Production Managers Research Articles

The influence of unfamiliar ingredients on the greenness evaluation of environmentally friendly products

PurposeThe purpose of this study is to assist brand and product managers in selecting appropriate ingredient names for environmentally friendly products. It investigates the effects of unfamiliar ingredients on consumers’ evaluations of environmental friendliness and their purchase intentions, based on the cue consistency theory.Design/methodology/approachFive experimental studies (n = 968) were conducted to achieve the research objectives. Study 1 found that consumers tended to avoid choosing unfamiliar ingredients. Study 2 examined the impact of ingredient familiarity on consumers’ perceived greenness. Study 3 investigated the mediating role of perceived naturalness. Studies 4 and 5, respectively, explored the moderating effects of emphasizing the importance of technology in environmental conservation and product category.FindingsThe findings indicate that when environmentally friendly products are labeled with unfamiliar ingredients (vs. familiar), consumers’ perceived greenness and purchase intentions decrease. This effect is mediated by perceived naturalness. Moreover, the negative impact of unfamiliar ingredients is mitigated by emphasizing the importance of technology and the high-tech product category.Originality/valueThis paper reveals the unique role of unfamiliar ingredients in shaping consumer attitudes toward environmentally friendly products. Based on cue consistency theory, it uncovers how unfamiliar ingredients influence the perceived greenness of environmentally friendly products through perceived naturalness. Furthermore, the paper demonstrates the impact of emphasizing the importance of technology (emphasis vs. control) and product category (high-tech vs. low-tech) on consumer attitudes and behaviors toward environmentally friendly products.

Product managers in software startups: A grounded theory

Context:Defining and designing a software product is not merely a technical endeavor, but also a socio-technical journey. As such, its success is associated with human-related aspects, such as the value users perceive. To handle this issue, the product manager role has become more evident in software-intensive companies. A unique, challenging context for these professionals is constituted by software startups, emerging companies developing novel solutions looking for sustainable and scalable business models. Objective:This study aims to describe the role of product managers in the context of software startups. Method:We performed a Socio-Technical Grounded Theory study using data from blog posts and interviews. Results:The results describe the product manager as a multidisciplinary, general role, not only guiding the product by developing its vision but also as a connector that emerges in a growing company, enabling communication of software development with other areas, mainly business and user experience. The professional performing this role has a background in one of these areas but a broad knowledge and understanding of key concepts of the other areas is needed. We also describe how differences of this role to other lead roles are perceived in practice. Conclusions:Our findings represent several implications for research, such as better understanding of the role transformation in growing software startups, practice, e.g., identifying the points to which a professional migrating to this role should pay attention, and the education of future software developers, by suggesting the inclusion of related topics in the education and training of future software engineers.

Strategic Implementation of Big Data Automation for Wastage Management Reporting Using Analytical Hierarchy Process in The Tobacco Industry

In today's data-driven era, big data automation is crucial, often referred to as "the new oil." Industries, particularly the fast-moving consumer goods (FMCG) sector like the tobacco industry, must undergo digital transformation to stay competitive. The integration of big data automation with reporting processes is significantly correlated, as it can automate repetitive reporting tasks, enhancing efficiency. This automation enables decision-makers to make faster and more accurate decisions. This research focuses on assessing the capacity and factors involved in the collaboration between the operations department and the digital team to automate repetitive reporting processes by integrating big data from various sources such as SAP and Microsoft Forms. The study employs a combination of qualitative and quantitative methods, along with the Analytic Hierarchy Process (AHP), to identify optimal business solutions. Insights from this research prioritize big data automation and reporting projects to meet business needs. Results indicate among four alternative project groups, the Central Data Wastage project is the top priority with a score of 51.7%, followed by SMD Wastage at 25.2%, PMD Wastage at 14.7%, and FMD Wastage at 8.4%. Five stakeholders participated in this research, including a product manager, business user, business analyst, and two developers. These participants contributed to assessing criteria, sub-criteria, and alternative project groups. This research not only helps prioritize projects but also facilitates seamless digitalization within the operations team, fostering synergy with the digital team.

