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(Digital) servitization, referring to service-driven strategies and their increasing implementation in manufacturing, is one of the most rapidly growing areas in industrial service research. However, the cultural change involved in successful servitization is a phenomenon that is widely observed but poorly understood. This research aims to clarify the processes of social construction as manufacturers change their organizational culture to transform into industrial service providers.

This research takes a systematic approach to integrate disparate literature on servitization into a cohesive framework for cultural change, which is purposefully augmented by rationale culled from organizational learning and sensemaking literature.

The organizational learning framework for cultural change in servitization introduces a dynamic perspective on servitizing organizations by explaining social processes between organizational and member-level cultural properties. It identifies three major cultural orientations toward service, digital and learning that govern successful servitization.

This research contributes to the servitization literature by presenting a new approach to reframe and explore cultural change processes across multiple levels, thus providing a concrete starting point for further research in this area.

Social media marketers are keen to understand how viewers perceive their brands on a platform and how the learning experiences from content can impact their attitudes toward a brand. This study aims to focus on examining the effect of firm-generated content (FGC) on X (formerly known as Twitter), using Kolb’s experiential learning theory to analyze the viewers’ learning process. In addition, the study investigates how the length of time a viewer follows a brand and the type of brand can influence their attitudes toward it.

This study involved three qualitative studies on X to investigate how content learning affects consumer attitudes toward two brands, namely, Nike and Subway. The study also examined the impact of the duration of following the brands, with participants following the brands for 4, 8 and 12 weeks, respectively, to assess changes in their attitudes.

The results demonstrate that content learning significantly impacts consumer attitudes. By following brands and engaging with their FGC over time, viewers can transition from being occasional or intermittent followers to becoming devoted brand enthusiasts. Through the four-stage experiential learning process, followers undergo cognitive, emotional and behavioral transformations that collectively shape their brand attitudes. The impact of content learning varies according to the brand type, and the duration of following has a positive effect on brand attitudes.

The study’s findings have significant marketing implications for social media marketers, suggesting that they should restructure their social media platforms as learning platforms to effectively engage followers. Companies should adjust their content marketing strategies from a learner’s perspective, providing followers with content that resonates with them, enhances their learning outcomes and helps shift their beliefs and brand attitudes, ultimately converting them into loyal consumers.

To the best of the author’s knowledge, this qualitative research is the first of its kind to apply experiential learning theories to investigate how users learn from FGC by following brands on social media and how this learning ultimately changes their brand attitude. The study provides a unique perspective on social media marketing, enriching the understanding of content marketing and consumer experiences on social media platforms.

The role of Technology Transfer Units, examined in this study, was found to be vital to expedite the process of disseminating new varieties and their production technology.

A total of 1,000 households were surveyed in the sampled states. A probit model was used to analyse.

Age, education, land holding, tractor use and number of working family members in agriculture were found to significantly affecting adoption of the new seed varieties. Technology transfer through licensing has impacted the adoption of new seed varieties positively by highlighting Punjab possessing the highest adoption and western Uttar Pradesh was majorly adopting the old variety.

The authors believed in farmers’ memory to recall the varietal information of wheat.

The study recommended various incentives to attract the seed industry in UP to minimize the economic loss potentially suffered by them.

Quality seeds are germane to increase the productivity of crops, and it is paramount to disburse the seed varieties to the end users in an efficient way to achieve the overall objective of productivity enhancement.

In this context, a study was conducted in three states of India, namely, Punjab, Haryana and Uttar Pradesh (UP) to find out the adoption rate of newly developed varieties of wheat, HD 3086 after three years (2014–2015) of its commercialization by IARI as well as HD 2967, which was released in 2011.

This study aims to investigate the role of three customer equity drivers on customer repurchase intent in business-to-business (B2B) markets. It also explores the interconnected nature of equity drivers, specifically, the effects of brand equity and value equity on relationship equity. Further, it investigates how perceived switching costs moderates the interrelationships between customer equity drivers. The authors explore the interrelationships between the customer equity drivers in a B2B context involving commodity products in a developing market.

Data collection was done from a pool of 184 institutional customers of a lubricant brand in a developing market. The sample had representations of buyer organizations across sectors, namely, automobile, cement, metal, fertilizer, railway, defence and mining, etc. The final data were subjected to partial least squares-based structural equation modeling to test the hypothesized model.

The study found a direct effect of brand equity, and value equity on relationship equity and an indirect effect on repurchase intent, namely, relationship equity. Perceived switching cost was found to moderate the interaction between brand equity and relationship equity as well as between value equity and relationship equity. The direct effect of relationship equity on repurchase intent was also significant.

