Search results

This study aims to analyze how ready a firm is to transform into Industry 4.0 using the Readiness Index (INDI 4.0) assessment. It also investigates the differences (before and after) of the program “Making Indonesia 4.0” in 2018 in socioeconomic and demographic aspects.

The INDI 4.0 assessment involved a self-evaluation by 622 companies across 13 industry sectors, subsequently verified by the Ministry of Industry. This study incorporates discussions with industry experts to enhance the interpretation of the analytical findings.

This study explores the interrelation among the components of INDI 4.0 across different levels, assessing the readiness of each sector for Industry 4.0. The findings reveal the diverse impact of implementing Industry 4.0 in Indonesia on socioeconomic and demographic aspects. Furthermore, the study proposes several policy recommendations for the Indonesian government’s consideration.

This study’s scope is confined to the industrial context of Indonesia, as the assessment components are tailored to the specific characteristics and culture of the country’s industry. Subsequent research endeavors can leverage this study as a foundational reference, adapting the components to align with the particular interests of other nations.

Businesses, especially those in Indonesia, can employ these findings to evaluate their position in the context of Industry 4.0 transformation compared to their industry. Simultaneously, the Indonesian government can use these results as a starting point to evaluate and potentially enhance their policies related to Industry 4.0. We recommend five policy proposals for the Indonesian government: diversifying measurement models, shifting terminology, emphasizing soft skills, promoting continuous learning and implementing Center of Digital Industry Indonesia 4.0 (PIDI 4.0) initiatives.

This study offers a broad impact of Industry 4.0 implementation in socioeconomic and demographic aspects in Indonesia, such as income, job-shifting, age, educational background and gender.

To the best of our knowledge, no prior research has explored the repercussions of industrial implementation on socioeconomic and demographic facets.

A maturity model for Industry 4.0 (I4.0 MM) with influencing factors is designed to address maturity issues in adopting Industry 4.0. Standardisation in I4.0 supports manufacturing industry transformation, forming reference architecture models (RAMs). This paper aligns key factors and maturity levels in I4.0 MMs with reputable I4.0 RAMs to enhance strategy for I4.0 transformation and implementation.

Three steps of alignment consist of the systematic literature review (SLR) method to study the current published high-quality I4.0 MMs, the taxonomy development of I4.0 influencing factors by adapting and implementing the categorisation of system theories and aligning I4.0 MMs with RAMs.

The study discovered that different I4.0 MMs lead to varied organisational interpretations. Challenges and insights arise when aligning I4.0 MMs with RAMs. Aligning MM levels with RAM stages is a crucial milestone in the journey toward I4.0 transformation. Evidence indicates that I4.0 MMs and RAMs often overlook the cultural domain.

Findings contribute to the literature on aligning capabilities with implementation strategies while employing I4.0 MMs and RAMs. We use five RAMs (RAMI4.0, NIST-SME, IMSA, IVRA and IIRA), and as a common limitation in SLR, there could be a subjective bias in reading and selecting literature.

To fully leverage the capabilities of RAMs as part of the I4.0 implementation strategy, companies should initiate the process by undertaking a thorough needs assessment using I4.0 MMs.

The novelty of this paper lies in being the first to examine the alignment of I4.0 MMs with established RAMs. It offers valuable insights for improving I4.0 implementation strategies, especially for companies using both MMs and RAMs in their transformation efforts.

Using the next-day and next-week returns of stocks in the Korean market, we examine the association of option volume ratios – i.e. the option-to-stock (O/S) ratio, which is the total volume of put options and call options scaled by total underlying equity volume, and the put-call (P/C) ratio, which is the put volume scaled by total put and call volume – with future returns. We find that O/S ratios are positively related to future returns, but P/C ratios have no significant association with returns. We calculate individual, institutional, and foreign investors’ option ratios to determine which ratios are significantly related to future returns and find that, for all investors, higher O/S ratios predict higher future returns. The predictability of P/C depends on the investors: institutional and individual investors’ P/C ratios are not related to returns, but foreign P/C predicts negative next-day returns. For net-buying O/S ratios, institutional net-buying put-to-stock ratios consistently predict negative future returns. Institutions’ buying and selling put ratios also predict returns. In short, institutional put-to-share ratios predict future returns when we use various option ratios, but individual option ratios do not.