Huge urgency and consummate skill: an interview with Jim Pennington

ABSTRACT Jim Pennington is a printer and small press publisher who trained at London's North-Western Polytechnic and the London College of Printing. Over the course of his long career, he has worked as a print room and production manager first for the British Safety Council (1971–75) then for the anti-poverty charity War on Want (1975–79) and then for the Lithosphere Printing Co-operative (1979–1991), among others. In parallel, Pennington ran Aloes Books, the influential small press he established with the poets Allen Fisher and Dique Miller. Aloes Books has published key works by Kathy Acker, Thomas Pynchon and William Burroughs, among many others.

Research on dynamic control technology of production plan based on modified BOM

Abstract This paper describes a dynamic control technology of production planning based on modified Bom. Firstly, the modified BOM information is constructed based on the reservation relation after the MRP operation. Then, based on the planning network graph model, the critical task node and its critical path are found, and the running status is monitored in real time. The critical path is modified dynamically, and the method is proved to be scientific and feasible by an example. In this paper, an intuitionistic and dynamically updated plan network chart is generated by the system’s automatic mapping, which is used to guide the production managers in making plans, adjusting plans, reporting progress, and performing other daily work. In addition, the critical path technology is applied to assist the production decision-maker in identifying the key tasks that need the key support, thus further improving the production management and control ability of the enterprise, which has very good social benefits.

Social systems for future manufacturing framework: An overarching view of people, organization and society

Drawing on insights from relevant scholarship in the areas of producing engineering, sociology of work and political economy, as well as a series of ‘webinars’ with R&D managers, production managers and trade union representatives conducted between 2021 and 22, we propose an interdisciplinary, multidimensional framework (‘social systems for future manufacturing’) as a means to better understand the shopfloor and societal implications of technological change in the context of the ‘fourth industrial revolution’. Our framework entails three distinct though interrelated levels of analysis (worker, manufacturing, as well as societal), all of which are necessary to engage with in order to understand the implications of digital transformation. We argue that the extent to which employee voice is institutionally embedded at societal level, will have an impact on the way in which new technology will be developed and implemented (namely in a labour-saving or labouraugmenting way). We also discuss relevant factors in terms of workforce skills development at individual and firm level, as well as in terms of national vocational training and development infrastructure, and how these are likely to determine industrial competitiveness in the context of the fourth industrial revolution. Theoretical and managerial implications around work organisation and workforce training are discussed.

SELECTION OF BEST 3D PRINTING PARAMETERS FOR FUSED DEPOSITION MODELING PART USING A NOVEL HYBRID MULTI-CRITERIA OPTIMIZATION TECHNIQUE

One of the most widely used 3D printing techniques is fused deposition modeling (FDM) which builds things layer by layer by dispensing molten materials through a heated nozzle. To showcase the flexibility of 3D printing, test samples were made using polylactic acid (PLA). Trial runs with Taguchi’s (L27) orthogonal array were performed. Layer thickness, printing speed, and carbon deposition (C-deposition) were the three input parameters that were improved. This was accomplished by combining principal component analysis (PCA) with multi-objective optimization based on ratio analysis (MOORA) in an integrated approach to multi-criteria optimization. The main goal of this study is to maximize the input parameters for the Industry 4.0 technological production process of embossing components. The MOORA-PCA technique finds the best combinations of process variables; for example, printing at a speed of 75[Formula: see text]mm/s, layer thickness of 0.1[Formula: see text]mm, and carbon content of 15[Formula: see text]mg yield the required results. The results of this study will help production managers and researchers choose the best FDM 3D printing methods for improving mechanical qualities and surface roughness. The economic feasibility of the 3D printing businesses may be strengthened by these research findings, which will benefit customers looking for ecologically friendly items.

