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This study aims to discuss appropriate individual, organizational and macroleveled conditions when it is desired to foster intrapreneurship with nudge techniques.

Conceptual paper.

This paper aims to provide theoretical framework on the relationship between nudges approach and intrapreneurship and offer measurable propositions at different level of analysis for future research.

At the individual level it is theorized that nudges techniques on intrapreneurship will work when: “agents are aware of being nudged”, “choice architect is a well-recognized leader”, “perception of meaningful work is high” and “agents had former experience about being nudged”. At the organizational level it is claimed that the “number”, “type” and “frequency” of nudges plays the prominent role. Lastly, at the macrolevel it is postulated that “cultural adjustments”, “providing education” and “forming networks” sets the ground for pushing masses to intrapreneurial activities via nudges.

Recent trends have shown the integration of Chinese word segmentation (CWS) and part-of-speech (POS) tagging to enhance syntactic and semantic parsing. However, the potential utility of hierarchical and structural information in these tasks remains underexplored. This study aims to leverage multiple external knowledge sources (e.g. syntactic and semantic features, lexicons) through various modules for the joint task.

We introduce a novel learning framework for the joint CWS and POS tagging task, utilizing graph convolutional networks (GCNs) to encode syntactic structure and semantic features. The framework also incorporates a pre-defined lexicon through a lexicon attention module. We evaluate our model on a range of public corpora, including CTB5, PKU and UD, the novel ZX dataset and the comprehensive CTB9 dataset.

Experimental results on these benchmark corpora demonstrate the effectiveness of our model in improving the performance of the joint task. Notably, we find that syntax information significantly enhances performance, while lexicon information helps mitigate the issue of out-of-vocabulary (OOV) words.

This study introduces a comprehensive approach to the joint CWS and POS tagging task by combining multiple features. Moreover, the proposed framework offers potential adaptability to other sequence labeling tasks, such as named entity recognition (NER).

The COVID-19 outbreak disrupted the business environment and severely affected the morale and performance of the employees. Further, the Indian automobile industry witnessed major setbacks and drastically impacted sector in COVID-19. Talent agility is an emerging concept in the field of HRM that will foster innovations and productivity in the automobile industry. Thus, this study aims to explore the barriers to building in-house agile talents in the Indian automobile industry in the new normal.

The barriers of talent agility were identified through a literature review and validated through experts’ opinions. This study used a hybrid approach, which combines Interpretive Structural Modelling-Polarity (ISM-P) and decision-making trial and evaluation laboratory (DEMATEL) to develop a hierarchical structural model of the barriers, followed by classification into cause and effect groups.

The result of the multi-method approach identified that shortage of skills and competencies, lack of IT infrastructure, lack of ambidextrous leaders, lack of smart HRM technologies and practices, lack of attractive reward system/career management, poor advanced T&D, poor industry, institute interface and financial constraints are the critical barriers.

It can provide a strategic roadmap for automobile manufacturers to promote talent agility in the current wave of digitalization (Industry 4.0). This study can help the managers to address and overcome the barrier and hurdles in building talent agility.

This study is unique in that it addresses the contemporary issues related to talent agility in the context of the Indian automobile industry in the current rapidly changing environment. This study developed a holistic integrated ISM(P)-DEMATEL hierarchical framework on the barriers of talent agility indicating inner dependency weights, i.e., the strength of interrelationship between the barriers.

This paper aims to empirically investigate how knowledge coordination is carried out within and across expert teams in the outsourcing service desk context from the standpoint of the information and communication technologies (ICT) provider.

The authors draw on an embedded case study to unravel the mechanisms used by expert teams to achieve knowledge coordination. Data collection included semi-structured interviews and document analysis over eight months.

Four groups of coordination mechanisms were found according to their nature and role in helping MESA achieve its organizational goals. The authors also identified how this set of mechanisms responds to the task-resource dependency and how it evolves over time to provide reliable ICT services. Furthermore, the analysis of four knowledge coordination dimensions inside each group uncovers the complexity of coordination in the studied context, with the “who” dimension being predominant across the groups. The analysis further reveals that the content (what) and mode of coordination (how) of each group respond to elements in the knowledge coordination process relevant for the different stages of the ICT service delivery process.

The case study addresses how people in outsourced services coordinate their knowledge to manage the interdependencies among the involved organizations and thus, achieving their goals. This study extends previous research that had analyzed the consequences of knowledge coordination for providers and customers by delving into the mechanisms used in this process from the provider’s point of view.

