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The rising incidence of non-communicable diseases and sugar-sweetened beverages (SSB) consumption in Indonesia is a concern to the central government. However, the excise tax policy proposal for SSBs, introduced by the Minister of Finance in a 2020 meeting with the Parliament, remains unapproved until 2023. This study analyzes the process and factors influencing stakeholders in managing the implementation of the excise tax policy on SSBs in Indonesia.

This research is based on Kingdon’s Multiple Streams Theory (MST), focusing on the problem, policy and politics streams. It employs a qualitative design and combines a literature review and in-depth interviews in data collection. Interviews were conducted with policymakers, civil servants, senior representatives from nongovernmental health and consumer organizations, trade associations and academics in nutrition, health and taxation.

We identified that policy entrepreneurs have not succeeded in aligning the policy with the ideology of decision-makers and broader government goals. Therefore, the Parliament has not ratified the SSB excise tax policy until 2023. The effectiveness of interventions is expected to be the primary driver of policy adoption. Despite the complexity of this policy process, we conclude that implementing some reinforcing strategies may be necessary to facilitate policy change. In the Indonesian context, other actors who proactively manage potential criticism from multiple stakeholders are needed. The power of the Coordinating Ministry for Economic Affairs is expected to navigate political complexities and interests among stakeholders. Support for adopting the SSB excise tax policy will likely rise when framing policy alternatives aligns with decision-makers values, involves coordination between ministries, and has a broad public appeal.

Despite the constitutional mandate to regulate levies through legislation, there is no research on policy processes in Indonesia, specifically regarding levies and taxes. Examining the Indonesian institutional context in which a policy process takes place may improve the understanding of the many components of MST under unique conditions, providing insights into the specific factors that drive policy change in certain cases.

Hybrid organizations offer an innovative approach to promote social impact. However, hybrids face the challenge of reconciling the dual mission (social/financial). The purpose is to understand how hybrids and information and communication technologies (ICT) interact, unveiling opportunities ICT offers for hybrids regarding the dual mission.

We used affordance theory and adopted a predominantly inductive approach inspired by the so-called “Gioia template.” The research design was based on semi-structured interviews with entrepreneurs, specialists, and people working in institutes, foundations, and accelerators, i.e. social actors operating in the hybrid organizational ecosystem in Brazil.

Our findings suggest that the affordances of the relationship between organizational and ICT resources act as facilitators. A theoretical contribution is conceptualizing “affordance of conciliation,” indicating how ICT resources might facilitate achieving social/financial goals, thus minimizing efforts to reconcile mission duality. Furthermore, we list categories and aggregate dimensions and elucidate how results aligned with goals are generated through the process-based model. We show that ICT has a significant role in helping hybrids overcome challenges.

Our results extend affordance theory with theoretical and practical implications. We highlight fundamental components that contribute to proposing the new concept of “affordance of conciliation.” We contribute to information systems literature by better understanding the social interactions between ICT and hybrids. Finally, we help hybrids understand the support of ICT resources to fulfill their dual mission.

Project bundling is an innovative practice that groups or bundles several infrastructure projects into a single contract. While project bundling has various benefits, agencies are facing some challenges when bundling their projects, including properly assessing the feasibility (or infeasibility) of project delivery methods (PDMs) of interest. More specifically, project owners face the challenge of properly selecting between traditional and alternative PDMs for their bundled projects. Although some research efforts were devoted to providing guidelines in relation to different aspects related to project bundling, no previous study was conducted to help project owners performing PDMs-related feasibility analysis for bundled projects, which differ from normal, singly delivered projects. To fill this knowledge gap, this paper develops a decision-support tool that assists agencies in deciding whether they should select a traditional or alternative PDM (i.e. whether to go with the Design-Bid-Build (DBB) PDM or not) for their bundled projects.

An analytical methodology comprised of four main steps was followed in this paper. First, an expert survey was developed and distributed to industry experts to quantify the importance of 25 project bundling objectives. Second, principal component analysis was used to determine the weights for the different project bundling objectives. Third, a series of statistical tests was implemented to identify different feasibility tiers. Fourth, a user-friendly decision-support tool was developed, and its capabilities were demonstrated.

The results showed that six tiers exist to classify the feasibility (or infeasibility) of traditional PDMs (i.e. the DBB method) for bundled projects. The research outcomes have also reflected that the following five project bundling objectives contribute the most to making traditional PDMs (i.e. the DBB method) more feasible for bundled projects: (1) Having well-defined design features; (2) Requiring prior knowledge or experience with similar project size and scope; (3) Completing the overall project on schedule; (4) Keeping rate of expenditures within cash flow plan; and (5) Acquiring specific legislative, regulatory and jurisdictional requirements early on.