Innovating the Future: Unveiling the Initial Iteration of the Pragmatic Framework for Product Managers

Context: This study introduces the Pragmatic Framework for Product Managers, a tool developed to enhance the understanding and application of product management activities. Objectives: The aim is to provide a comprehensive overview of Product Manager (PM) activities that positively impact efficiency, business growth, budget control, user satisfaction, and release processes. The framework is intended to aid decision-making, training, and clarifying the PM role, ultimately contributing to product success. Methods: A systematic literature review of 134 studies was conducted to develop the PFPM. This extensive research led to identifying and classifying 122 activities into 33 categories within 6 domains, forming a robust framework for product managers. Results: The PFPM, in its initial iteration, represents a minimal viable product of the framework. The research findings highlight the framework’s potential for future refinement, particularly in the context of software startups. Conclusion: The PFPM significantly affects software companies' product decision-making, PM training, and role transparency. It is a valuable resource for researchers and practitioners in Software Product Management (SPM), Requirement Engineering (RE), New Product Development (NPD), and innovation. The framework paves the way for future studies focused on the unique dynamics of PM activities in the software startup ecosystem.

Visual effect of air pollution on the need for arousal and variety-seeking behavior

Research on air pollution, one of the most common environmental factors, has primarily focused on its effects on physical, mental, and cognitive health. However, air pollution-induced achromatic color of an environment, which is a prominent feature of air pollution, has received little attention. This study explored the visual effects of air pollution on the variety-seeking purchase behavior of consumers through two scenario-based experiments and primed manipulation (Study 1 and Study 2) and one natural experiment using data from a local fruit chain store (Study 3). Study 1 tested the main effect of air pollution on the variety-seeking behavior and found that primed air pollution increased variety-seeking when consumers purchased beverages. Study 2 broadened the category and tested the mechanism, and the results indicated that primed air pollution increased the variety of purchased chocolates and demonstrated the mediating effect of the need for arousal. Study 3 tested the boundary condition and extended the external validity with actual purchases. The results revealed that severe air pollution increased the purchased SKUs by 22.9% and visibility reduced the moderation effect. This research extended the literature on the visual effect of air pollution by providing evidence of the effects of air pollution on variety-seeking behavior through the need for arousal. And, product managers could leverage the results by offering a greater variety of goods on days with air pollution to increase sales.

Consumers’ motivation to purchase second-hand clothing: a multimethod investigation anchored on belief elicitation and theory of planned behavior

PurposeAmidst escalating sustainability challenges, product and brand managers face a pressing need to foster responsible consumption and marketing strategies. Guided by the theory of planned behavior, this paper aims to explore consumers’ motivation to purchase second-hand clothing, a type of product that contributes to Sustainable Development Goal (SDG) 12 on Responsible Consumption and Production by democratizing the brand, extending the life-cycle of the product, promoting a circular economy, while reducing economic costs for consumers and environmental costs for companies.Design/methodology/approachA two-stage study was conducted: 20 consumers were initially interviewed to identify the salient beliefs about second-hand clothing, and following that, a survey was conducted with 449 consumers to statistically analyze consumers’ motivation to purchase second-hand clothing. The data were analyzed using partial least squares-structural equation modeling (PLS-SEM) and necessary condition analysis (NCA).FindingsFrom a “should-have” perspective (PLS-SEM), the study reveals that behavioral beliefs, injunctive normative beliefs, descriptive normative beliefs and control beliefs positively shape attitudes, injunctive norms, descriptive norms and perceived behavioral control toward second-hand clothing, whereas attitudes, injunctive norms, moral norms and perceived behavioral control positively influence consumers’ purchases of second-hand clothing. From a “must-have” perspective (NCA), the study shows that behavioral beliefs, injunctive normative beliefs and descriptive normative beliefs are necessary conditions to positively shape attitudes, injunctive norms and descriptive norms toward second-hand clothing, whereas attitudes, injunctive norms and perceived behavioral control are necessary conditions to stimulate second-hand clothing purchases.Originality/valueThe study offers a deep dive into consumers’ motivation to purchase second-hand clothing using a multimethod approach that enables not only the elicitation of salient beliefs (through interviews) but also the empirical examination of these beliefs alongside varying subjective norms in motivating consumers to purchase second-hand clothing (via survey). Given that beliefs are deeply rooted, the rigorous unfolding and validation of consumers’ beliefs about second-hand clothing, including the “should-haves” versus the “must-haves,” provide finer-grained insights that product and brand managers can strategically use to encourage consumers to purchase second-hand clothing.