The internationalization of business has grown the production value chains and created performance challenges for industrial production. Industry 4.0, the digital transformation of industrial processes, promises to deliver performance improvements through smart functionalities. This study investigates how digital transformation translates to performance gain by adopting a systems perspective to drive smartness.

This study uses qualitative research to collect data on the lived experiences of digital transformation practitioners for theory development. It uses semi-structured interviews with industry experts and applies the Gioia methodology for analysis.

The study determined that enterprise smartness is an organizational capability developed by digital transformation, it is a function of integration and the enabler of organizational performance gains in the Industry 4.0 context. The study determined that performance gains are experienced in productivity, sustainability, safety and customer experience, which represents performance metrics for Industry 4.0.

This study contributes a model that inserts smartness in the linkage between digital transformation and organizational outcomes to the digital transformation and production management literature.

The study indicates that digital transformation programs should focus on developing smartness rather than technology implementations, which must be considered an enabling activity.

Existing studies recognized the positive impact of technology on performance in industrial production. The study addresses a missing link in the Industry 4.0 value creation process. It adopts a systems perspective to establish the role of smartness in translating technology use to performance outcomes. Smart capabilities have been the critical missing link in the literature on harnessing digital transformation in organizations. The study advances theory development by contributing an Industry 4.0 value model that establishes a link between digital technologies, smartness and organizational performance.

This paper aims to discuss the emergence of science, technology and innovation (STI) institutions in Indonesia during the Dutch East Indies colonial period in 1778–1941. The emergence of these institutions reflected the dynamics of science and technology development and innovation in that era.

This paper navigates a historiographical approach. Data collection techniques use “secondary data research,” with archival investigation published by official sources in the Dutch East Indies in the 18th and 19th centuries as well as other reference sources, and data analysis techniques use “supplementary analysis.”

This research indicates that the STI institution during the Dutch East Indies colonial period was formed to maximize the natural resources of the Dutch East Indies. The STI institution at that time was constructed as part of Buitenzorg’s Plantentuin the lands, plantations, solutions for health, astronomy, geology, forestry and culture.

The limitations of this research, as well as future research. Relying too much on “secondary data” is a limitation of this study. Therefore, it is necessary to collect primary data through in-depth interviews with historical scientists studying STIs in Indonesia in future research.

This study, to the best of the authors’ knowledge, considered the first study, reveals the dynamics of STI in Indonesia during the Dutch East Indies colonial era by examining the dynamics of the institution. In addition, this study succeeded in dividing five institutional STI clusters in the Dutch East Indies Colonial period 1778–1941, namely, units/institutions formed as part of Planuntungin te Buitenzorg; units/institutions formed based on plantations, initiated by private plantations to find solutions to the pests and diseases that attack their crops; units/institutions formed to seek solutions in the health sector; units/institutions formed based on astronomy, geology and forestry; and units/ institutions regarded as scientific councils/associations.

This exploratory research examined how emphasizing a brand’s unethical behaviour through high moral intensity news framing influences consumer boycott intention.

The hypotheses were tested and validated using two experimental studies that expose customers of real retail and personal care product brands to news articles that have high and low moral intensity news frames.

The results showed high moral intensity news framing’s positive effect on consumer boycott intention. The frame’s influence is moderated by moral awareness and partially mediated by perceived moral intensity and moral judgement. The findings suggest that consumers’ perception of the frame and their attitude towards the brand will have a substantial role in boycott intention.

These research outcomes aid in the understanding of news framing effects on boycott intention, providing both insights for consumer activists and managerial implications for stewards of brands.

While previous research have examined the impact of news frames on the typical audience, there has been relatively little focus on news framing’s impact on consumers and their decision to boycott brands. This study addresses this gap by applying the work on emphasis framing to a consumer decision-making context. It also introduces moral intensity framing to the news frame classification. In addition, this study expands current conceptualizations of individual ethical decision-making to help explain consumer boycott intent.

The purpose of this study is to examine the sociocultural implications of caring for persons with COVID-19 in a developing country context.

In total, 156 nurses participated in the study. Stratified random sampling methodology was used. Data were collected via online self-administered questionnaire. Descriptive and inferential statistics, including ANOVA tests were done.

Nurses experienced stigmatization, discrimination and reduced income. Nurses functioned on the frontline during the COVID-19 pandemic and encountered negative sociocultural experiences from a personal, social and professional perspective. ANOVA showed statistically significant relationships between the conflicts between their work role, family commitments and level of physical interactions with a number of variables.