This research adds to the body of knowledge by equipping agencies and project owners with a decision-support system that helps them identify whether traditional or alternative PDMs are more appropriate for the specific objectives of their bundling program(s). By making the right PDM decision, project owners can enhance their bundling practices (especially in relation to the PDM proper selection) and ultimately the performance of their bundled projects.

The purpose of the present study was to identify the conditions that affect the absorptive capacity (AC) of knowledge, discover its consequences and design an AC paradigm pattern in public academic libraries. To do so, AC was investigated at the levels of individual and organizational learning.

In this paper, to answer the three major questions of this qualitative-survey study, the views of 24 experts were examined using an in-depth semi-structured interview and grounded theory strategy. The data were collected using a nonrandom combined targeted sampling procedure (targeted and snowball) and analyzed based on Strauss and Corbin’s (1998) approach and utilizing MAX Qualitative Data Analysis (MAXQDA) software.

Throughout the grounded analytic stages, 121 open codes were identified. These were distributed around the AC axial category in academic libraries in terms of individual and organizational learning. The 33 axial concepts were then categorized into 16 selective general categories. The paradigm pattern was initially designed to explore the relationships between causal, intervening, strategies, context and consequences conditions. The transition from academic libraries to learning organizations, their increased functional value and the development of their innovation were identified as the consequences of AC development.

This qualitative research is the first in the field of Library and Information Science (LIS) to support AC both theoretically and empirically in terms of learning in academic libraries. This study thus not only addresses a key research gap in LIS but also provides significant insights and direction to interested librarians and researchers.

India has witnessed a significant surge in internet users in recent years, creating an ideal environment for E-entrepreneurship. With the rise of E-commerce and the growth of the digital economy, there is tremendous potential for online businesses in developing nations. Our study outlines the behavioral, cognitive, and environmental aspects shaping students' E-entrepreneurial intentions (EEI).

Our study incorporated structural equation modeling (SEM), social cognitive theory (SCT), and entrepreneurial event model (EEM) to evaluate the EEI of 460 students from India's top five engineering institutes. The direct and indirect linkages in the model were examined by employing mediation and moderation analysis.

Our findings reveal that behavioral, environmental, and cognitive factors facilitate evaluating feasibility, further igniting students' EEI. The cognitive factors and E-entrepreneurial feasibility (EEF) mediate the relationship between the underlying constructs. Furthermore, financial resource availability (FRA) strengthens, and loss aversion bias(LAB) weakens the linkage between EEF and EEM.

The study's findings may benefit online innovation communities, potential technopreneurs, financial institutions, and policymakers in improving the entrepreneurial ecosystem.

The study integrates psychological and sociological perspectives to understand the key facilitators of EEF and EEI. The study combines SCT and the EEM by appending crucial constructs such as FRA and LAB to broaden the horizons of EEI.

The purpose of this paper was to first identify and then model the impact of critical success factors (CSFs) of public–private partnership (PPP) power projects.

Review of empirical literature came out with 20 CSFs which were ranked by experts and industry practitioners through a two-round Delphi questionnaire survey.

These CSFs were grouped into CSF groups (CSFGs) using component analysis, and they served as the input variables for fuzzy analysis. The six components were collaboration and transparency, guarantee and permits, socio-political support, expected profitability, technical feasibility and risk allocation (RA). Overall success index of PPP power projects in Ghana was 5.966 and showed there is high impact of CSFGs on project success. Fuzzy analysis also confirmed RA as the most significant CSFG.

The model developed can serve as a multi-dimension CSF framework that can be used as a success attainment tool for PPP power projects. For policy developers and stakeholders, the model serves as a pointer to issues which the government/public sector must focus on to attract huge investments from the private sector in the power sector.

The purpose of the study is to propose a normative approach for market segmentation, profile and monitoring using computing and information technology to analyze User-Generated Content (UGC).

The specific steps include performing a structural analysis of the UGC and extracting the base variables and values from it, generating a consumer characteristics matrix for segmenting process, and finally describing the segments' preferences, regional and dynamic characteristics. The authors verify the feasibility of the method with publicly available data. The external validity of the method is also tested through questionnaires and product regional sales data.

The authors apply the proposed methodology to analyze 53,526 UGCs in the New Energy Vehicle (NEV) market and classify consumers into four segments: Brand-Value Suitors (32%), Rational Consumers (21%), High-Quality Fanciers (26%) and Utility-driven Consumers (21%). The authors describe four segments' preferences, dynamic changes over the past six years and regional characteristics among China's top five sales cities. Then, the authors verify the external validity of the methodology through a questionnaire survey and actual NEV sales in China.

The proposed method enables companies to utilize computing and information technology to understand the market structure and grasp the dynamic trends of market segments, which assists them in developing R&D and marketing plans.