THE MEDIATING ROLE OF MARKETING CAPABILITY AND INNOVATION CAPABILITY IN THE LINK BETWEEN STRATEGIC ORIENTATION DIMENSIONS AND BUSINESS PERFORMANCE(IN THE CASE OF SELECTED MANUFACTURING COMPANIES OPERATING IN ETHIOPIA)

<p>The purpose of this study is to determine the mediating role of Marketing and Innovation Capabilities in the link between Strategic Orientation dimensions and Business Performance. 385 questionnaires were distributed to CEOS, marketing managers & marketing experts, and brand managers, production managers, finance managers, IT specialists, research & development managers of 20 manufacturing firms and only 300 questionnaires were used for final analysis. After confirming that the CFA measurement models fulfill the GOF statistics, the discriminant validity analysis results show that the AVE values exceeded their respective square inter-construct correlations in several cases. Following Exploratory Factor Analysis (EFA) to investigate the underlying theoretical structure of the phenomenon and Confirmatory Factors Analysis (CFA) measurement models that meet the Goodness-of-Fit (GoF) statistics, the discriminant validity analysis results show that the AVE values are more significant than their respective square inter-construct correlations in several cases. The reliability test for all seven constructs is above 0.7, which satisfies the recommended threshold in the literature. The statistical software utilized for data analysis in this study was PROCESS Macro 3.4.1 and SPSS 25. The study's findings revealed that all four dimensions of strategic orientation and marketing and innovation capabilities had a favorable and significant impact on business performance. Both marketing and innovation capabilities have mediated the relationship between strategic orientation components (MO, EO, TO, and KO) and business performance. This study concludes that focusing solely on strategic components is insufficient and that integrated organizational processes are required to attain truly outstanding business performance. The study discovered that greater marketing and innovation capabilities enhance business performance (both financial and marketing). As a result, manufacturing businesses should examine each component of strategic orientation independently - market, entrepreneurial, technological, and knowledge orientations - while assessing fundamental abilities, which include marketing and innovation capabilities.</p><p> </p><p><strong>JEL:</strong> M31, O31, O32, O36, L25, L26, L60</p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0187/a.php" alt="Hit counter" /></p>

Learning outcome evaluation in manual assembly

Mass customization and shorter product life cycles are causing ever more variants in production, especially in manual assembly. At the same time, more diverse personnel structures are emerging due to demographic change and labor shortages. This is causing different challenges to production managers, e.g., competence gaps. To meet these challenges, learning in manual assembly becomes increasingly important. The design of the learning process can only be improved by checking whether the processes fulfill their purpose. Various learning evaluation measures are described in general vocational education and competence development, but it is hard to select the right one for the learning process. This paper shows a procedure, how learning evaluation measures can be selected, and how they can measure learning progress. For this, a test person study was conducted to compare different learning evaluation measures and show their usability and advantages in manual assembly. The results support making learning in assembly easier to apply and controllable. In the long term, feeding back the results improves the learning process design.

Critical Role of Manufacturing Execution Systems in Digital Transformation of Manufacturing Industry

