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This paper examines how lateral accountability mechanisms may be used to address the unity–diversity tension in a large not-for-profit (NFP) inter-organizational partnership governed under a lead organization model.

A case study was conducted in the New South Wales Settlement Partnership comprising 23 NFP organizations providing settlement services for migrants and humanitarian entrants. Multiple data sources included semi-structured interviews, proprietary and publicly available documents and observation.

The paper demonstrates (1) the usefulness of a strength-based approach that the lead organization adopts in enacting lateral accountability mechanisms, which enables a balance between unity and diversity in the partnership; and (2) the capability of the lead organization governance model to address the unity–diversity tension.

The paper (1) identifies the importance of a strength-based approach in implementing lateral accountability mechanisms to address the unity–diversity tension; and (2) challenges prior research that advocates the network administrative organization governance model in addressing the tension.

For practice, the paper identifies a suite of lateral accountability practices designed to address the unity–diversity tension. For policy, it provides confidence for government in promulgating the lead organization governance model in “purchasing” public services.

The paper demonstrates how lateral accountability mechanisms may be used to provide a balance between the objectives of preserving and leveraging the benefits of partner diversity and achieving unity. The strength-based approach (used in enacting the accountability mechanisms), while having a history in psychology and social work research, has not been recognized in prior partnership accountability and governance studies.

The purpose of this chapter is to show the multi-faceted nature of shopping behavior at North Market and to develop the concept of productive leisure as a way of examining and reframing Daniel Miller’s Theory of Shopping.

This chapter utilizes Daniel Miller’s Theory of Shopping as a starting point to understand the dimensions of shopping at North Market. It draws upon survey data collected by North Market, as well as participant observations and informal interviews conducted by the authors.

Much of the shopping at the market goes beyond simple provisioning, thrift, and treats, and instead fits into a hybrid category we call “productive leisure.” Productive leisure occurs when individuals complete productive tasks during their leisure time. It maximizes thrift-time by completing productive tasks during leisure and in response to or in connection with finding a reward (treat). In the case of shoppers at North Market, many customers are using their leisure time to provision.

This chapter presents a new way of thinking about shopping at public markets and could potentially serve to help public markets redefine their role in local food systems and in the greater community. More broadly, this chapter provides a unique insight into how and why people use public markets.

The purpose of this study is to seek to quantify how unique service resources and consumer habit affect e-service loyalty (e-loyalty) in a highly competitive market. This study is grounded on Chinese social networking service (SNS) industry. A resource-based view is introduced as an alternative perspective to understand building consumer loyalty in e-service contexts.

A research framework is developed by reviewing prior literature. An online survey is conducted to collect research data. Based on 221 valid responses, the research model is tested by using partial least squares path modeling technique.

The features of market environments affects the loyalty of consumers to e-service providers. Consumers become mercenary in highly competitive and low differentiation e-service markets like Chinese SNS industry. The interaction of satisfaction and switching cost affects loyalty. Satisfied consumers can be either loyal or not loyal to a service provider depending on their level of switching cost, but unsatisfied consumers will have no loyalty. In addition, users are loyal to a SNS partly because it is their habit to use the service. Our study suggests that relying on consumer satisfaction to build e-loyalty may be problematic and risky.

The study represents an attempt to introduce the resource-based view to e-loyalty research. The research highlights the importance of habit in building consumers’ e-service satisfaction and loyalty and contributes to new insights on the importance of industry environment in determining e-service satisfaction–loyalty relationship based on studying consumers in a highly competitive market.

The People’s Republic of China, as a progressively developing economy, is subject to dynamic structural changes, which are potentially de‐stabilizing in nature. Since the end of the 1970s China had abandoned Mao Zedong’s socioeconomic theories and policies and instituted profound socioeconomic reforms. Her more pragmatic approach has increasingly emphasized economic freedom and individualism. The pursued “pragmatism” involves a revolutionary mixture of both a planned and a market economy with greater economic but not political freedom. Essential socioeconomic reforms were not complemented by requisite political reforms. According to Walter Eucken’s “instability thesis,” this may de‐stabilize China’s socioeconomic and political structures. The challenge which China continues to face is how to reconcile two sets of conflicting principles, economic freedom and Marxist‐Leninist‐Maoist control of politics and society, resolving Eucken’s hypothesis of potential long‐term instability. This paper addresses this challenge in terms of ethical and economic perspectives.