This study contributes to the research on UGC-based universal market segmentation methods. In addition, the proposed UGC structural analysis algorithm implements a more fine-grained data analysis.

The smart contract provides an opportunity to improve existing contract management practices in the construction projects by replacing traditional contracts. However, translating the contracts into computer languages is considered a major challenge which has not been investigated. Thus, it is necessary to: (1) identify the obstructing clauses in real-world contracts; and (2) analyze the replacement's technical and economic feasibility. This paper aims to discuss the aforementioned objectives.

This study identified the flexibility clauses of traditional contracts and their corresponding functions through inductive content analysis with representative standard contracts as materials. Through a speculative analysis in accordance to design science paradigm and new institutional economics, the economic and technical feasibility of existing approaches, including enumeration method, fuzzy algorithm, rough sets theory, machine learning and artificial intelligence, to transform respective clauses (functions) into executable codes are analyzed.

The clauses of semantic flexibility and structural flexibility are identified from the contracts. The transformation of semantic flexibility is economically and/or technically infeasible with existing methods and materials. But with more data as materials and methods of rough sets or machine learning, the transformation can be feasible. The transformation of structural flexibility is technically possible however economically unacceptable.

Given smart contracts' inability to provide the required flexibility for construction projects, smart contracts will be more effective in less relational contracts. For construction contracts, the combination of smart contracts and traditional contracts is recommended. In the long run, with the sharing or trading of data in the industry level and the integration of machine learning or artificial intelligence reducing relevant costs, the automation of contract management can be achieved.

This study contributes to the understanding of the smart contract's limitations in industry scenarios and its role in construction project management.

This paper aims to address the challenge raised in the literature regarding whether and how digitalization supports a servitized new product development (NPD) process, considering the customer’s involvement from the early stage of the process.

Pragmatic constructivism (PC) has been adopted for conceptualizing the NPD process as the construction of a new reality. PC is the method theory used for interpreting the field evidence drawn from a qualitative case study carried out at a multinational company operating in the semiconductor industry.

This study shows how digitalization supports the alignment to the overarching topoi of the company servitization strategy by enabling the integration and merging of different organizational topoi during the NPD process.

This study is confined to a single-case study and context.

The results of this study are relevant for managers involved in the stage-gate product development of manufacturing companies, informing them on how the use of digital tools enables or hinders the progression of product development projects.

This paper contributes to the servitization literature by offering field evidence that demonstrates the importance for manufacturing firms of acquiring customer feedback from an early NPD phase. Another contribution is related to the literature on the role of digitalization in NPD processes, describing how digital tools give support during the different phases of the NPD process.

The number of construction dispute cases has maintained a high growth trend in recent years. The effective exploration and management of construction contract risk can directly promote the overall performance of the project life cycle. The miss of clauses may result in a failure to match with standard contracts. If the contract, modified by the owner, omits key clauses, potential disputes may lead to contractors paying substantial compensation. Therefore, the identification of construction project contract missing clauses has heavily relied on the manual review technique, which is inefficient and highly restricted by personnel experience. The existing intelligent means only work for the contract query and storage. It is urgent to raise the level of intelligence for contract clause management. Therefore, this paper aims to propose an intelligent method to detect construction project contract missing clauses based on Natural Language Processing (NLP) and deep learning technology.

A complete classification scheme of contract clauses is designed based on NLP. First, construction contract texts are pre-processed and converted from unstructured natural language into structured digital vector form. Following the initial categorization, a multi-label classification of long text construction contract clauses is designed to preliminary identify whether the clause labels are missing. After the multi-label clause missing detection, the authors implement a clause similarity algorithm by creatively integrating the image detection thought, MatchPyramid model, with BERT to identify missing substantial content in the contract clauses.

1,322 construction project contracts were tested. Results showed that the accuracy of multi-label classification could reach 93%, the accuracy of similarity matching can reach 83%, and the recall rate and F1 mean of both can reach more than 0.7. The experimental results verify the feasibility of intelligently detecting contract risk through the NLP-based method to some extent.

NLP is adept at recognizing textual content and has shown promising results in some contract processing applications. However, the mostly used approaches of its utilization for risk detection in construction contract clauses predominantly are rule-based, which encounter challenges when handling intricate and lengthy engineering contracts. This paper introduces an NLP technique based on deep learning which reduces manual intervention and can autonomously identify and tag types of contractual deficiencies, aligning with the evolving complexities anticipated in future construction contracts. Moreover, this method achieves the recognition of extended contract clause texts. Ultimately, this approach boasts versatility; users simply need to adjust parameters such as segmentation based on language categories to detect omissions in contract clauses of diverse languages.