The manufacturing industry is undergoing a significant digital transformation, propelled by advancements in technology. Central to this evolution is the implementation of Manufacturing Execution Systems (MES), which act as a vital link between the shop floor and enterprise-level systems. This paper explores the critical role of MES in the digital transformation of the manufacturing industry, focusing on how it enhances operational efficiency, improves product quality, and enables real-time decision-making. Manufacturing Execution Systems are software platforms designed to monitor, control, and optimize production processes from the initiation of orders to the delivery of finished goods. MES provides real-time data on production activities, enabling manufacturers to make informed decisions, reduce production costs, and increase overall productivity. The integration of MES with advanced technologies such as the Internet of Things (IoT), Big Data analytics, and cloud computing has further amplified its impact, leading to smarter and more agile manufacturing processes. One of the key benefits of MES is its ability to improve operational efficiency. MES offers real-time visibility into the production process, allowing manufacturers to identify and resolve bottlenecks, minimize downtime, and optimize resource utilization. Predictive maintenance capabilities, enabled by IoT integration, prevent unexpected equipment failures and reduce maintenance costs. MES also plays a crucial role in enhancing product quality. By monitoring each stage of production, MES ensures adherence to quality standards and facilitates traceability. In the event of a quality issue, MES allows for quick identification of the root cause and implementation of corrective actions. Real-time quality checks at critical points in the production process help detect and address defects promptly. Moreover, MES supports real-time decision-making by providing up-to-date data that allows production managers to respond swiftly to changes in demand or production conditions. This agility is essential in today's fast-paced manufacturing environment. In conclusion, Manufacturing Execution Systems are indispensable in the digital transformation of the manufacturing industry. By improving efficiency, product quality, and decision-making capabilities, MES equips manufacturers to succeed in the era of Industry 4.0. This paper highlights the benefits and challenges of MES implementation, offering valuable insights for manufacturers aiming to leverage MES in their digital transformation efforts

Entrepreneurial Design Thinking and Marketing Performance of Food and Beverage Firms in Port Harcourt, Rivers State

The study examined the relationship between entrepreneurial design thinking and marketing performance of food and beverage firms in Port Harcourt, Rivers State. This study adopted a correlational research design, with the population of the study comprising 25 food and beverage firms in Rivers State. Sequel to the population of the study, which is 25 food and beverage firms, the study adopted a census study with a focus on the managerial staff (production manager, quality control manager, marketing manager, and procurement manager). The questionnaire was distributed in a frame of four (4) copies per firm. A total of one hundred (100) copies of the questionnaire were distributed. The reliability of the instrument was determined using Cronbach's alpha test and it stood at 0.88, higher than the benchmark of 0.7. The data collected for this study were analyzed through descriptive and inferential statistics. The Spearman Rank-Order Correlation Technique was employed to test the various hypotheses formulated. The empirical results of this study confirmed that all the dimensions of entrepreneurial design thinking (creativity, and problem solving) were found to be significant predictors of marketing performance as measured by sales growth. Since all the dimensions of entrepreneurial design thinking are significant predictors of marketing performance, it is therefore concluded that entrepreneurial design thinking has the potential of improving marketing performance of food and beverage firms in Port Harcourt, Rivers State. Based on the findings and conclusions, the study recommends that: food and beverage firms should come up with creative designs, novel products and services that will catch the customer’s attention and improve marketing performance. Also, food and beverage firms, especially those facing different challenges, should adopt a problem solving approach to enable them to improve their sales growth.

Coding Competencies across Roles: Computer Programming Practices in News Media Organizations

ABSTRACT Computer programming, or what is commonly known as coding, is an emerging skill supporting the development of digital products in news media organizations. Through the application of practice theory—a hybrid approach that includes elements of field theory, actor–network theory and structuration theory—we examine the ways in which coding competencies are practiced within and across different roles, for different purposes. This exploratory study uses online interviews with 19 news product managers and leaders–or those who are tasked with the management, development, testing, and launching of digital news products–offering unique insight on coding practices across functions. Results highlight the usage, value and nuances of coding skills, providing insight into the activities, materiality, and reflexivity of computer programming as it emerges across roles in a news organization.