Livelihood recovery is a top priority to sustain resettled communities. The purpose of this paper is to assess livelihood vulnerability of those displaced and resettled in the aftermath of the 2008 Wenchuan earthquake, China, based on a newly constructed locational adjustable framework.

The study takes two resettlement villages in Sichuan Province as case study areas. Face-to-face surveys using a structured questionnaire and semi-structured in-depth interviews were conducted to collect primary data in 2012-2013.

The findings show that distant resettlement of people post the Wenchuan earthquake has resulted in an increased livelihood vulnerability within resettlers and that they face more hazards post-resettlement when compared to host residents in the resettlement areas.

The indicators considered for the livelihood vulnerability index (LVI) are only a subset that represents typical factors applicable in the context of rural settings of China. The LVI may vary if more indicators are incorporated or coefficients obtained using different methods.

Highlights should be placed on livelihood assets and hazards to livelihood of the displaced people. During the transition period there is a pressing need for greater efforts to enhance migrants’ employment skills and assist them to restore viable livelihood activities.

This paper constructs a locational adjustable framework for analyzing and assessing livelihood vulnerability of disaster-induced resettlers from three aspects: livelihood hazards, livelihood assets and coping strategies.

In contrast to existing studies, this paper aims to propose that digital transformation does not depend on a single condition; rather, it depends on the interaction between internal and external factors of a firm. Therefore, the aim of this paper is to examine the effect of a combination of internal and external factors on a firm's digital transformation intention.

An empirical analysis on a sample of 112 Chinese small- and medium-sized firms was conducted by applying smart-PLS and fuzzy set qualitative comparative analysis (fsQCA).

The results of smart PLS show that external pressures (institutional and market pressures) and human capital have a positive impact on corporate digital transformation intentions. From a combination perspective, the results of the fsQCA show that there are five causal conditions that lead to high digital transformation intention. In contrast to the net effect, the results of fsQCA show that different combinations of states of internal (human capital, organizational culture and technological capital) and external elements (institutional and market pressures) of the firm are likely to stimulate digital transformation intention.

This study provides empirically based insights into firms' digital transformation intentions and advances the current understanding of the drivers and inhibitors of digital transformation. Unlike most current research, which tends to focus on the net effect of factors influencing the digital transformation of enterprises, this study focuses on identifying the core elements influencing enterprises' digital transformation intention, especially the joint effect of different factors, both internal and external to the enterprise. The combined SEM and fsQCA findings of this paper not only enrich the existing theories on digital transformation but also have high value in guiding the digital transformation of firms.

The purpose of this paper is to explore the combined influence of exchange quality (i.e. leader-member exchange or LMX, and coworker exchange or CWX) and organizational identity on the relationship between authoritarian leadership and employee innovation based on social cognition theory and social exchange theory.

The authors used mediation analysis, moderation analysis and bootstrapping method to test the proposed model with data from 286 supervisor-subordinate dyads in China, covering manufacturing and Internet companies. This study also used the time-lag design, and the questionnaires were collected by means of multi-source and multiphase.

The results indicate that authoritarian leadership can positively influence employee innovation behavior, and organizational identity mediates this relationship. Both LMX and CWX can significantly moderate the relationship between authoritarian leadership and organizational identity, while the moderating effect is the opposite. To be specific, CWX has appositive effect, while LMX has a negative effect. Furthermore, the indirect effect of authoritarian leadership on employee innovation behavior via organizational identity is also moderated by LMX.

At present, scholars have not yet reached a consensus on the relationship between authoritarian leadership and employee innovation behavior. Based on social cognition theory and social exchange theory, this study incorporates organizational identity and exchange quality into the research model to explore this lack of consensus. By verifying the research model, this study offers original views on when and how authoritarian leadership and exchange quality affect employee innovation behavior, which enriches the current research on employee innovation and the effectiveness of authoritarian leadership in different cultural contexts.

The construction industry is facing challenges not only for workers' mobility in the pandemic situation but also for Lean Construction (LC) practise in responding to the high-quality development during the post-pandemic. As such, this paper presents a construction workforce management framework based on LC to manage both the emergency goal in migrant worker management and the long-term goal in labour productivity improvement in China.