The advancements in internet technologies and the use of sophisticated digital devices in supply chain operations incessantly generate enormous amounts of data, which is termed as big data (BD). The BD technologies have brought about a paradigm shift in the supply chain decision-making towards profitability and sustainability. The aim of this work is to address the issue of implementation of the big data analytics (BDA) in sustainable supply chain management (SSCM) by identifying the relevant factors and developing a structural model for this purpose.

Through a comprehensive literature review and experts’ opinion, the crucial factors are found using the PESTEL framework, which covers political, economic, social, technological, environmental and legal factors. The structural model is developed based on the results of the total interpretive structural modelling (TISM) procedure and MICMAC analysis.

The policy support regarding IT, culture of data-based decision-making, inappropriate selection of BDA technologies and the laws related to data security and privacy are found to affect most of the other factors. Also, the company’s vision towards environmental performance and willingness for material and energy optimization are found to be crucial for the environmental and social sustainability of the supply chain.

The study is focused on the manufacturing supply chain in emerging economies. It may be extended to other industry sectors and geographical areas. Also, additional factors may be included to make the model more robust.

The proposed model imparts an understanding of the relative importance and interrelationship of factors. This may be useful to managers to assess their strengths and weaknesses and ascertain their priorities in the context of their organization for developing a suitable investment plan.

The study establishes the importance of BDA for conservation and management of energy and material. This is crucial to develop strategies for enhancing eco-efficiency of the supply chain, which in turn enhances the economic returns for the society.

This study addresses the implementation of BDA in SSCM in the context of emerging economies. It uses the PESTEL framework for identifying the factors, which is a comprehensive framework for strategic planning and decision-making. This study makes use of the TISM methodology for model development and deliberates on the social and environmental implications too, apart from theoretical and managerial implications.

Most previous studies predicting review helpfulness ignored the significance of deep features embedded in review text and instead relied on hand-crafted features. Hand-crafted and deep features have the advantages of high interpretability and predictive accuracy. This study aims to propose a novel review helpfulness prediction model that uses deep learning (DL) techniques to consider the complementarity between hand-crafted and deep features.

First, an advanced convolutional neural network was applied to extract deep features from unstructured review text. Second, this study used previous studies to extract hand-crafted features that impact the helpfulness of reviews and enhance their interpretability. Third, this study incorporated deep and hand-crafted features into a review helpfulness prediction model and evaluated its performance using the Yelp.com data set. To measure the performance of the proposed model, this study used 2,417,796 restaurant reviews.

Extensive experiments confirmed that the proposed methodology performs better than traditional machine learning methods. Moreover, this study confirms through an empirical analysis that combining hand-crafted and deep features demonstrates better prediction performance.

To the best of the authors’ knowledge, this is one of the first studies to apply DL techniques and use structured and unstructured data to predict review helpfulness in the restaurant context. In addition, an advanced feature-fusion method was adopted to better use the extracted feature information and identify the complementarity between features.

大多数先前预测评论有用性的研究忽视了嵌入在评论文本中的深层特征的重要性, 而主要依赖手工制作的特征。手工制作和深层特征具有高解释性和预测准确性的优势。本研究提出了一种新颖的评论有用性预测模型, 利用深度学习技术来考虑手工制作特征和深层特征之间的互补性。

首先, 采用先进的卷积神经网络从非结构化的评论文本中提取深层特征。其次, 本研究利用先前研究中提取的手工制作特征, 这些特征影响了评论的有用性并增强了其解释性。第三, 本研究将深层特征和手工制作特征结合到一个评论有用性预测模型中, 并使用Yelp.com数据集对其性能进行评估。为了衡量所提出模型的性能, 本研究使用了2,417,796条餐厅评论。

广泛的实验验证了所提出的方法优于传统的机器学习方法。此外, 通过实证分析, 本研究证实了结合手工制作和深层特征可以展现出更好的预测性能。

据我们所知, 这是首个在餐厅评论预测中应用深度学习技术, 并结合了结构化和非结构化数据来预测评论有用性的研究之一。此外, 本研究采用了先进的特征融合方法, 更好地利用了提取的特征信息, 并识别了特征之间的互补性。

This study, a conceptual paper, aims an answer the question, how significant cluster ambidexterity is for the resilience of individual clusters.

The authors draw up an abductive synopsis of empirical information and relevant theoretical sources. A case study is used to illustrate some of the findings.

The results of the analysis show that the ambidexterity of a cluster can contribute to its resilience when adverse external developments arise. Ambidexterity proves to be simultaneously a common strategy of key cluster actors and a mechanism for coping with critical situations and developments that can be activated by the cluster actors and may – eventually – lead to cluster resilience. While ambidexterity does not guarantee cluster survival, it can contribute significantly to their economic resilience under adverse conditions.