Multi-objective optimization of production system with staggered production

In the manufacturing industry, timely order fulfilment from diverse customers is important for operational profitability, given the constraints of limited production capacity. This paper proposes a novel queueing system for a production system, in which order processing times are categorised as regular or rush based on customer preference. The proposed queueing system employs variable service rates and service strategies in conjunction with staggered production to deal with heterogeneous arrivals. We compute the steady-state probability distribution and develop system performance indicators. The lower and upper bounds of the expected sojourn time for rush and regular orders are derived. Numerical results show that an appropriate switching policy can reduce rush orders by approximately 35% while regular orders increase by less than 10%, demonstrating significant improvement in order fulfilment management. We formulate a multi-objective optimisation problem and implement the NSGA-II algorithm to obtain Pareto optimal solutions. Finally, three regression models are proposed to determine the minimum cost and the associated optimal service rates, given the maximum acceptable value of the expected number of rush orders. The established models can explain at least 80% of the dataset of the optimal solutions and thus our numerical findings provide useful guidance for decision-makers and production managers.

Data Science Has Exploded Across the Industry Over the Past 75 Years

In 1957, the Journal of Petroleum Technology published an article titled “Application of Large Computers to Reservoir Engineering Problems.” That was the first reference in JPT to what became known as supercomputers. The high-speed computers used in the 1957 article were capable of performing 60 million operations in about 3½ hours. They were proposed to analyze the thorny problem of multiphase flow. “It presently appears that the large computer will be required to investigate multiphase flow and to predict the flow behavior of oil and gas reservoirs considering two- and three-space dimensions.” Now, 67 years later, supercomputer speeds are measured in trillions of operations per second, and investigating multiphase flow is just one of the many uses. This extreme growth in speed has given rise to artificial intelligence (AI) and machine learning (ML), which has found its way into almost every corner of the petroleum industry. The Society of Petroleum Engineers and JPT has kept up with the digital advances in the industry, holding countless conferences, symposia, and other meetings centered on the digital aspects of the industry and recently creating the Data Science and Engineering Analytics (DSEA) technical discipline. In 2019, SPE launched the Data Science and Digital Engineering in Upstream Oil and Gas online publication. Four leaders in the oil and gas digital space shared their views on the current state of data science in the industry and the future of the discipline. Sushma Bhan, currently on the board of directors for Ikon Science, is SPE’s Technical Director for DSEA. Before joining Ikon, she worked for Shell for 32 years, eventually rising to the role of chief data officer for subsurface and wells. “My journey in the oil and gas industry began in 1988, when I joined Shell’s Production Computing Assisted Operations team as a programmer analyst,” Bhan said. “During my time there, I gained valuable exposure to field production operations and developed a deep understanding of real‑time data.” Jim Crompton, who claims he is mostly retired, is an associate professor of petroleum engineering at the Colorado School of Mines, a faculty fellow at the school’s Payne Institute for Public Policy, and director of Reflections Data Consulting. “My academic career started out in exploration geophysics, so my first introduction to engineering analytics came from processing seismic data at Chevron Geophysical Company in Houston,” Crompton said. “I studied earthquake seismology in graduate school, but, when I graduated, I learned the oil and gas industry paid a lot more than the USGS did, so my career goals changed.” Shahab D. Mohaghegh is a professor at West Virginia University and the president of Intelligent Solutions. “Since I started working for the petroleum industry throughout the world using artificial intelligence in 2000, I was able to work with actual data (field measurements) that has been saved by all the companies,” Mohaghegh said. “Working with actual data using AI provided several technologies that have been incredibly fantastic compared to what we have done in the past.” Pushpesh Sharma, the chair of the DSEA Technical Section, holds a PhD degree in chemical engineering from the University of Houston and is senior product manager for Aspen Technology. “Till a few years ago, the focus was on proving the efficacy of data science/machine learning methods for energy use cases,” Sharma said. “However, in recent years, the concerns are around large-scale deployment, maintenance, and the black‑box nature of ML models. Because of that, I started seeing increased focus on the deployment, explainability, and trust of ML models.”

