team believes that Indigenous trade, when practiced in the right way, can be used as a tool for enhancing Indigenous self-determination and collective wellbeing. This paper outlines a tentative definition of ‘Indigenous trade’ for research purposes and provides grounded examples of Indigenous trade that fit within that tentative definition. The Tauhokohoko project is concerned primarily with the involvement of Indigenous peoples in international trade as a financial activity; however, the tentative definition encompasses financial, non financial, tangible and non-tangible forms of exchange. The paper ends by outlining the key literature that informs the working definition of ‘Indigenous trade’, including perspectives on the origins of international trade and Indigenous trade, international business and entrepreneurship, and Indigenous economies.

text: Defining indigenous trade: A working paper incorporating Māori and indigenous perspectives This working paper is part of a New Zealand Government–funded research project titled ‘Tauhokohoko: Enabling mana motuhake through Indigenous trade’ (Tauhokohoko), which was developed by the University of Waikato and Te Rōpū Taumata Charitable Trust (Te Taumata). The paper was prepared by the Tauhokohoko Research Aim 1.3 team and sets out a working definition of ‘Indigenous trade’.

The Tauhokohoko Research Aim 1.3 team acknowledges that the term ‘Indigenous’ can be problematic, since it collectivises the experiences of all Indigenous peoples. The project acknowledges the diverse realities of Indigenous peoples and strives to use the term in an inclusive way. Despite the existence of fraught relations between Indigenous peoples and postcolonial trade practices, Indigenous peoples have always had their own trade practices and relations grounded in Indigenous ways of being, knowing and doing. Within contemporary economies, Indigenous peoples are increasingly turning to international trade to meet their economic, social and cultural aspirations.

The Tauhokohoko Research Aim 1.3 team believes that Indigenous trade, when practiced in the right way, can be used as a tool for enhancing Indigenous self-determination and collective wellbeing. This paper outlines a tentative definition of ‘Indigenous trade’ for research purposes and provides grounded examples of Indigenous trade that fit within that tentative definition. The Tauhokohoko project is concerned primarily with the involvement of Indigenous peoples in international trade as a financial activity; however, the tentative definition encompasses financial, non financial, tangible and non-tangible forms of exchange. The paper ends by outlining the key literature that informs the working definition of ‘Indigenous trade’, including perspectives on the origins of international trade and Indigenous trade, international business and entrepreneurship, and Indigenous economies.

year: 2025-12-10