The framework is created based on the integrated culture and technology strategies of LC. A combination of qualitative and quantitative methods is taken to explore factors influencing the mobility of construction workers and to measure labour productivity improvement. The case study approach is adopted to demonstrate the framework application.

For method application, a time-and-motion study and Percent Plan Complete indicator are proposed to offer labour productivity measurements of “resources efficiency” and “flow efficiency”. Moreover, the case study provides an industry level solution for construction workforce management and Lean Construction culture shaping, as well as witnesses the LC culture and technology strategies alignment contributing to LC practise innovation.

Compared with previous studies which emphasised solely LC techniques rather than socio-technical system thinking, the proposed integration framework as well as implementation of “Worker's Home” and “Lean Work Package” management models in the COVID-19 pandemic contribute to new extensions of both the fundamental of knowledge and practise in LC.

Imitation learning is a powerful tool for planning the trajectory of robotic end-effectors in Cartesian space. Present methods can adapt the trajectory to the obstacle; however, the solutions may not always satisfy users, whereas it is hard for a nonexpert user to teach the robot to avoid obstacles in time as he/she wishes through demonstrations. This paper aims to address the above problem by proposing an approach that combines human supervision with the kernelized movement primitives (KMP) model.

This approach first extracts the reference database used to train KMP from demonstrations by using Gaussian mixture model and Gaussian mixture regression. Subsequently, KMP is used to modulate the trajectory of robotic end-effectors in real time based on feedback from its interaction with humans to avoid obstacles, which benefits from a novel reference database update strategy. The user can test different obstacle avoidance trajectories in the current task until a satisfactory solution is found.

Experiments performed with the KUKA cobot for obstacle avoidance show that this approach can adapt the trajectories of the robotic end-effector to the user’s wishes in real time, including trajectories that the robot has already passed and has not yet passed. Simulation comparisons also show that it exhibits better performance than KMP with the original reference database update strategy.

An interactive learning approach based on KMP is proposed and verified, which not only enables users to plan the trajectory of robotic end-effectors for obstacle avoidance more conveniently and efficiently but also provides an effective idea for accomplishing interactive learning tasks under constraints.

With the continuous growth of online education, the quality issue of online educational videos has become increasingly prominent, causing students in online learning to face the dilemma of knowledge confusion. The existing mechanisms for controlling the quality of online educational videos suffer from subjectivity and low timeliness. Monitoring the quality of online educational videos involves analyzing metadata features and log data, which is an important aspect. With the development of artificial intelligence technology, deep learning techniques with strong predictive capabilities can provide new methods for predicting the quality of online educational videos, effectively overcoming the shortcomings of existing methods. The purpose of this study is to find a deep neural network that can model the dynamic and static features of the video itself, as well as the relationships between videos, to achieve dynamic monitoring of the quality of online educational videos.

The quality of a video cannot be directly measured. According to previous research, the authors use engagement to represent the level of video quality. Engagement is the normalized participation time, which represents the degree to which learners tend to participate in the video. Based on existing public data sets, this study designs an online educational video engagement prediction model based on dynamic graph neural networks (DGNNs). The model is trained based on the video’s static features and dynamic features generated after its release by constructing dynamic graph data. The model includes a spatiotemporal feature extraction layer composed of DGNNs, which can effectively extract the time and space features contained in the video's dynamic graph data. The trained model is used to predict the engagement level of learners with the video on day T after its release, thereby achieving dynamic monitoring of video quality.

Models with spatiotemporal feature extraction layers consisting of four types of DGNNs can accurately predict the engagement level of online educational videos. Of these, the model using the temporal graph convolutional neural network has the smallest prediction error. In dynamic graph construction, using cosine similarity and Euclidean distance functions with reasonable threshold settings can construct a structurally appropriate dynamic graph. In the training of this model, the amount of historical time series data used will affect the model’s predictive performance. The more historical time series data used, the smaller the prediction error of the trained model.

A limitation of this study is that not all video data in the data set was used to construct the dynamic graph due to memory constraints. In addition, the DGNNs used in the spatiotemporal feature extraction layer are relatively conventional.

In this study, the authors propose an online educational video engagement prediction model based on DGNNs, which can achieve the dynamic monitoring of video quality. The model can be applied as part of a video quality monitoring mechanism for various online educational resource platforms.