The concept is developed on a limited empirical basis and would need to be tested and deepened by comparing a wide range of case studies from different clusters.

A better understanding of the importance of ambidexterity for the development of industrial clusters contributes to a better fine-tuning of cluster support policies.

Ambidexterity as a concept originating from business administration has so far only been rudimentarily tapped for empirical and theoretical cluster research. The paper identifies and develops a path how this could be accomplished to a greater extent in the future.

Security assurance evaluation (SAE) is a well-established approach for assessing the effectiveness of security measures in systems. However, one aspect that is often overlooked in these evaluations is the assurance context in which they are conducted. This paper aims to explore the role of assurance context in system SAEs and proposes a conceptual model to integrate the assurance context into the evaluation process.

The conceptual model highlights the interrelationships between the various elements of the assurance context, including system boundaries, stakeholders, security concerns, regulatory compliance and assurance assumptions and regulatory compliance.

By introducing the proposed conceptual model, this research provides a framework for incorporating the assurance context into SAEs and offers insights into how it can influence the evaluation outcomes.

By delving into the concept of assurance context, this research seeks to shed light on how it influences the scope, methodologies and outcomes of assurance evaluations, ultimately enabling organizations to strengthen their system security postures and mitigate risks effectively.

Achieving social sustainability has become a critical challenge in global supply chain networks, particularly during complex crises such as terrorism. The purpose of this study is to explore how institutional forces influence the social sustainability approaches of logistics service providers (LSPs) in high terrorism-affected regions (HTAR). This then leads to investigating how the key factors interact with Institutional Theory.

An exploratory multiple-case study research method was used to investigate six cases of different-sized logistics LSPs, each in an HTAR. The data was collected using semistructured interviews and triangulated using on-site observations and document analysis. Thematic analysis was used in iterative cycles for cross-case comparisons and pattern matching.

The findings interact with Institutional Theory and the three final-order themes. First, management processes are driven by coopetition and innovation. Second, organizational resources, structure and culture lead to an ineffective organizational design. Finally, a lack of institutionalization creates institutional uncertainty. These factors are rooted in many other first-order factors such as information sharing, communication, relationship management, capacity development, new process developments, workforce characteristics, technology, microlevel culture and control aspects.

This study answers the call for social sustainability research and enriches the literature on social sustainability, Institutional Theory and LSPs in HTARs by providing illustrations showing that institutional forces act as driving forces for social sustainability initiatives by shaping the current management processes. Conversely, the same forces impede social sustainability initiatives by shaping the current organizational designs and increasing institutional uncertainty.

Supply chain finance (SCF) has become a vital ingredient that fosters growth and provides flexibility to the global supply chain. Thus, it becomes essential to understand the factors that contribute to the success of the supply chain finance ecosystem (SCFE). This study aims to identify the critical success factors (CSFs) for the development of an efficient and effective SCFE. Based on their characteristics, the study intends to classify the factors into constructs and further establish a hierarchical relationship among the CSFs.

The study is based on empirical data collected from 221 respondents based on administered questionnaires. Exploratory factor analysis (EFA) is carried out on 16 selected factors (out of 21 proposed factors) based on the feedback of the experts and the factors were classified into four constructs. The total interpretive structural modeling (TISM) model was developed by identifying and finalizing CSFs of the SCFE. The model developed a hierarchical relationship between the various factors.

The study identified significant CSFs for the efficient and effective SCF ecosystem. Four constructs were developed by analyzing CSFs using the EFA. The finalized 16 CSFs modeled through the TISM and further hierarchical relationship established between the CSFs concludes that governmental policies and sectoral growth are the strongest driving forces and financial attractiveness is the weakest driving force. Based on the CSFs and the constructs identified, it was found that for the success of the SCF ecosystem, the existence of an economic ecosystem provides a facilitating framework for the overall development of the SCFE. Also, the trustworthiness among the partners fosters better relationships and results in financial feasibility and offers business opportunities for all the stakeholders.

This study will help the SCF partners across the globe understand the CSFs that ensure development of mutually beneficial SCF ecosystems and provide flexibility to the supply chain partners. The CSFs would provide insights to the policymakers and the financial intermediaries for providing a conducive environment for the development of a better SCF ecosystem. Also, the buyers and sellers would understand the CSFs that would develop better relationships among them and ultimately help in development of business across the globe.

The study identifies the CSFs for the SCF ecosystem. The study ascertains the significant factors and classifies them into clusters using EFA. Unlike the literature available, the paper develops the hierarchical relationship between the CSFs and develops a model for an efficient and effective SCF ecosystem.