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The paper aims to fill this gap by positing a framework that considers the service automation decision as a matter of knowledge management: a choice between human resident and codified knowledge assets.

The paper is a conceptual paper, grounded in the knowledge-based view.

The paper uses the information processing theory, which argues that the level of uncertainty in a process should dictate the type of knowledge deployed, as the contingency for the automation choice, and customer interaction uncertainty as the driver of that contingency. From these ideas, propositions are generated relating customer interaction uncertainty and service automation. Further implications for artificial intelligence (AI) are also explored.

The framework illuminates and informs the strategic choices regarding service automation, including the use of AI in professional services, a timely and highly important topic. It offers a valuable model for practitioners and contributes to the academic literature by pointing the way for future directions for scholarly research.

The purpose of this paper is to test a mediated model of the relationship between self-concept orientation (individualist and collectivist) and organizational identification (OrgID, Cooper and Thatcher, 2010), with proposed mediators including the need for organizational identification (nOID, Glynn, 1998) as well as self-presentation concerns of social adjustment (SA) and value expression (VE, Highhouse et al., 2007).

Data were collected from 509 participants in seven countries. Direct and mediation effects were tested using structural equation modeling (AMOS 25.0).

Individualist self-concept orientation was positively related to VE and collectivist self-concept orientation was positively related to nOID, VE and SA. VE mediated the relationship between both self-concept orientations and OrgID. In addition, nOID mediated the relationship for collectivist self-concept orientation.

This study identifies underlying psychological needs as mediators of the relationship of self-concept orientation to OrgID. Understanding these linkages enables employers to develop practices that resonate with the self-concept orientations and associated psychological needs of their employees, thereby enhancing OrgID.

This study provides a significant contribution to the OrgID literature by proposing and testing for relationships between self-concept orientations and OrgID as mediated by underlying psychological needs. The results provide support for the mediated model as well as many of Cooper and Thatcher’s (2010) theoretical propositions, with notable exceptions.

The purpose of this paper is to examine the relationship between individual- and country-level values and preferences for job/organizational attributes.

Survey data were collected from 475 full-time employees (average of nine years work experience, and three years in a managerial position) enrolled in part-time MBA programs in seven countries.

Preference for a harmonious workplace is positively related to horizontal collectivism, whereas preference for remuneration/advancement is positively related to vertical individualism. The authors also find a positive relationship between preference for meaningful work and horizontal individualism, and between preference for employer prestige and social adjustment (SA) needs.

Although the sample comprised experienced, full-time professionals, using graduate business students may limit generalizability. Overall, the results provide initial support for the utility of incorporating the multi-dimensional individualism and collectivism measure, as well as SA needs, when assessing the relationships between values and employee preferences.

For practitioners, the primary conclusion is that making assumptions about preferences based on nationality is risky. Findings may also prove useful for enhancing person-organization fit and the ability to attract and retain qualified workers.

This study extends research on workers’ preferences by incorporating a new set of values and sampling experienced workers in a range of cultural contexts.

In the rapidly changing business world, innovation plays a vital role for organizations to gain a competitive advantage. Various factors associated with technology management and innovations in organizations are diverse in the existing literature. Therefore, there is a need to bridge these gaps in the fitting proportions toward innovations within organizations. The primary objective of this study is to identify, explain and interpret the relationships between the identified technology-related factors that are important for innovations in organizations.

In this study, a modified total interpretive structural modeling (M-TISM) methodology was used to examine and analyze the various interactions between identified factors for innovations in organizations. However, the argumentation of the links is relatively weak in M-TISM. In order to compensate for this, M-TISM is additionally altered by an “Argumentation-based Modified TISM”. Hence, this research strengthens the modified TISM methodology by incorporating argumentation and total interpretation of the relationships between the identified factors.

A total of six major factors were identified using a literature review. Results suggest that workforce technical skills, technological infrastructure, technological alliances, technology transfer and top management support have an impact on innovation in organizations. Results also suggest that top management support and the technological infrastructure of an organization have a greater impact on innovation.

For policymakers and practitioners, this study provides a suggestive list of critical factors, which may help to develop policies or guidelines for improving innovation in organizations. Policymakers should focus on technological infrastructure and collaborations to enhance innovations and productions within the organizations. For academicians, this study provides a modified TISM model that shows the impact of technology-related factors on innovations. Future researchers could expand this study by adding a greater number of technological factors and validate this model in other industries.

This study fills a gap in the literature by interpreting the various relationships among the identified factors and innovations. The model has been validated through a panel of seven experts from the Indian automotive industry of multiple organizations. This study is useful in the automobile industry as it determines what and how technology-related factors affect innovations, process improvement and R&D production for organizations.

There is a renewed interest among economists and policymakers in striking a perfect balance between the market, state, and the community for equitable and sustained development. Inclusive development is the need of the hour and healthcare cooperatives provide the perfect means to deliver that. The cooperatives can and should have an important role to play in this scenario. However, there is an important need to provide an enabling environment for the genesis and growth of Healthcare Cooperatives in India and to learn from the best models and practices across the world. The chapter discusses the effectiveness of healthcare cooperatives.

Wavelets are being increasingly used for damage diagnostics. The purpose of this paper is to present an algorithm that uses the wavelet transform for detecting mixed-mode, also known as combined mode, cracks in large truss structures.

The mixed-mode crack is modeled by superposing two damage modes, and this model is combined with a finite element model of the truss. The natural modes of the truss are processed through the wavelet transform and then used to determine the damage location. The influence of multiple parameters such as truss geometry, crack geometry, number of truss members, orientation of truss members, etc. is investigated as part of the study.

The proposed damage detection algorithm is found to be successful in detecting single mode as well as mixed-mode cracks even in the presence of significant end effects, and even when a relatively coarse sampling of natural modes is used. Results from multiple simulations that involve three commonly used truss structures are presented. A correlation between damage severity and the magnitude of wavelet coefficients is observed.

The proposed algorithm is found to be successful in accurately detecting damage, but direct determination of damage severity is found to be challenging.

We provide an overview of how artificial intelligence is transforming the identification, structuring, and prioritization of customer needs – known as the voice of the customer (VOC). First, we summarize how the VOC helps firms gain insights on using user-generated data. Second, we discuss the types of user-generated data and the challenges associated with analyzing each type of data. Third, we describe common methods, matched to the firms' goals and the structure of the data, that are used to analyze the VOC. Fourth, and most importantly, we map the methods to relevant applications, providing guidance to select the appropriate method to address the desired research questions.

The growth of social media and the sharing economy is generating abundant unstructured image and video data. Computer vision techniques can derive rich insights from unstructured data and can inform recommendations for increasing profits and consumer utility – if only the model outputs are interpretable enough to earn the trust of consumers and buy-in from companies. To build a foundation for understanding the importance of model interpretation in image analytics, the first section of this article reviews the existing work along three dimensions: the data type (image data vs. video data), model structure (feature-level vs. pixel-level), and primary application (to increase company profits vs. to maximize consumer utility). The second section discusses how the “black box” of pixel-level models leads to legal and ethical problems, but interpretability can be improved with eXplainable Artificial Intelligence (XAI) methods. We classify and review XAI methods based on transparency, the scope of interpretability (global vs. local), and model specificity (model-specific vs. model-agnostic); in marketing research, transparent, local, and model-agnostic methods are most common. The third section proposes three promising future research directions related to model interpretability: the economic value of augmented reality in 3D product tracking and visualization, field experiments to compare human judgments with the outputs of machine vision systems, and XAI methods to test strategies for mitigating algorithmic bias.