Concept of structure and principles of management of project and product IT management

In the current article, the issues of two related specialties such as project management and product management are raised. The problem of overlapping responsibilities of these two directions and their interaction in terms of project activity is highlighted. In addition, the modern market of projects from various web platforms and the demand for specialists in these fields of activity were analyzed. The main duties and requirements for these specialties are highlighted. As well as areas in which the use of project and product managers is more oriented. IT management is the most important function in an organization because it ensures that the organization's technologies and IT systems are aligned with its shared business goals and that they are used effectively and efficiently. It includes a wide range of tasks and responsibilities, such as: development and implementation of IT strategy and policies, management of IT resources, ensuring security and reliability of IT systems, supervision of development and maintenance of IT systems and applications, management of IT projects, managing relationships with suppliers and other external partners and managing IT budgets and costs. It includes such professions as: business analyst, project manager, product manager and others. As the role of technology continues to play a critical role in the success of organizations, management in IT is becoming an increasingly important function and IT universities know that effective management can help organizations increase their competitiveness, reduce costs and increase the efficiency of their operations. The reason for the transition from general management to PM is often reliability - instead of a relatively abstract perspective to predictable results.The main problem is the overlapping of the duties of specialties and levels of responsibilities in accordance with the roles on the project.

Implication of 4th Industrial Revolution on Tax Revenue Collection in Tanzania: A Case of Manufacturing Industries

The study assessed the implication of the Fourth Industrial Revolution (4IR) on tax revenue collection in Tanzania. Specifically, the study set out to establish the causality of investment in 4IR technologies, on manufacturing industries’ Value Added Tax (VAT); determine the influence of investment in 4IR technologies on manufacturing industries’ Corporate Tax and examine the effects of investment in 4IR technologies on manufacturing industries’ Employment Tax (PAYE). To achieve the research objectives, the study used a quantitative descriptive design to generate the required data. Moreover, the study used structured questionnaires for cross-sectional survey, administered with 225 production managers of manufacturing industries in Tanzania. It used probability sampling in the form of cluster sampling. Additionally, the study used the Least Square method to analyse the objectives of the study in addition to testing the hypotheses. The study found a positive and statistically significant causal effect between investment in modern technology, VAT, Corporate tax and Employment Tax (PAYE) of manufacturing firms in Tanzania, for the period under study. Implicitly, the emergence of 4IR does not necessarily occasion a decline in tax revenue collections (VAT, PAYE, and Corporate Tax) on the part of manufacturing firms in developing countries endowed with resources, like Tanzania, but rather complements and accelerates its growth. Thus, the study calls on the Tanzania Government to review and improve its Sustainable Industrial Development Policy of 2020 and the Integrated Industrial Development Strategy of 2025 to embrace, promote and support not only resource-based industries, but also technological based industries, to speed up industrialization within the country and ultimately improve and increase tax revenue collection. The government should also improve its investment policies and align them with technological changes brought by 4IR, to attract more foreign direct investments, importation and adaptation of 4IR technologies from developed countries that enhance productivity, turnover and tax revenue collections from the manufacturing sector.

Employment relationships and firm sustainability: does the strength of the HRM system play a role?

The aim of the present study, based on the framework of social exchange theory and sustainability, is twofold: first, to show how distinct employment relationships (ERs) can have different effects on sustainability outcomes; and second, to demonstrate how such an impact is mediated by the strength of the HRM system. The proposed model was tested using the multivariate method of partial least squares structural equation modelling (PLS-SEM) on a sample of 206 companies in the chemical and metallurgical industries. Data were collected via a questionnaire composed of measurement scales previously validated in the reference literature. Each company’s production and HR managers were asked to complete the questionnaire. Our results showed that mutual investment ERs can indeed produce better sustainability results, and organisations with quasi-spot contract (QSC) relationships are unable to achieve sustainable organisational outcomes. The results also confirmed that a strong HRM system reinforces the positive effects of mutual investment ERs. Moreover, a strong HRM mitigates the negative effects of QSCs. In essence, our paper shows that the strength of an HRM system is a major driver of sustainability. Therefore, when implementing working relationships, managers should foster shared perceptions of the firm’s values among its employees, thus clearly defining which behaviours are expected and rewarded.