Data were collected from one Regional Health Authority and may not be representative of the national population of nurses. Further, as the researchers depended on gatekeepers to access participants, the recruitment process may not have been entirely based on randomization as originally agreed.

The findings from this study can be used as a framework to develop context specific programmes and policies to support health professionals, including nurses.

Pandemics, while not new, contribute to serious sociocultural challenges for individuals and families, as well as nurses, as part of their professional roles. In this regard, maintaining effective social networks must be central to effective functioning in crisis situations, such as pandemics.

Nurses have played a key role, working both to identify, isolate and manage those with COVID-19 and supporting those who have non-COVID-19 related health needs. While nurses have been at the forefront delivering care in these uncertain times, doing so puts them at great risk, for not only contracting COVID-19 but also for experiencing negative psychosocial effects that may be due to the nature of their jobs.

This paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the I-Optimal design.

I-optimal design methodology is used to plan the experiments by means of Minitab-17.1 software. Samples are manufactured using Stratsys FDM 400mc and tested as per ISO standards. Additionally, an artificial neural network model was developed and compared to the regression model in order to select an appropriate model for optimisation. Finally, the genetic algorithm (GA) solver is executed for improvement of tensile strength of FDM built PC components.

This study demonstrates that the selected process parameters (raster angle, raster to raster air gap, build orientation about Y axis and the number of contours) had significant effect on tensile strength with raster angle being the most influential factor. Increasing the build orientation about Y axis produced specimens with compact structures that resulted in improved fracture resistance.

The fitted regression model has a p-value less than 0.05 which suggests that the model terms significantly represent the tensile strength of PC samples. Further, from the normal probability plot it was found that the residuals follow a straight line, thus the developed model provides adequate predictions. Furthermore, from the validation runs, a close agreement between the predicted and actual values was seen along the reference line which further supports satisfactory model predictions.

This study successfully investigated the effects of the selected process parameters - raster angle, raster to raster air gap, build orientation about Y axis and the number of contours - on tensile strength of PC samples utilising the I-optimal design and ANOVA. In addition, for prediction of the part strength, regression and ANN models were developed. The selected ANN model was optimised using the GA-solver for determination of optimal parameter settings.

The proposed ANN-GA approach is more appropriate to establish the non-linear relationship between the selected process parameters and tensile strength. Further, the proposed ANN-GA methodology can assist in manufacture of various industrial products with Nylon, polyethylene terephthalate glycol (PETG) and PET as new 3DP materials.

The purpose of this paper is to explore a new approach to solve the problem of positive and negative offset in the calculation process of integral elements, then propose a series of new grey relational degree model for cross sequences.

The definitions of cross sequences and area elements have been proposed at first. Then the concept of difference degree between sequences has been put forward. Based on the definition of difference degree between sequences, various modified grey relational degree models for cross sequences have been proposed to solve the measurement problem of cross sequence correlation relationships.

(1) The new definition of cross sequences; (2) The area element; (3) Various modified grey relational degree models for cross sequences based on the definition of difference degree between sequences.

The grey relational analysis model of cross sequences is a difficult problem in grey relational analysis. The new model proposed in this article can effectively avoid the calculation deviation of grey relational analysis model for cross sequences, and reasonably measure the correlation between cross sequences. The new model was used to analyse the food consumer price index in Shaanxi Province, clarifying the relationship between different types of food consumer price indices, some interesting results that are not completely consistent with general economic theory were obtained.

The new definition of cross sequences, the area element and various modified grey relational degree models for cross sequences were proposed.

Using the lenses of social exchange and reactance theories, this study examines the relationships between abusive supervision and both turnover intentions and job satisfaction. The moderating role of employee depression in the relationship between abusive supervision and these specific work outcomes is also investigated, by incorporating the conservation of resources theory.

Quantitative data were collected from a sample of 221 frontline retail employees, across shopping malls in the Caribbean nation of Trinidad. A 3-step multiple hierarchical regression analysis was performed to test the relationships.

The findings provided support for the propositions that abusive supervision predicts job satisfaction and turnover intentions, respectively. Employee depression moderated the relationship between abusive supervision and job satisfaction but did not moderate the relationship between abusive supervision and turnover intentions.

While existing research has explored the relationships between abusive supervision, job satisfaction and turnover intentions, limited studies have investigated the moderating role of employee depression. This study contributes to understanding this pervasive workplace issue by investigating a relatively unexplored moderating effect.