The study implies that B2B firms should ground their marketing program on these customer equity drivers, especially when dealing with commodity products. The absence of any of these drivers would be detrimental in customer retention. The study also establishes the relevance of switching cost(s) and its impact on the underlying dynamics between the different equity drivers in the context of commodity products. The customer equity drivers along with switching costs, if managed well, may become switching barriers for customers and eventually would ensure recurring revenue through repeat purchases.

To the best of the authors’ knowledge, this is one of the first studies that focuses on the disaggregated effect of customer equity on customer outcomes in the B2B context. Furthermore, this study investigates how perceived switching costs moderates the interrelationships between customer equity drivers in the industrial sales context in an emerging market.

Existing deterministic models that predict the capacity of corroded reinforced concrete (RC) beams have limited applicability because they were based on accelerated tests that induce general corrosion. This research gap was addressed by performing a combined numerical and statistical analysis on RC beams, subjected to natural corrosion, to achieve a much better forecast.

Data of 42 naturally corroded beams were collected from the literature and analyzed numerically. Four constitutive models and their combinations were considered: the elastic-semi-plastic and elastic-perfectly-plastic models for steel, and two tensile models for concrete with and without the post-cracking stresses. Meanwhile, Popovics’ model was used to describe the behavior of concrete under compression. Corrosion coefficients were developed as functions of corrosion degree and beam parameters through linear regression analysis to fit the theoretical moment capacities with test data. The performance of the coefficients derived from different combinations of constitutive laws was then compared and validated.

The results showed that the highest accuracy (R² = 0.90) was achieved when the tensile response of concrete was modeled without the residual stresses after cracking and the steel was analyzed as an elastic-perfectly-plastic material. The proposed procedure and regression model also showed reasonable agreement with experimental data, even performing better than the current models derived from accelerated tests and traditional procedures.

This study presents a simple but reliable approach for quantifying the capacity of RC beams under more realistic conditions than previously reported. This method is simple and requires only a few variables to be employed. Civil engineers can use it to obtain a quick and rough estimate of the structural condition of corroding RC beams.

Taking into consideration the current human need for agricultural produce such as rice that requires water for growth, the optimal consumption of this valuable liquid is important. Unfortunately, the traditional use of water by humans for agricultural purposes contradicts the concept of optimal consumption. Therefore, designing and implementing a mechanized irrigation system is of the highest importance. This system includes hardware equipment such as liquid altimeter sensors, valves and pumps which have a failure phenomenon as an integral part, causing faults in the system. Naturally, these faults occur at probable time intervals, and the probability function with exponential distribution is used to simulate this interval. Thus, before the implementation of such high-cost systems, its evaluation is essential during the design phase.

The proposed approach included two main steps: offline and online. The offline phase included the simulation of the studied system (i.e. the irrigation system of paddy fields) and the acquisition of a data set for training machine learning algorithms such as decision trees to detect, locate (classification) and evaluate faults. In the online phase, C5.0 decision trees trained in the offline phase were used on a stream of data generated by the system.

The proposed approach is a comprehensive online component-oriented method, which is a combination of supervised machine learning methods to investigate system faults. Each of these methods is considered a component determined by the dimensions and complexity of the case study (to discover, classify and evaluate fault tolerance). These components are placed together in the form of a process framework so that the appropriate method for each component is obtained based on comparison with other machine learning methods. As a result, depending on the conditions under study, the most efficient method is selected in the components. Before the system implementation phase, its reliability is checked by evaluating the predicted faults (in the system design phase). Therefore, this approach avoids the construction of a high-risk system. Compared to existing methods, the proposed approach is more comprehensive and has greater flexibility.

By expanding the dimensions of the problem, the model verification space grows exponentially using automata.

Unlike the existing methods that only examine one or two aspects of fault analysis such as fault detection, classification and fault-tolerance evaluation, this paper proposes a comprehensive process-oriented approach that investigates all three aspects of fault analysis concurrently.

Digital labor platforms (DLPs) are transforming the nature of the work for an increasing number of workers, especially through extensively employing automated algorithms for performing managerial functions. In this novel working setting – characterized by algorithmic governance, and automatic matching, rewarding and punishing mechanisms – gig-workers play an essential role in providing on-demand services for final customers. Since gig-workers’ continued participation is crucial for sustainable service delivery in platform contexts, this study aims to identify and examine the antecedents of their working outcomes, including burnout and engagement.

We suggested a theoretical framework, grounded in the job demands-resources heuristic model to investigate how the interplay of job demands and resources, resulting from working in DLPs, explains gig-workers’ engagement and burnout. We further empirically tested the proposed model to understand how DLPs' working conditions, in particular their algorithmic management, impact gig-working outcomes.

Our findings indicate that job resources – algorithmic compensation, work autonomy and information sharing– have significant positive effects on gig-workers’ engagement. Furthermore, our results demonstrate that job insecurity, unsupportive algorithmic interaction (UAI) and algorithmic injustice significantly contribute to gig-workers’ burnout. Notably, we found that job resources substantially, but differently, moderate the relationship between job demands and gig-workers’ burnout.

This study contributes a theoretically accurate and empirically grounded understanding of two clusters of conditions – job demands and resources– as a result of algorithmic management practice in DLPs. We developed nuanced insights into how such conditions are evaluated by gig-workers and shape their engagement or burnout in DLP emerging work settings. We further uncovered that in gig-working context, resources do not similarly buffer against the negative effects of job demands.

The results indicate that land prices exert pressure on retail performance (RP) and that the enhancement of digital means has a positive effect on RP. Additionally, digital instruments (DI) play a significant moderating role in the relationship between land prices and RP.

This paper empirically examines the impact of land prices on RP using panel data from 239 Chinese cities between 2011 and 2022.

The use of lagged land prices as instrumental variables effectively alleviates endogeneity issues. Both two-stage least squares (2SLS) and generalized method of moments (GMM) regression results suggest that higher land prices are associated with improved RP. Further analysis reveals that the increase in land prices leads to scale effects, structural effects and technological effects, contributing to the enhancement of RP. The impact of land prices on RP becomes more pronounced in larger cities and economically developed regions experience the pressure from land prices earlier.

The findings of this study have practical implications for discussions on retail industry development, site selection for retail businesses and the establishment of sustainable mechanisms for expanding domestic demand.

In recent years, there has been growing interest in the use of stainless steel (SS) in reinforced concrete (RC) structures due to its distinctive corrosion resistance and excellent mechanical properties. To ensure effective synergy between SS and concrete, it is necessary to develop a time-saving approach to accurately determine the ultimate bond strength τ_u between the two materials in RC structures.

Three robust machine learning (ML) models, including support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGBoost), are employed to predict τ_u between ribbed SS and concrete. Model hyperparameters are fine-tuned using Bayesian optimization (BO) with 10-fold cross-validation. The interpretable techniques including partial dependence plots (PDPs) and Shapley additive explanation (SHAP) are also utilized to figure out the relationship between input features and output for the best model.

Among the three ML models, BO-XGBoost exhibits the strongest generalization and highest accuracy in estimating τ_u. According to SHAP value-based feature importance, compressive strength of concrete f_c emerges as the most prominent feature, followed by concrete cover thickness c, while the embedment length to diameter ratio l/d, and the diameter d for SS are deemed less important features. Properly increasing c and f_c can enhance τ_u between ribbed SS and concrete.

An online graphical user interface (GUI) has been developed based on BO-XGBoost to estimate τ_u. This tool can be utilized in structural design of RC structures with ribbed SS as reinforcement.

The purpose of this paper is to present a wall-climbing robot platform for heavy-load with negative pressure adsorption, which could be equipped with a six-degree of freedom (DOF) collaborative robot (Cobot) and detection device for inspecting the overwater part of concrete bridge towers/piers for large bridges.

By analyzing the shortcomings of existing wall-climbing robots in detecting concrete structures, a wall-climbing mobile manipulator (WCMM), which could be compatible with various detection devices, is proposed for detecting the concrete towers/piers of the Hong Kong-Zhuhai-Macao Bridge. The factors affecting the load capacity are obtained by analyzing the antislip and antioverturning conditions of the wall-climbing robot platform on the wall surface. Design strategies for each part of the structure of the wall-climbing robot are provided based on the influencing factors. By deriving the equivalent adsorption force equation, analyzed the influencing factors of equivalent adsorption force and provided schemes that could enhance the load capacity of the wall-climbing robot.

The adsorption test verifies the maximum negative pressure that the fan module could provide to the adsorption chamber. The load capacity test verifies it is feasible to achieve the expected bearing requirements of the wall-climbing robot. The motion tests prove that the developed climbing robot vehicle could move freely on the surface of the concrete structure after being equipped with a six-DOF Cobot.

The development of the heavy-load wall-climbing robot enables the Cobot to be installed and equipped on the wall-climbing robot, forming the WCMM, making them compatible with carrying various devices and expanding the application of the wall-climbing robot.

A heavy-load wall-climbing robot using negative pressure adsorption has been developed. The wall-climbing robot platform could carry a six-DOF Cobot, making it compatible with various detection devices for the inspection of concrete structures of large bridges. The WCMM could be expanded to detect the concretes with similar structures. The research and development process of the heavy-load wall-climbing robot could inspire the design of other negative-pressure wall-climbing robots.